Role of agriculture in economic growth of Pakistan

This research based on the role of agriculture in the economic growth of Pakistan. Secondary data has been collected from the year 1980-2010 from the government authentic websites. For this purpose simple regression applied to identify the significance relationship of agricultural sub-sectors with GDP. Results suggested that there is the significance role of agriculture sub-sectors towards the economic growth only forestry showed insignificant relationship with GDP. Another objective is based on to know the contribution of each sub-sector over the aggregate agriculture amount. Result suggest that crops and livestock’s total contribute 91% combined in the aggregate agriculture sector that represent significance contribution for the performance regarding in this sector while fisheries and forestry have minimal contribution because of many reasons, major reasons involved low investment intensity in this sector, insufficient facilities, untrained and unskillful labor force engaged with it.Economic growth, major crops, minor crops, Livestock, forestry, fisheries, Gross Domestic Product (GDP)

The Impact of Gender Difference Motivating Assessing and Grading Student’s Performance

This experiment was conducted to find out the influence of grade incentives and gender on student’s performance at the graduate level. We perform a two way analysis of variance on a sample of three groups of students taking a first-year core mathematics course and another three groups taking a fourth-year compulsory accounting course. We find that grade incentives significantly affect student performance for both sampled courses across all six groups. Gender is found to significantly affect the performance of mathematics students, but not of accounting students. The interaction between gender and grade incentives does not have a significant impact on performance in either experiment. Keywords: Student performance, grade incentive, gender, experimental research, accounting students, mathematics students.

Value Co-Creation in Branding Social Marketing Services: An Exploratory Study

The study intends to determine how informally social pioneering initiative in terms of value cocreation gets transformed into vibrant social brands having credibility and sustainable dimensions. This phenomenon is critical to study in order to advance the body of knowledge of social marketing, particularly in context to branding social causes. The rationale for continuing with conventional approaches reinforces model ways of thinking in social marketing. This is one of the reasons that several efforts were made to promote development programs like Millennium Development Goals, Non-smoking, and environmental conservation have barely come to fruition. Lack of success or failure could only be attributed due to lack of conceptual advancement and developing innovative techniques to transform the creative ideas of social marketing into practices. This research transcends the traditional approaches taking group rather than individual as a unit of analysis. An exploratory study has been conducted to find out what are the major determinants, ideas and thoughts that transform social pioneering initiatives in to a credible brand. The study confirms that value co-creation is a function of transcendental values that people experience while interacting and consuming social marketing services

Impact of foreign capital inflows on economic growth in the presence of currency and banking crises

Foreign capital inflows (FCI) have been considered to be a key element in the process of economic globalization and integration of the world economy. However, the frequent occurrence of financial crises around the world has awakened the debate about the causes, consequences, impact and aftershocks of these crises. These sorts of financial crises are majorly occurring because of systemic banking crisis and currency crisis. These crises significantly influence the relationship between FCI and economic growth. The objective of this study is to identify the impact of foreign direct investment, foreign debt, workers’ remittances and exports of goods and services on economic growth in high, upper middle, lower middle and low income countries. To attain the objective of this research, we collect a panel data of 96 countries and group them on the basis of different income levels. The final sample of this study consists of 10 low income countries, 23 lower middle income countries, 30 upper middle income countries and 33 high income countries. We employed fixed effect & random effect model estimation method to judge the desired relationship among variables. Fully modified ordinary least squares (FMOLS) has also been used to ensure the robustness of initial results. Results indicate the negative and significant influence of systemic banking and currency crisis. Results also indicate the positive and significant impact of all four types of FCI on economic growth in all income level countries except, remittances in low income countries and foreign debt in lower middle income. These two results show the negative impact on economic growth. Results also conclude that the banking and currency crisis are harmful for the relationship of foreign direct investment and economic growth in all income level countries. The study recommends several policy implications to improve the positive impact of foreign capital inflows on economic growth and reduce or control the negatively influence of systemic banking crisis and currency crisis on the relationship of foreign capital inflows and economic growt

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Validity of capital asset pricing model: evidence from Karachi stock exchange

This study investigates the validity of Capital Asset Pricing (CAP) Model in Karachi stock exchange (KSE). The data of 387 companies of 30 different sectors on monthly, quarterly and semiannual basis are used. The Paired sample t- test is applied to find the difference between actual and expected returns. Results show that capital asset pricing model (CAPM) predict more accurately the expected return on a short term investment as compare to long term investment. It is recommended that the investors should more focus on CAPM results for short term as compare to long term investments in KSE.Portfolio choice, Investment Decisions, Capital Assets Pricing Model, Risk

Do Workers’ Remittances Boost Human Capital Development?

This study examines the influence of workers’ remittances along with the economic governance system on human capital development in 17 countries having low income, lower middle, upper middle and high income levels by using the annual panel between 1996 and 2013. Overall, results of fixed-effects model reveal that workers’ remittances have significantly positive impact on the human capital development. Results also reveal the positive and significant impact of all selected variables of economic governance system on human capital. It is concluded that the strong economic governance system strengthens the association between workers’ remittances and human capital during the aforementioned time period. JEL Classification: F24, J23 Keywords: Remittances, Economic Governance System, Human Capital Developmen

Work Environment and its Impact on Triple Constraint of Project Management

Project management’s fundamental concern is to effectively manage its triple constraints throughout the life cycle of a project to maximize productivity. At the same timework environment is considered a key feature, which influences the framework of project management. The present study assesses the impact of the work environment on the triple constraints (Scope, Time and Cost) of projects in the IT industry. The theoretical framework comprises Remuneration, Job Satisfaction, Job Security, and Working Hours as components of work environment and triple constraints as the dependent variable. Three hundred Project Managers across a number of IT firms have been approached, out of which 279 have responded to the questionnaire. The measurement tool has been developed by the researcher except for one construct, which has been adopted, followed by a pilot study. Inferential statistics have been applied to test the data. The study concludes that all project managers view a flexible and conducive work environment as bearing a strong relationship with the triple constraint of project management

Computational reinforcement learning using rewards from human feedback

University of Technology Sydney. Faculty of Engineering and Information Technology.A promising method of learning from human feedback is reward shaping, where a robot is trained via human-delivered instantaneous rewards. The existing approach, which requires numerous reward signals about the quality of agent’s actions from the human trainer, is based on a number of assumptions about human capabilities. For example, it assumes that humans can provide a precisely correct feedback to an agent’s action, or that they would always prefer to train an agent by means of reward signals, or that they can assess an agent’s actions for any length of training. In this thesis, we have relaxed these assumptions and have addressed two important issues which are not handled by the existing approach. First, how to compute a potential function using human feedback which can indicate the correctness of an action in terms of increasing or decreasing potential. Second, how to design training methods which cater to human preferences. Furthermore, we have identified that there are two important preferences of a human trainer in the application of reward shaping: (a) a preference to transfer knowledge by providing demonstrations and (b) a preference for short training durations. To address these issues, we have introduced three new methods of computing rewards from human-feedback. The first method, named rewards from state preference, takes human feedback as preferences of states in terms of distance to the goal state. It removes the assumption of highly accurate evaluative feedback from the user. It computes a high-quality potential function for potential-based reward shaping from only a few human feedbacks. Using feedbacks as state preferences, a ranking model is learned which computes a complete ranking of states. These state rankings define a potential function for potential-based reward shaping. This method learns a policy much faster than a reinforcement learner which is trained without human feedbacks. The second method, named rewards from action labels, replaces the traditional evaluative-style feedback approach with a demonstration-style feedback approach. The method caters to the human preference of providing a demonstration. It takes human-feedback as an action label for the current state, which is similar to providing demonstrations. The agent acts using its own policy. A reward function is computed by comparing agent’s action with the action label. We found that this method can be favorable to a naïve user as compared to the traditional evaluative-style feedback method. Finally, the third method, named rewards from part-time trainers, is designed to reduce the load of a single dedicated trainer by curtailing the length of a training session. A policy is taught by a number of trainers. Each trainer provides reward signals for a small number of steps. Experiments, using online crowd, showed that the random part-time trainers can collectively train a good policy. In a survey, conducted for this method, people overwhelmingly voted in favor of the idea of training for a short duration. Overall, this thesis contributes towards further enhancing the application scope of reward shaping. It develops three new efficient techniques of conducting reward shaping using human feedbacks of different types