Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms.

Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.

Design-Time Reliability Prediction Model for Component-Based Software Systems.

Software reliability is prioritised as the most critical quality attribute. Reliability prediction models participate in the prevention of software failures which can cause vital events and disastrous consequences in safety-critical applications or even in businesses. Predicting reliability during design allows software developers to avoid potential design problems, which can otherwise result in reconstructing an entire system when discovered at later stages of the software development life-cycle. Several reliability models have been built to predict reliability during software development. However, several issues still exist in these models. Current models suffer from a scalability issue referred to as the modeling of large systems. The scalability solutions usually come at a high computational cost, requiring solutions. Secondly, consideration of the nature of concurrent applications in reliability prediction is another issue. We propose a reliability prediction model that enhances scalability by introducing a system-level scenario synthesis mechanism that mitigates complexity. Additionally, the proposed model supports modeling of the nature of concurrent applications through adaption of formal statistical distribution toward scenario combination. The proposed model was evaluated using sensors-based case studies. The experimental results show the effectiveness of the proposed model from the view of computational cost reduction compared to similar models. This reduction is the main parameter for scalability enhancement. In addition, the presented work can enable system developers to know up to which load their system will be reliable via observation of the reliability value in several running scenarios.

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing.

The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization's resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm's performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

The impact of intervention strategies and prevention measurements for controlling COVID-19 outbreak in Saudi Arabia.

On 11 March 2020, the World Health Organization announced the novel coronavirus COVID-19 outbreak as a pandemic due to the rapid growth in the number of cases worldwide. The ability of countries to contain and mitigate interventions is crucial in controlling the exponential spread of the novel virus. Several social distancing and control measurements have been applied in Saudi Arabia to mitigate COVID-19 epidemic such as quarantine, schools closure, suspending travels, reducing crowds, people movement restrictions, self-isolation and contacts tracing. This research aims to study the country interventions in Saudi Arabia and their impact on decreasing the spread of COVID-19. This paper examined different control measurements scenarios produced by a modified SEIR mathematical model with an emphasis on testing capacity expansion and number of critical cases. The modified SEIR mathematical model is solved numerically using Rung-Kutta analysis method for solving the modified SEIR system of ordinary differential equations. The simulation results revealed that the interventions are vital to flatten the virus spread curve. Early implementation of country interventions can delay the peak and decrease the population fatality rate.

Developing a risk prediction model for breast cancer: a Statistical Utility to Determine Affinity of Neoplasm (SUDAN-CA Breast).

BACKGROUND: Breast cancer risk prediction models are widely used in clinical settings. Although most of the well-known models were designed based on data collected from western population, yet they have been utilized for surveillance purposes in many limited-resource countries. Given the genetic variations in risk factors that exist between different races, we therefore aimed to develop and validate a tool for breast cancer risk assessment among Sudanese women. METHODS: Using cross-sectional design, 153 subjects were eligible to participate in our study. Data were collected from the only couple of tertiary centers in Sudan. They underwent multiple logistic regression using purposeful selection method to build the model. Various adjustments were made to determine significant predictors. Overall performance, calibration and discrimination were assessed by R 2, O/E ratio and c-statistic, respectively. RESULTS: SUDAN predictors of breast cancer were: age, menarche, family history, vegetables and fruits weekly servings, and type of cereals that traditional cuisine is made of. Both Nagelkerke R 2 (0.495) and O/E ratio (0.78) were good. c-statistic expressed the excellent discriminatory power of the model (0.864, p < 0.001, 95% CI 0.81-0.92). CONCLUSIONS: Our findings suggest that SUDAN provides a simple, efficient and well-calibrated tool to predict and classify women's lifetime risks of developing breast cancer. Input from our model could be deployed to guide utilization of the more advanced screening modalities in resource-limited settings to maximize cost effectiveness. Consequently, this might improve the stage at which the diagnosis is usually made.

Utilization of simulated patients to assess diabetes and asthma counseling practices among community pharmacists in Qatar.

Background Patient counseling is one of the most important services a pharmacist can provide to patients. Studies have shown that counseling provided by pharmacists may prevent medication related problems and improve adherence to medication therapy. Objective To explore counseling practices among community pharmacists using simulated patients and to determine if patient, pharmacist, and pharmacy characteristics influence the counseling provided by community pharmacists. Setting Private community pharmacies within Qatar. Method This is a randomized, cross sectional study where simulated patients visited community pharmacies and presented the pharmacist with a new prescription or requested a refill for either a diabetes or asthma medication. Pharmacists completed a questionnaire at the end of the simulated interaction, which was utilized to determine if patient, pharmacist, or pharmacy characteristics had any influence on the counseling provided to patients. A scoring system was devised to assess the pharmacist's counseling practices. Main outcome measure To evaluate the type of information provided by community pharmacists to the simulated patient regarding diabetes and asthma. Results One hundred and twenty-nine pharmacists were enrolled in the study. Eighty one percent of pharmacists had a score <35%. Medication name (95%), directions (47%), indication (43%), and dose (41%) were the most frequently counseled components by pharmacists during the simulated interaction. Male patients received better counseling compared to the female patients (t = 6.177; p < 0.0001). Pharmacists with a master of pharmacy degree provided significantly better counseling (f = 3.261; p = 0.042). Many pharmacists (65%) provided hypoglycemia management to patients, however, 63% referred the patient to the physician when the patient experienced hypoglycemia from inappropriate medication administration. Only 2 (7%) pharmacists correctly counseled the patient on all 8 inhaler administration steps. Majority of pharmacists (50%) educated on the role of the rescue and controller therapy in asthma, however, 33% referred the patient to the physician when the patient inquired about controller therapy use. Conclusion Patient counseling was substandard with the majority of community pharmacists focusing on the name of the medication. Pharmacists rarely assessed patient's medical history or medication use. Disease management and problem solving skills of pharmacists were suboptimal with many referring patients back to the physician.

Validation and cultural adaptation of the Arabic versions of the Mini-Mental Status Examination - 2 and Mini-Cog test.

INTRODUCTION: The elderly population is increasing around the world, and the prevalence of dementia increases with age. Hence, it is expected that the number of people with dementia will increase significantly in the coming years. The Mini-Mental Status Examination - 2 (MMSE-2) and Mini-Cog are widely used tests to screen for dementia. These scales have good reliability and validity and are easy to administer in clinical and research settings. AIM: The purpose of this study was to validate the Arabic versions of MMSE-2 and Mini-Cog. These scales were assessed against the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria for dementia, as the gold standard. METHODS: The standard versions of the MMSE-2 and Mini-Cog were translated to Arabic following the back-translation method. Then, a trained rater administered these tests to 134 Arab elderly aged >60 years. A physician, blind to the results of these two tests, assessed the participants for vascular dementia or probable Alzheimer's disease, based on the DSM-IV-TR criteria. RESULTS: The sample included 67.2% Qataris. The mean age was 74.86 years (standard deviation =7.71), and 61.9% did not attend school. The mean of the adjusted scores of MMSE-2 based on age and education level was 19.60 (standard deviation =6.58). According to DSM-IV-TR, 17.2% of the participants had dementia. Sensitivity and specificity of the MMSE-2 and the Mini-Cog together were 71.4% and 61.6%, respectively, which were better than those of each test alone. CONCLUSION: Together, the Arabic versions of MMSE-2 and Mini-Cog are good screening tools for cognitive impairment in Arabs.

Knowledge, attitude and practices of Qatari patients with type 2 diabetes mellitus.

OBJECTIVES: Patient compliance with their medications and their ability for self-management in type 2 diabetes mellitus (T2DM) is a growing cause of concern to healthcare providers. Knowledge about diabetes, attitude towards the condition and time management with respect to the condition (practice), collectively known as KAP, are known to affect compliance and play an important part in diabetes management. We aimed to describe the knowledge, attitude, practice and psychological status of adult Qatari patients with T2DM, and to explore the interaction between these and other patient-related factors which could impact on the ability of the patients to manage their diabetes and to achieve desirable health outcomes. METHODS: A questionnaire (the Diabetes Habits and Beliefs Questionnaire, DHBQ) was used to investigate the level and relationship between knowledge, attitude, general practice and psychological status of patients with T2DM. The data was collected in face-to-face interviews with patients visiting the diabetic clinic at a tertiary hospital in Qatar during the period January 2008 to March 2009. KEY FINDINGS: There were significant differences in attitude and knowledge between educational levels. Knowledge and attitude were highly correlated and the psychological status of the patient was positively associated with both knowledge and attitude. There was generally poor practice of regularly inspecting feet to detect signs of neuropathy, taking medication in relation to meals, modifying doses when necessary and setting goals for therapy. CONCLUSIONS: The data provided can assist pharmacists and other healthcare practitioners in tailoring educational programmes aimed at improving diabetes control.

The utility of an electronic adherence assessment device in type 2 diabetes mellitus: a pilot study of single medication.

OBJECTIVES: The primary objective of this pilot study was to determine if the Medication Event Monitoring System (MEMS) is capable of providing meaningful estimates of compliance within the indigenous Qatari population. The secondary objective was to highlight any specific problems which might be associated with the use of MEMS within this population. METHOD: A sample of adult diabetic Qatari patients attending an outpatient diabetic clinic were administered a Knowledge, Attitude, and Practices (KAP) questionnaire and then dispensed one of their regular medications in a MEMS((R))-fitted bottle. Data contained in the MEMS((R)) were downloaded after the patients returned for a refill and adherence was estimated using 2 methods: pill count and MEMS((R)) data. RESULTS: A total of 54 patients agreed to participate in this pilot study. Adherence to daily doses was 67.7% and with regimen 13.7%. No correlation was found between adherence assessed by pill count and MEMS((R)). The association between KAP and adherence was generally poor. A number of other issues and challenges in the use of MEMS((R)) that could affect its utility were noted and will be discussed. CONCLUSIONS: Our results revealed problems associated with the use of MEMS((R)) that could affect its usefulness in assessing adherence in this part of the world. Some issues identified in this pilot study included retrieving the MEMS((R)), registering extra opening of MEMS((R)), desire to hoard medicine by taking doses at different frequency than recorded in MEMS((R)). All these issues could be closely associated with the attitudes and practices of the patients, as demonstrated by our KAP analysis and correlations.