Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of life of elderly women with urinary incontinence: cross-sectional study

Objective: To assess the quality of life of elderly women with urinary incontinence. Method: cross-sectional study conducted with 194 elderly women with incontinence in the Center of Elderly Health Care of Joao Pessoa, Paraiba. Result: Considering the impact of urinary incontinence on the quality of life of elderly women, measured based on the International Consultation on Incontinence Questionnaire – Short Form, a mean score of 10.22 was obtained, classified as very severe. Based on the results from the Kin g ’s Health Questionnaire, it was observed that urinary incontinence had negative influence on the quality of life of the interviewed women in terms of general health, impact of incontinence, measures of severity and emotions. Conclusion: the empirical results in this study represent valuable subsidies to plan for specific interventions to improve the quality of life of these women.
MarVeLScaler: A Multi-View Learning-Based Auto-Scaling System for MapReduce

To promote cloud computing from current pay-per-request model to truly pay-per-use, tenants are crying for automatic tools to auto-estimate the amount of resources for MapReduce jobs. Such tools call for accurately quantifying the relationship among workload, resources and completion time. Various prediction models have been proposed. However, none of these models takes virtual machines’ (VMs) performance variance during a job's execution into consideration, leading to underestimate the required resources and exceed the job's deadline. To address this problem, we propose a multi-view deep learning model to capture real-time performance variance and automatically scale out the cloud cluster whenever necessary. We implement MarVeLScaler, a prototype system including two useful modules, namely, Scale Estimator and Scale Controller. Scale Estimator preliminarily estimates the required cluster size for a MapReduce job with given a concrete workload and deadline. During the runtime, Scale Controller adjusts the scale of the cluster according to its real-time running status to guarantee the job finished on time. We evaluate the performance of MarVeLScaler based on Hadoop in Alibaba Cloud. Experiments show that MarVeLScaler can provide 98.4 percent accuracy of prediction in determining initial cluster size, and save 30.8 percent of expense while still guaranteeing similar performance compared with the state-of-the-art methods.
Gamification of an asynchronous HTML5-related competency-based guided learning system

Practice e-learning systems with traditional asynchronous drills are the most common assisted-learning tools for cognitive study, and they are often employed in instructional environments. Such systems provide learners with immediate feedback that enables them to develop cognitive abilities through constant learning. In this study, a novel, asynchronous HTML5-related gamified competency-based guided learning (CBGL) system was designed. The CBGL algorithm was embedded in the system to provide personalization for enhanced learning effects. This study used the core drivers of the Octalysis gamification framework to design gamified mechanisms that ameliorated the long and tedious CBGL processes. This study used the system satisfaction test to survey the flow experience of the participants and logged their learning behaviors for data mining. The results of system satisfaction analysis revealed that the system gamification generated a favorable flow experience among participants; they actively used the system to study and felt that the guided learning process was engaging. Results of the data mining analysis also revealed that the percentage of learners who completed the CBGL process rose from 58.3% to 79.2%. Therefore, the gamified mechanism helped participants overcome the boring and lengthy learning processes of the CBGL system.
The new algorithm involving minimum spanning tree for computer networks in a growing company

The aim of this article is to present a new algorithm based on minimum spanning trees. Minimum Spanning Trees have long been used in data mining, pattern recognition and machine learning. However, it is difficult to apply traditional minimum spanning tree algorithms to a large dataset since the time complexity of the algorithms is quadratic. The given algorithm is designed to reduce this difficulty. This application has reduced the cost.
Identifying active ingredients in complex behavioural interventions for obese adults with obesity-related co-morbidities or additional risk factors for co-morbidities: a systematic review

Abstract Reducing obesity is an important preventive strategy for people who are at increased risk of major disabling or life-threatening conditions. Behavioural treatments for obesity are complex and involve several components aiming to facilitate behaviour change. Systematic reviews need to assess the components that moderate intervention effects. Electronic databases and journals were searched for randomised controlled trials of behavioural interventions targeting dietary and/or physical activity change for obese adults (mean BMI≥30, mean age≥40 years) with risk factors and follow-up data≥12 weeks. A reliable taxonomy of theory-congruent behaviour change techniques (BCTs; Abraham & Michie, 2008) was used to identify programme components. Meta-regression suggested that increasing numbers of identified BCTs are not necessarily associated with better outcomes. The BCTs provision of instructions (β =− 2.69, p=0.02), self-monitoring (β = − 3.37, p<0.001), relapse prevention (β = − 2.63, p=0.02) and prompting practice (β = − 3.63, p<0.001) could be linked to more successful interventions. Studies including more BCTs aimed at dietary change that are congruent with Control Theory were associated with greater weight loss (β = − 1.13, p=0.04). Post-hoc ratings of intervention components in published trials can lead to the identification of components and theories for behaviour change practice and research.
Cancer Risk of Focal Thyroid Incidentaloma in Patients Undergoing 18F-fluorodeoxyglucose Positron Emission Tomography-Computed Tomography Studies: Local Experience in a Single Centre

Objectives: To determine the prevalence of focal thyroid fluorodeoxyglucose positron emission tomography– computed tomography incidentaloma in our locality and evaluate the rate of malignancy. Methods: A retrospective review of 1407 individual patients who underwent fluorodeoxyglucose positron emission tomography–computed tomography imaging in a clinical positron emission tomography centre, Queen Elizabeth Hospital, from December 2006 to August 2007 was performed. Thyroid incidentaloma was generally regarded as any newly identified thyroid lesion encountered during imaging studies in patients without a history of thyroid disease. We defined it as an incidental finding of abnormally increased fluorodeoxyglucose uptake in the thyroid gland of a patient without a history of thyroid disease. Among identified patients, those who underwent further investigations were analysed using the electronic patient record system in our institution. Corresponding findings from surgical biopsy, fine needle aspiration, and histopathological findings were reviewed. Results: Of 1407 subjects, 45 (3.2%) showed focal incidentaloma, 30 of whom were not followed up further, and in 15 (33%) a histopathological diagnosis was obtained. Among the latter, 6 (40%) of the thyroid lesions turned out to be malignant. Conclusions: The point prevalence of focal thyroid incidentaloma identified by fluorodeoxyglucose positron emission tomography–computed tomography in this study was 3.2%. In all, 33% of focal thyroid incidentaloma patients underwent further histopathological investigation in our institution. In this study, there was no statistically significant difference in maximum standardised uptake value between malignant and benign nodules. Of those patients with focal thyroid incidentalomas that had been clinically selected for further investigation or surgery, 40% had malignant lesions. Further investigation of this patient group may be warranted lest it affects prognosis and management.
Effect of a 1-year lifestyle intervention on physical activity in overweight and obese children

Abstract This study evaluated the effect of a family-based multifactor intervention programme on physical activity among overweight or obese children. Children (n = 105, mean age 10.6 ± 1.07 years) with overweight or obesity were randomized into an intervention or control group. The intervention group participated in a 1-year programme aiming at lifestyle changes regarding food habits and physical activity. All children's physical activity was measured using SenseWear Armband at baseline and after 1 year. The children in both groups had a physical activity level (PAL) of 1.67 (0.27) at baseline. When comparing the intervention and control groups, no significant differences were found in physical activity outcome variables after 1 year of intervention. Contrarily to the hypotheses, both groups decreased their energy expenditure and time spent at >3 MET, and there was no change in steps and screen time after 1 year. Despite extensive efforts, the intervention showed no significant positive effect on overweight and obese children's PAL. Further studies are needed to obtain more knowledge on how to maintain or increase the PAL successfully among overweight and obese children.
Prevention and Treatment of HIV Infection in Injection Drug Users in the Bronx

Human Immunodeficiency Virus-1 infection is prevalent among injection drug users nationwide, and is especially prevalent here in the Bronx and New York City. Injection drug users are at increased risk for acquiring and transmitting HIV infection primarily because of unsafe injection practices and high-risk sex behaviors, but several studies have tested risk reduction interventions for injection drug users and found them to decrease these risk behaviors. The most successful risk reduction interventions mobilize peers, involve community outreach, and work in conjunction with substance abuse treatment programs. Other successful interventions for reducing HIV transmission risk among injection drugs users rely on medical providers to offer individualized risk reduction along with medical treatment for HIV. In this article, we review HIV prevention and treatment programs that have helped stem the HIV epidemic among injection drug users in the Bronx.
The advantages of non-monotonic logic in modular architectures: High performance and interpretable outputs with limited training data

Deep learning has become the state of the art algorithm for many areas of machine learning research. Deep learning can achieve high performance but it requires a lot of training data and is not readily interpretable. This makes it difficult to develop algorithms for new applications, as errors cannot easily be resolved when the algorithm’s reasoning is obscure, and large datasets cannot always be obtained. To address these limitations logic knowledge bases can be used for interpretable reasoning while deep learning is used used for any processing that is not suitable for representation by a knowledge base. A knowledge base requires no training data and provides interpretable outputs, and in addition, the intermediate outputs from knowledge base components can be used to guide learning for the deep learning components, making training more efficient. These advantages were central to the design of a novel architecture, which used deep learning for image processing and a combination of non-monotonic logic and decision tree induction for interpretable reasoning. Performance was evaluated with visual question answering (VQA), and a state of the art planning architecture was used to show the advantages of knowledge bases in dynamic domains. The VQA architecture’s performance was evaluated in two domains: estimating the stability of simulated block towers, and determining the messages conveyed by traffic signs. Experimental results showed that the proposed architecture achieved interpretability and high performance with small training datasets. The planning architecture was evaluated in a dynamic tower-building domain. Another domain in which a simulated Turtlebot acted as an office assistant was used to show that the VQA and planning architectures could be combined. The performance of knowledge base reasoning was improved through the use of automated axiom learning, which was shown in all four domains. In the stability and traffic domains the use of axiom learning to add to the knowledge base improved VQA accuracy, while in the tower building and office assistant domains the action plans developed after axiom learning were more sensible and efficient than those developed before.
A Botnets Circumspection: The Current Threat Landscape, and What We Know So Far

Botnets have carved a niche in contemporary networking and cybersecurity due to the impact of their operations. The botnet threat continues to evolve and adapt to countermeasures as the security landscape continues to shift. As research efforts attempt to seek a deeper and robust understanding of the nature of the threat for more effective solutions, it becomes necessary to again traverse the threat landscape, and consolidate what is known so far about botnets, that future research directions could be more easily visualised. This research uses the general exploratory approach of the qualitative methodology to survey the current botnet threat landscape: Covering the typology of botnets and their owners, the structure and lifecycle of botnets, botnet attack modes and control architectures, existing countermeasure solutions and limitations, as well as the prospects of a botnet threat. The product is a consolidation of knowledge pertaining the nature of the botnet threat; which also informs future research directions into aspects of the threat landscape where work still needs to be done.
Sound Source Localisation in Ambisonic Audio Using Peak Clustering

Accurate sound source direction-of-arrival and trajectory estimation in 3D is a key component of acoustic scene analysis for many applications, including as part of polyphonic sound event detection systems. Recently, a number of systems have been proposed which perform this function with ﬁrst-order Ambisonic audio and can work well, though typically performance drops when the polyphony is increased. This paper introduces a novel system for source localisation using spherical harmonic beamforming and unsupervised peak clustering. The performance of the system is investigated using synthetic scenes in ﬁrst to fourth order Ambisonics and featuring up to three overlapping sounds. It is shown that use of second-order Am-bisonics results in signiﬁcantly increased performance relative to ﬁrst-order. Using third and fourth-order Ambisonics also results in improvements, though these are not so pronounced.
Renoprotective effect of silymarin on gentamicin- induced nephropathy

Gentamicin (GM) is an aminoglycoside antibiotic which is used in clinical practice to treat severe gramnegative infections. Silybum marianum , also known as milk thistle, is a member of Asteraceae family and is well recognized as a hepatoprotective herbal medicine. The main objective of this present study was to evaluate the renoprotective effect of silymarin on GM-induced nephropathy. Eighteen healthy Ghezel sheep were randomly divided into three groups of control and treatments. All sheep of two groups received 20 mg/kg/body weight of GM every 8 h by intramuscular injection for 10 days. Treatment group also receive silymarin at the dose of 80 mg/case three-times a day for 20 days intramuscular injection for 14 days. This study showed that the GM-induced renal toxicity, as measured by multiple functional, structural and enzymatic factors is significantly reduced by co-supplementation of silymarin. The results of this study suggest the potential of silymarin to protect against GM-induced nephrotoxicity.
A New Frequency Domain Filter for Precise Phasor Computation of Electrical Power Current

Digital filters of electrical protective relays play primary roles in calculating the fundamental phasors of electrical power signals. In most fault cases, large amounts of decaying DC components are included in the current signals during the fault period. Decaying DC component significantly reduces the precision and convergence speed of all conventional frequency-domain filters such as Fourier and Walsh filters. In this investigation, this paper introduces a new frequencydomain filtering algorithm to remove the decaying DC components and accurately capture the fundamental power system phasors for digital protective relays. This is executed by means of a mathematical procedure using digital signal processing techniques. The proposed filter is tested for a wide variety of current signals during different fault conditions to assess its performance. The simulation results show that the proposed filtering algorithm has a superior performance at a wide range of decaying components.
Secure analysis on entire software‐defined network using coloring distribution model

Software Definition Network (SDN) has three features as separation of control and forwarding, unified management of configuration, and dynamic programming, which have greatly improved flexibility of network deployment and dynamics of network management, as well as efficiency of network transmission. However, its security problem is quite outstanding. This paper proposes a new security defense method based on coloring distribution model, which aims at the shortcomings of the current research that does not change the weak security, certainty, statics, and isomorphism of SDN. Motivated by the idea of moving target defense, our method abstracts network topology of SDN using coloring theory and realizes diversified deployment of controllers and switches, thus improving the security of network itself without changing the structure of SDN. Simulation results show that our method can prevent denial of service (DOS) attacks against controllers and switches and at the same time effectively block the worm, which is one of the most threat of smart city, propagation via switches.
Sensor-based human activity recognition via zero-shot learning

Sensor-based activity recognition aims to recognize human activities from the sensor readings. In recent years, with the advance of the sensor technology and the development of the machine learning algorithms, it obtains much progress. Activity recognition is a classification problem. Thus, existing works on sensor-based activity recognition are generally based on supervised-classification algorithms, such as SVM, HMM, CRF, kNN, and decision tree. In these works, for each class to recognize, sufficient labeled training instances belonging to this class are needed. In practical applications, the process of obtaining labeled sensor readings is time consuming and costly. The activity classes having sufficient sensor readings are generally classes predefined during data collecting-and-labeling in the model learning phase. However, in many applications, the activities needed to recognize could contain not only the predefined classes, but also some previously unseen classes which do not have labeled training instances during model learning. In this thesis, we focus on sensor-based activity recognition including the unseen classes. Under this problem setting, the training instances belong to a set of activity classes (referred to as the seen classes), while the testing instances can come from the previously unseen activity classes (referred to as the unseen classes). For problems under this problem setting, as there are no labeled training instances belonging to the unseen classes, the zero-shot learning methods are used. We focus on three problems under this setting. Firstly, we study the problem of unseen activity recognition for one person. In this problem, the classes covered by the training and the testing instances are disjoint. We propose a method, referred to as Nonlinear Compatibility Based Method (NCBM), to
Classifying Non-speech Vocals: Deep vs Signal Processing Representations

Deep-learning-based audio processing algorithms have become very popular over the past decade. Due to promising results reported for deep-learning-based methods on many tasks, some now argue that signal processing audio representations (e.g. magnitude spectrograms) should be entirely discarded, in favor of learning representations from data using deep networks. In this paper, we compare the effectiveness of representations output by state-of-the-art deep nets trained for task-specific problems, to off-the-shelf signal processing representations applied to those same tasks. We address two tasks: query by vocal imitation and singing technique classification. For query by vocal imitation, experimental results showed deep representations were dominated by signal-processing representations. For singing technique classification, neither approach was clearly dominant. These results indicate it would be premature to abandon traditional signal processing in favor of exclusively using deep networks.
Energy-Efficient Neighbor Discovery for the Internet of Things

Internet of Things (IoT) networks are usually distributed in nature. Due to the possible mobility of IoT devices, it is common and critical for each IoT device to keep discovering who is in its neighborhood, referred to as neighbor discovery. Due to the limited battery capacity of IoT devices, it is challenging to design a neighbor discovery protocol (NDP) that can achieve both low duty cycle and low discovery latency. In this article, we build a model called Circle to characterize the process of neighbor discovery in IoT networks. Then, we give a necessary and sufficient condition for neighbor discovery and theoretically prove its correctness. This is the first time in the research community that a necessary and sufficient condition is given for neighbor discovery. According to the necessary and sufficient condition, we analytically derive a lower bound of the worst case discovery latency and demonstrate when the lower bound can be achieved. The analytical model is generic as it can be used to analyze existing NDPs. Based on the Circle model and the analysis, we propose an NDP, which is also called Circle. We compare Circle with the state-of-the-art NDPs in a real testbed, and experimental results show that Circle is superior to the existing state-of-the-art NDPs.
Pulmonary Tuberculosis in a Patient with Systemic Lupus Erythematosus - A Case Study

HUSSAIN N. , JAFFERY G. , SABRI A.N. , HASNAIN S. , MIR N. Department of Micribiology and Molecular Genetics Quaid-e-Azam Campus, University of the Punjab, Lahore – Pakistan Department of Pathology, Services Institute of Medical Sciences, Lahore – Pakistan Department of Rheumatology, Fatima Memorial Hospital, Lahore Pakistan For correspondence: Dr. Nageen Hussain, Qualification: M.Sc. inMicrobiology and Molecular Genetics, PhD Scholar. Designation: Lecturer in the Department of Microbiology and Molecular Genetics Quaid-e-Azam Campus, University of the Punjab, Lahore Pakistan
Active Institutional Holdings and Dividend Payout Policy

In this paper we test whether there is an effect of active institutional investors onto firm’s dividend policy. The focus on active institutions is due to the fact that it is active institutions that are more likely to affect firms’ payout policy as compared to passive institutions who hold a stock indirectly via an index. We construct a proxy variable that measures the active institutional ownership of a given firm. We show that higher active ownership results in a lower dividend yield. We also show that different types of institutions have different effect on the likelihood of dividend initiation. A larger active ownership by mutual funds lead to a higher chance of the dividend initiation while active ownership by other types of institutions, e.g. banks or pension funds, result in a lower chance of dividend initiation. Finally, the effect of active ownership on payout policy exhibits a lag of one to two years.