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One of the models that could be used to understand the adoption of vaccine uptake is the Health Belief Model (HBM). The aim of this study is to assess the role of HBM constructs and Perceived Health Status (PHS) on the vaccination status of individuals and to understand the role of socio-demographic variables on HBM scoring. A comparative cross-sectional telephone survey was conducted among 1325 vaccinated (60.0%) and non-vaccinated (40.0%) individuals aged 40 years and above in July 2021 in Punjab province, Pakistan. A higher level of education was the strongest predictor of positive HBM. All constructs of HBM, PHS and cues-to-action were significant predictors of COVID-19 vaccination uptake, with perceived benefits as the strongest predictor. In order to expand the vaccination coverage, double-pronged interventions utilizing both information and communication technology and human resources should be designed that address each barrier perceived by individuals and understandably communicate the benefits of COVID-19 vaccination to the broader population.

An effective feature representation can boost recognition tasks in the sketch domain. Due to an abstract and diverse structure of the sketch relatively with a natural image, it is complex to generate a discriminative features representation for sketch recognition. Accordingly, this article presents a novel scheme for sketch recognition. It generates a discriminative features representation as a result of integrating asymmetry essential information from deep features. This information is kept as an original feature‐vector space for making a final decision. Specifically, five different well‐known pre‐trained deep convolutional neural networks (DCNNs), namely, AlexNet, VGGNet‐19, Inception V3, Xception, and InceptionResNetV2 are fine‐tuned and utilised for feature extraction. First, the high‐level deep layers of the networks were used to get multi‐features hierarchy from sketch images. Second, an entropy‐based neighbourhood component analysis was employed to optimise the fusion of features in order of rank from multiple different layers of various deep networks. Finally, the ranked features vector space was fed into the support vector machine (SVM) classifier for sketch classification outcomes. The performance of the proposed scheme is evaluated on two different sketch datasets such as TU‐Berlin and Sketchy for classification and retrieval tasks. Experimental outcomes demonstrate that the proposed scheme brings substantial improvement over human recognition accuracy and other state‐of‐the‐art algorithms.

The concept of software birthmark is developed for the detection of theft and piracy in software applications. The originality of software can be evaluated by comparing software programs on the basis of their birthmarks. A number of birthmark designs have been proposed which are used to specify birthmark for source code and executable code related to particular programming languages. This study presents a systematic literature review on available software birthmark designs and related techniques for comparing birthmarks in order to identify pirated software. This research is focused on identifying different applications of software birthmark, especially the estimation of software birthmark to identify the extent of piracy performed in a software. The objective is to gain insight into complex details of software birthmark by accumulating and analyzing the knowledge provided in the literature in order to facilitate further research in software birthmark and its applications. The study is conducted by following the systematic literature review protocol. The data are collected from primary studies published from 1992 to April 2018 in specified journals and conference/workshop proceedings. A total of 143 primary studies are selected, based on predefined exclusion, inclusion, and quality criteria. The research identifies 22 software birthmark techniques frequently used and discussed by researchers and industry. The study also identifies a number of important applications of software birthmarks. These applications define the use of software birthmark in software theft and plagiarism detection, intellectual software asset management, detecting binary theft, malware detection, detecting the theft of natural language, and semantics-based repackaging detection for mobile apps. The results show that despite the large-scale research and development of different birthmark techniques, there is a lack of organized knowledge which is needed to facilitate the usage of software birthmark for critical applications like clone detection and malware detection. Furthermore, it is seen that the area of software birthmark estimation is not well researched which needs to be explored further. The study recommends that the area of software birthmark needs to be explored for developing a reliable and authentic mechanism which can accurately and easily detect software theft and ultimately prevent the piracy of software.

In today's digital world the information surges with the widespread use of the internet and global communication systems. Healthcare systems are also facing digital transformations with the enhancement in the utilization of healthcare information systems, electronic records in medical, wearable, smart devices, handheld devices, and so on. A bulk of data is produced from these digital transformations. The recent increase in medical big data and the development of computational techniques in the field of cardiology enables researchers and practitioners to extract and visualize medical big data in a new spectrum. The role of medical big data in cardiology becomes a challenging task. Early decision making in cardiac healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Therefore, to facilitate this process a detailed report of the existing literature will be feasible to help the doctors and practitioners in decision making for the purpose of identifying and treating cardiac diseases. This detailed study will summarize results from the existing literature on big data in the field cardiac disease. This research uses the systematic literature protocol as presented by Kitchenham et al. The data was collected from the published materials from 2008 to 2018 as conference or journal publications, books, magazines and other online sources. 190 papers were included relying on the defined inclusion, exclusion, and checking the quality criteria. The current study helped to identify medical big data features, the application of medical big data, and the analytics of the big data in cardiology. The results of the proposed research shows that several studies exist that are associated to medical big data specifically to cardiology. This research summarizes and organizes the existing literature based on the defined keywords and research questions. The analysis will help doctors to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients with heart related diseases.

The digital transformations and use of healthcare information system, electronic medical records, wearable technology, and smart devices are increasing with the passage of time. A variety of sources of big data in healthcare are available, such as biometric data, registration data, electronic health record, medical imaging, patient reported data, biomarker data, clinical data, and administrative data. Visualization of data is a key tool for producing images, diagrams, or animations to convey messages from the viewed insight. The role of cardiology in healthcare is obvious for living and life. The function of heart is the control of blood supply to the entire parts of the body. Recent speedy growth in healthcare and the development of computation in the field of cardiology enable researchers and practitioners to mine and visualize new insights from patient data. The role of visualization is to capture the important information from the data and to visualize it for the easiness of doctors and practitioners. To help the doctors and practitioners, the proposed study presents a detailed report of the existing literature on visualization of data in the field of cardiology. This report will support the doctors and practitioners in decision-making process and to make it easier. This detailed study will eventually summarize the results of the existing literature published related to visualization of data in the cardiology. This research uses the systematic literature protocol and the data was collected from the studies published during the year 2009 to 2018 (10 years). The proposed study selected 53 primary studies from different repositories according to the defined exclusion, inclusion, and quality criteria. The proposed study focused mainly on the research work been done on visualization of big data in the field of cardiology, presented a summary of the techniques used for visualization of data in cardiology, and highlight the benefits of visualizations in cardiology. The current research summarizes and organizes the available literature in the form of published materials related to big data visualization in cardiology. The proposed research will help the researchers to view the available research studies on the subject of medical big data in cardiology and then can ultimately be used as evidence in future research. The results of the proposed research show that there is an increase in articles published yearly wise and several studies exist related to medical big data in cardiology. The derivations from the studies are presented in the paper.

With the wide spread use of agile methodologies in software development industry proper training of software engineers to cope with the diversity of agile development process has become more crucial than before. One solution for development organizations is to train and prepare their teams for agile acceptance. Although it is the most common scenario for organizations who wish for agile transformation, but this process is slow and lengthy as teams with zero prior agile exposure feel the burden of agile practices and sometimes become defiant. Another solution is to groom perspective software engineers with agile practices in universities and other training institutions. This solution is less expensive, widespread, and long lasting. The following paper analyzes the proceedings and retrospective of a graduate level course of agile software development delivered at a public sector university. The course is based on Agile skill development using Extreme Programming practices in project based training environment. The course was designed and taught with a research goal to establish applicable and realistic guidelines for organizing and delivering an agile methodology training. Such guidelines are of fundamental importance; as agile methodologies require a profound skill development so that perspective software engineers can accept the technological as well as social transformation that is an integral part of an agile development setup.

BACKGROUND The effect of postmenopausal hormone therapy (HT) on cardiovascular disease (CVD) risk remains controversial. OBJECTIVE AND RATIONALE We aimed to systematically review the evidence regarding the role of dose, route of hormone administration, timing of initiation and duration of HT on cardiovascular risk among postmenopausal women. SEARCH METHODS The electronic databases Medline Ovid, Web of Science and Cochrane Central were systematically searched to identify studies published before 30 January 2018. Reference lists, using Elsevier's Scopus, of the included studies were searched for further identification of relevant studies. Clinical trials and observational studies that assessed clinical and subclinical cardiovascular outcomes in relation to dose, route of administration, duration of use, or timing of HT initiation among postmenopausal women were included. Data were extracted by independent reviewers using a pre-designed data collection form. The Cochrane Collaboration's tool and the Newcastle-Ottawa Scale were used by two independent investigators to assess the risk of bias in RCTs and in prospective observational studies, respectively. OUTCOMES In total, 33 unique studies (6 trials and 27 prospective observational studies) were identified, including a total of 2 588 327 women. The synthesis of the existing knowledge on this topic was challenging due to inconsistent findings between some studies, caused by substantial diversity in scientific rigor and quality across the available literature. Overall, the evidence did not support the concerns that oral or transdermal HT increases heart disease risk. Contrary, observational data showed that a beneficial cardioprotective effect can be observed even with use of low doses of oral HT (effect of 0.3 mg/day of oral conjugated equine estrogen was similar to that seen with the standard dose of 0.625 mg/day), but clinical trials to support a cardioprotective benefit of HT in primary prevention have not been identified. Furthermore, the current data suggested that oral and transdermal HT, in dose-dependent manner and irrespective of HT formulation, may increase thromboembolic risk, as well as risk of stroke. However, transdermal estrogen with 10 years since the menopause onset (>60 years old), due to greater absolute risks of coronary heart disease, stroke and venous thromboembolism, HT should be used for the shortest time possible and in lowest possible dose and preferably transdermal administration should be recommended. However, an individualized treatment approach including baseline CVD risk assessment should be applied when prescribing HT. The majority of studies included in the current review are from North American and European populations, which might limit the generalizability of the findings of this review to the other populations. Finally, the quality of evidence included in this review was generally low or moderate, highlighting a need for more rigorous research to help us better understand HT and cardiovascular health.

Flexibility and change adoption are key attributes for service-oriented architecture (SOA) and agile software development processes. Although the notion of agility is quite visible on both sides, still the integration of the two diverse concepts (architectural framework and development process) should be well thought of before employing them for a software development project. For this purpose, this study is designed to analyze the two diverse software architectural framework and development approaches, that is, SOA and Scrum process model, respectively, and their integrated environment in software project development setup perspective for Industrial Internet of Things (IIoT). This study also analyzes commonalities among Scrum process model and SOA architectural framework to identify compatibility between Scrum and SOA so that the Scrum process can be constructively used for SOA based projects. This study also examines the proper design and setup of Scrum process suitable for large-scale SOA based projects. For this purpose, an SOA based research and development project is selected as a case study using Scrum as the software development process. The project development and deployment perspective include eight core modules that constitute the overall project framework.

To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore, many countries are currently considering to lift the NPIs—increasing the likelihood of disease resurgence. In this regard, dynamic NPIs, with intervals of relaxed social distancing, may provide a more suitable alternative. However, the ideal frequency and duration of intermittent NPIs, and the ideal “break” when interventions can be temporarily relaxed, remain uncertain, especially in resource-poor settings. We employed a multivariate prediction model, based on up-to-date transmission and clinical parameters, to simulate outbreak trajectories in 16 countries, from diverse regions and economic categories. In each country, we then modelled the impacts on intensive care unit (ICU) admissions and deaths over an 18-month period for following scenarios: (1) no intervention, (2) consecutive cycles of mitigation measures followed by a relaxation period, and (3) consecutive cycles of suppression measures followed by a relaxation period. We defined these dynamic interventions based on reduction of the mean reproduction number during each cycle, assuming a basic reproduction number (R0) of 2.2 for no intervention, and subsequent effective reproduction numbers (R) of 0.8 and 0.5 for illustrative dynamic mitigation and suppression interventions, respectively. We found that dynamic cycles of 50-day mitigation followed by a 30-day relaxation reduced transmission, however, were unsuccessful in lowering ICU hospitalizations below manageable limits. By contrast, dynamic cycles of 50-day suppression followed by a 30-day relaxation kept the ICU demands below the national capacities. Additionally, we estimated that a significant number of new infections and deaths, especially in resource-poor countries, would be averted if these dynamic suppression measures were kept in place over an 18-month period. This multi-country analysis demonstrates that intermittent reductions of R below 1 through a potential combination of suppression interventions and relaxation can be an effective strategy for COVID-19 pandemic control. Such a “schedule” of social distancing might be particularly relevant to low-income countries, where a single, prolonged suppression intervention is unsustainable. Efficient implementation of dynamic suppression interventions, therefore, confers a pragmatic option to: (1) prevent critical care overload and deaths, (2) gain time to develop preventive and clinical measures, and (3) reduce economic hardship globally. Electronic supplementary material The online version of this article (10.1007/s10654-020-00649-w) contains supplementary material, which is available to authorized users.

Web log files are the greatest source of knowledge now days, which keeps all the information about users interaction to web. This interaction provides us the usage patterns of the user by using web usage mining. These files contain all the information about visitors of the web which is used as input for analysis. These files are converted to required formats after completing the preprocessing so Web Usage Mining (WUM) techniques can apply on these logs. Web usage mining gives us the details of user patterns. In this study we are going discover different behaviors patterns from the web proxy server log file of an educational organization with web usage mining technique. Results are based on the interest of users towards educational websites.

Hierarchical clustering groups similar entities on the basis of some similarity (or distance) association and results in a tree like structure, called dendrogram. Dendrograms represent clusters in a nested manner, where at each step an entity makes a new cluster or merges into an existing cluster. Hierarchical clustering has many applications, therefore researchers have made efforts to come up with improved hierarchical clustering approaches. An approach that has received attention is based on combining clustering results, since different hierarchical clustering algorithms produce different dendrograms and their combination has produced more promising results as compared to individual hierarchical clustering. This paper proposes the hierarchical clustering combination (HCC) approach which uses the different types of structural features present in the dendrogram. Firstly, the dendrograms are represented in a 4+N (4 is the extracted number of features and can be extended to N number) dimensional euclidean space (4+NDES) which results in vector matrices. 4+NDES is the structural representation of the dendrogram which contains not only the relative features but also the absolute features of the entities in the dendrogram. Then the vector matrices are aggregated and the distance is calculated between each two vector using the Euclidean distance measure. The final hierarchy is obtained using a recovery tool like individual hierarchical clustering. 4+NDES-HCC utilizes the structural contents of the dendrogram and has the flexibility to handle an increasing number of features. The proposed approach is tested for software clustering which plays an important role in maintenance of software systems. The experimental results of the proposed approach and comparative analysis with existing approaches reveal the effectiveness of the HCC for software clustering.

Software birthmark is an important property of software that is successfully used to detect piracy and theft of software. Estimation of a birthmark provides critical information about the extent of piracy performed in a software. This information can then be used to decide over many important issues related with software theft and piracy, including legal and ethical considerations. Research shows that a software birthmark based on multiple software features provides a powerful and unique identity to software, hence more useful in theft detection. The estimation of this features based birthmark may provide even close detection and estimation of software piracy. This estimation process provides an objective measure to detect software theft and piracy efficiently and accurately. The research uses the concept of fuzzy logic for estimation, which has already proved its success in estimation of other birthmarks. The technique is tested for features based birthmark through a case study and the results support validity of the process. The details of the case study elaborate upon techniques and details of implementing software birthmark estimation process and show that the method is effective in terms of efficiency and accuracy for the estimation of features based software birthmark.

Software theft or piracy is a rapidly growing problem which includes copying, modifying, and misusing proprietary software opposed to the license agreement. Software birthmark is a property of software that has been used for the detection of software theft successfully. Two separate pieces of software can be compared to identify the similarity in code by using their birthmarks. Comparison of the birthmarks of the softwares in question tells us whether software is a duplicate copy of another software or not. Similarity in birthmarks of two computer programs indicates that they are same. Until now, classification of software as pirated or not pirated still becomes a challenging task. Therefore, in this paper we present the use of rough set theory, which is a mathematical approach to deal with vagueness and uncertainty in classification problems. The technique is validated through an empirical case study. Some experiments show that the techniques are successful in assessing the specified properties of the birthmark and thus providing a valid classification decision.

[EN] Piracy of software is an increasing problem of modern day software industry. Piracy of software is the unlawful use of software or part of it without proper permission as described in license agreement. Software piracy is a serious crime but not taken seriously by most people. Preventing software piracy is very important for the growing software industry. Efforts are being made to prevent and detect software piracy. Several techniques have been developed most important of which is software birthmark. The birthmark of a software is the intrinsic properties of software. A recent research shows that a features based software birthmark can be used as a strong mechanism to detect piracy of a software and how much piracy performed has been performed on it. An objective measure is needed to overcome this problem and to compare features based birthmark of a software which efficiently and precisely detect piracy in reproduction of software. The proposed study presents Haar wavelet collocation method for software features (birthmark) to detect piracy. The proposed method gives an exclusive solution for the features based birthmark of software and is then further used for comparisons of birthmark. The results of the proposed study show the effectiveness in terms of accuracy and efficiency to compare the features based software. (C) 2019 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.