Application of social networking algorithms in program analysis: understanding execution frequencies

2011 Summer.Includes bibliographical references.There may be some parts of a program that are more commonly used at runtime, whereas there may be other parts that are less commonly used or not used at all. In this exploratory study, we propose an approach to predict how frequently or rarely different parts of a program will get used at runtime without actually running the program. Knowledge of the most frequently executed parts can help identify the most critical and the most testable parts of a program. The portions predicted to be the less commonly executed tend to be hard to test parts of a program. Knowing the hard to test parts of a program can aid the early development of test cases. In our approach we statically analyse code or static models of code (like UML class diagrams), using quantified social networking measures and web structure mining measures. These measures assign ranks to different portions of code for use in predictions of the relative frequency that a section of code will be used. We validated these rank ordering of predictions by running the program with a common set of use cases and identifying the actual rank ordering. We compared the predictions with other measures that use direct coupling or lines of code. We found that our predictions fared better as they were statistically more correlated to the actual rank ordering than the other measures. We present a prototype tool written as an eclipse plugin, that implements and validates our approach. Given the source code of a Java program, our tool computes the values of the metrics required by our approach to present ranks of all classes in order of how frequently they are expected to get used. Our tool can also instrument the source code to log all the necessary information at runtime that is required to validate our predictions

How does Norwegian technology start-ups use open innovation Strategies for to get access of new business ideas?

Master of Science in Business / Siviløkonom - Nord universitet 201

A Strategy for Asymmetrical Measures to Reduce Bribery in Bangladesh

Bangladesh is one of the most corrupt countries with a rampant bribery scenario. In the public service sector of this country, service is almost considered to be unattainable without bribing the service providers. From the viewpoint of specialists and ad-hoc academicians, symmetric punishment measure has been a failed mechanism to stop rampant bribery. This study initially pondered the reasoning behind the severity of the bribery scenario in Bangladesh using the dataset of the National Households Survey's 2017 (NHS) of Transparency International Bangladesh (TIB). It analyzed the legal jurisdictions of bribery especially the penal code 1860. In this study, following Basu argument on the asymmetric punishment system (harassment bribe) for India, an asymmetric punishment measure has been proposed through a game theoretical explanation about how it will work for Bangladesh instead of the current symmetric one. This game-theoretical analysis shows that asymmetric punishment is more efficient for reducing rampant bribery in a country than symmetric punishment. The study suggests the government initiate an asymmetric punishment policy on bribery with a strict punitive measure and monitoring of bribe-taking which, with a qualitative approach and case study

Prevalence of depression and anxiety among university students during COVID-19 in Bangladesh: A cross sectional study

Introduction: The COVID-19 outbreak has become a challenging crisis for public health. During the COVID-19 pandemic, the indefinite closure of educational institutions in Bangladesh has a severe impact on the mental health of students. Purpose: The purpose of this study is to investigate factors that might have considerable influence on the mental health of students during quarantine in Bangladesh though they did not explore in previous studies on mental health status during the pandemic. Methodology: A standardized questionnaire was generated using PH9 and GAD7 to measure depression and anxiety levels. A total of 203 responses were collected from university students of Bangladesh through social media. Results: Descriptive statistics found that 37% of the students experienced moderate to severe anxiety while 54% faced moderate to severe depression. Ordinal Logistic Regression analysis found that anxiety is significantly related to gender, students’ current affiliation status in university (e.g., sophomore, masters), and time spent on watching TV while depression was related to family member’s contact with Covid-19, performing multiple activities as hobbies, and spending time in reading and writing. Conclusions: This study adds valuable findings in the existing literature, and it will help Students, university authorities, and government can take productive steps to tackle mental health issue

Pattern of skeletal metastasis in breast cancer patients of northern part of Bangladesh

Background: Breast cancer is the most frequent female cancer, especially in 'developed' countries. 30-85% of metastatic breast cancer patients will develop bone metastases during the course of the disease. The study was aimed to evaluate the pattern of skeletal metastasis in breast cancer patients with whole body bone scan using 99mTechnetium methyl diphosphonate (99m'Tc-MDP). Methods: This single center based retrospective observational study was conducted among the histopathologically proven breast cancer patients referred to INMAS, Rangpur for 99m'Tc-MDP bone scintigraphy between March 2015 and March 2019. Bone scan was done with SPECT digital dual head gamma camera (Siemens S series) 3 hours after intravenous bolus injection of 20 mci99mI'c-MDP. Results: Out of total 300 patients, 120 (40%) patients were found secondaries in bones. Among them 80(66.7%) had only axial skeletal metastases and 15 (12.5%) had appendicular skeletal metastases. Both axial and appendicle skeletal metastases were found in 25 (20.8%) patients.  Conclusion: Thoraco-lumbar spine was the most common site of involvement in our study.

On-line Process Physics Tests via Lyapunov-based Economic Model Predictive Control and Simulation-Based Testing of Image-Based Process Control

Next-generation manufacturing involves increasing use of automation and data to enhance process efficiency. An important question for the chemical process industries, as new process systems (e.g., intensified processes) and new data modalities (e.g., images) are integrated with traditional plant automation concepts, will be how to best evaluate alternative strategies for data-driven modeling and synthesizing process data. Two methods which could be used to aid in this are those which aid in testing data-based techniques on-line, and those which enable various data-based techniques to be assessed in simulation. In this work, we discuss two techniques in this domain which can be applied in the context of chemical process control, along with their benefits and limitations. The first is a method for testing data-driven modeling strategies on-line by postulating the experimental conditions which could reveal if a model is correct, and then attempting to collect data which could help to reveal this. The second strategy is a framework for testing image-based control algorithms via simulating both the generation of the images as well as the impacts of control on the resulting systems

MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning

Mobile phones and other electronic gadgets or devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data. Mobile health data use mobile devices to gather clinical health data and track patient vitals in real-time. Our study is aimed to give decisions for small or big sports teams on whether one athlete good fit or not for a particular game with the compare several machine learning algorithms to predict human behavior and health using the data collected from mobile devices and sensors placed on patients. In this study, we have obtained the dataset from a similar study done on mhealth. The dataset contains vital signs recordings of ten volunteers from different backgrounds. They had to perform several physical activities with a sensor placed on their bodies. Our study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression) to analyze and predict human health behavior. XGBoost performed better compared to the other machine learning algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in specificity, and 99.66% in F1 score. Our research indicated a promising future in mhealth being used to predict human behavior and further research and exploration need to be done for it to be available for commercial use specifically in the sports industry.Comment: 6, Accepted by 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE

Test Methods for Image-Based Information in Next-Generation Manufacturing

Typical control designs in the process systems engineering literature have assumed that the primary sensing methodologies are traditional instruments such as thermocouples. Dig- italization is changing the landscape for manufacturing, and data-based sensing modalities (e.g., image-based sensing) are becoming of greater interest for plant control. These considerations require novel test/evaluation solutions. For example, process systems engineering researchers may wish to test image-based sensors in simulation. In this work, we provide preliminary thoughts on how image-based technologies might be evaluated via simulation for process systems

How does Norwegian technology start-ups use open innovation Strategies for to get access of new business ideas?

Master of Science in Business / Siviløkonom - Nord universitet 201

The trilogy of job stress, motivation, and satisfaction of police officers: Empirical findings from Bangladesh

Purpose: The purpose of the study was to identify the trilogy of job stress and job motivation on job satisfaction. For this purpose, police officers of Khulna city were investigated accordingly. Research Methodology: The questionnaire is quantitative in nature and a standard questionnaire was followed throughout the research study. The survey was done in police stations of Khulna city and information was taken from 100 officers from sub-inspector to police commissioners. Results: The study found that job stress is negatively related to job satisfaction. Along with this, there is a negative correlation between job stress and job motivation. However, a positive correlation exists between job motivation and job satisfaction. Limitations: The study result is based on the police personnel in Khulna city rather than in other cities in Bangladesh. There are not prevailing the same ratio of male and female which could affect the measurement of findings. Contribution: The study will help government practitioners and policymakers to understand job stress, motivation, and satisfaction of police personnel in Khulna city