Analysis of Hand Segmentation in the Wild

A large number of works in egocentric vision have concentrated on action and object recognition. Detection and segmentation of hands in first-person videos, however, has less been explored. For many applications in this domain, it is necessary to accurately segment not only hands of the camera wearer but also the hands of others with whom he is interacting. Here, we take an in-depth look at the hand segmentation problem. In the quest for robust hand segmentation methods, we evaluated the performance of the state of the art semantic segmentation methods, off the shelf and fine-tuned, on existing datasets. We fine-tune RefineNet, a leading semantic segmentation method, for hand segmentation and find that it does much better than the best contenders. Existing hand segmentation datasets are collected in the laboratory settings. To overcome this limitation, we contribute by collecting two new datasets: a) EgoYouTubeHands including egocentric videos containing hands in the wild, and b) HandOverFace to analyze the performance of our models in presence of similar appearance occlusions. We further explore whether conditional random fields can help refine generated hand segmentations. To demonstrate the benefit of accurate hand maps, we train a CNN for hand-based activity recognition and achieve higher accuracy when a CNN was trained using hand maps produced by the fine-tuned RefineNet. Finally, we annotate a subset of the EgoHands dataset for fine-grained action recognition and show that an accuracy of 58.6% can be achieved by just looking at a single hand pose which is much better than the chance level (12.5%).Comment: Accepted at CVPR 201

Empirical analysis of rough set categorical clustering techniques based on rough purity and value set

Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, attention has been put on categorical data clustering, where data objects are made up of non-numerical attributes. The implementation of several existing categorical clustering techniques is challenging as some are unable to handle uncertainty and others have stability issues. In the process of dealing with categorical data and handling uncertainty, the rough set theory has become well-established mechanism in a wide variety of applications including databases. The recent techniques such as Information-Theoretic Dependency Roughness (ITDR), Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR), Min-Min Roughness (MMR), and standard-deviation roughness (SDR). This work explores the limitations and issues of ITDR, MDA and MSA techniques on data sets where these techniques fails to select or faces difficulty in selecting their best clustering attribute. Accordingly, two alternative techniques named Rough Purity Approach (RPA) and Maximum Value Attribute (MVA) are proposed. The novelty of both proposed approaches is that, the RPA presents a new uncertainty definition based on purity of rough relational data base whereas, the MVA unlike other rough set theory techniques uses the domain knowledge such as value set combined with number of clusters (NoC). To show the significance, mathematical and theoretical basis for proposed approaches, several propositions are illustrated. Moreover, the recent rough categorical techniques like MDA, MSA, ITDR and classical clustering technique like simple K-mean are used for comparison and the results are presented in tabular and graphical forms. For experiments, data sets from previously utilized research cases, a real supply base management (SBM) data set and UCI repository are utilized. The results reveal significant improvement by proposed techniques for categorical clustering in terms of purity (21%), entropy (9%), accuracy (16%), rough accuracy (11%), iterations (99%) and time (93%). vi

An improved Pi-Sigma neural network using error feedback for time series prediction

Time series prediction grabs much attention because of its effect on the vast range of real-life applications. Traditional time series forecasting tools have some limitations like slow training process, less efficient training methods that decrease the performance of the model. Higher Order Neural Network (HONN) using recurrent feedback appeared as a powerful technique in the domain of time series prediction and it has the ability to expand the input space, making them more efficient for solving complex problems and perform high learning abilities in time series prediction. This study proposed a model called improved Pi-Sigma Neural Network using Error Feedback (PSNN-EF) which combines the properties of Pi-Sigma Neural Network (PSNN), recurrence and error feedback. PSNN-EF uses backpropagation gradient descent algorithm for training purpose and is tested with physical time series signals of humidity, evaporation and wind direction datasets that are collected from Malaysian Meteorological Department (MMD). The prediction result is compared with Jordan Pi-Sigma Neural Network (JPSN) and the ordinary PSNN. The results clearly showed that the PSNN-EF significantly improved the computational efficiency of the training process and has been developed to produce more realistic and acceptable results. The average improvement of the proposed model on evaporation dataset is 2.06%, humidity is 7.45% and wind is 3.51% as compared to other models. The benefit of using error feedback is that it generates more accurate and promising results of prediction. Therefore, from the performance of the proposed method, it is noticed that PSNN-EF can provide better solution to JPSN for one-step-ahead prediction of those three datasets

Impact of Green Human Resource Management on Job Seekers’ Attraction in a Developing Economy

The main purpose of this research is to investigate the impact green human resource management practices on job seekers’ attraction in a developing economy like Pakistan. It also explores the significant impact of employer reputation on the relationship of green human resource management practices on job seekers’ attraction. The impact of green human resource management practices on job seekers’ attraction is further investigated with the moderating role of employer reputation. A sample of 450 students attending the final year of a Master’s degree in Business Administration at three universities of Southern Punjab was studied by using survey design. Green human resource management practices have positive impact on job seeker attraction and employer good reputation increases the positive impact of green human resource management practices on job seeker attraction. The results provide some inputs for organization to invest more in green human resource management activities. It also opens new avenues for organization to understand that green practices enhance the green reputation of the firms and increases job seekers’ attraction towards the firm. The greening of human resource management is an emerging topic for scholars and consultants in developing economies. No previous study has explored the impact of green human resource management practices on job seekers’ attraction in a developing country like Pakistan. Keywords: Green human resource management, green reputation, job seekers’ attractio

Individual Differences in Categorization

The appearance of individual differences used to be regarded as noise in psychological experiments, but is slowly becoming a tool used to enhance and solidify findings in various fields of cognitive psychology. This presentation aims to very briefly discuss individual differences and categorization and what questions future research could aim to answer

Perceptions of arranged marriages by young Pakistani Muslim women living in a western society.

Exploration of attitudes towards arranged marriages were examined from the perspective of second-generation Pakistani Muslim women living in a western society. Symbolic Interactionism and Interpretive Interactionism were the theoretical and methodological considerations respectively. Twenty single females, between the ages of 16 to 30 years, living in Canada or the U.S. were interviewed utilizing an unstructured schedule interview. In addition to the interviews, WebGrid analyses of all females were conducted. The main research question addressed the role of western values in influencing the type of mate-selection one adheres to. Other questions centered around the Pakistani women\u27s definition of the situation with regards to arranged marriages. Cultural identity, communication patterns, inter-generational conflicts, double standard, family honor, dating, and interaction with males are also examined. The results indicated that even though Pakistani parents, especially fathers, are perceived to be resistant to cultural change, western values, through continuous interaction, are playing a determining role in the process of mate-selection for second generation Pakistani Muslim females. Most Pakistani women are adapting and modifying attitudes which reflect the ideas of western ideology of greater self-expression and personal gratification. Family honor is probably the only factor which remained in the realms of eastern ideology.Dept. of Sociology and Anthropology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1999 .Z35. Source: Masters Abstracts International, Volume: 39-02, page: 0415. Adviser: Muhammad Shuraydi. Thesis (M.A.)--University of Windsor (Canada), 1999

Impact of Emotional Intelligence on Leadership Effectiveness: Mediation Effect of Perceived Organizational Support and Supervisor Support

This study aims to investigate the impact of emotional intelligence (EI) on effectiveness of leaders based on a sample of 199 middle managers operating in UAE and Pakistan. Respondents were selected through convenience sampling and survey based questionnaires were used as instrument for data gathering. The research indicated significant relationship between EI and leadership effectiveness, and represents this relation to be mediated through perceived organizational support at organizational level and supervisor support at individual level. Structural equation modeling (SEM) and Preacher & Hayes regression approaches were used to measure mediation effect. The mediation results were significant for both of the mediators. Moreover, the study displays that leaders were more effective when they use emotional intelligence properly. Although there is evidence for the association between emotional intelligence and leadership effectiveness, the mediating role of perceived organizational support and supervisor support on this association remained unexplored till now. Keywords: Emotional intelligence, Leadership Effectiveness, Perceived organizational Support, Supervisor Suppor

Impact of Islamic Motives, Serviceability & Customers Awareness on Customer Satisfaction from Islamic Banks

This study aims to investigate the determinants of customers’ satisfaction from Islamic Banks by both quantitative and qualitative analysis. A sum of 131 respondents was selected through purposive and snowball sampling and survey based questionnaires were used as instrument for data gathering for quantitative side of the research, while 11 in-depth, semi-structured interviews were taken for the qualitative aspect of the study. The research indicated significant relationship between Islamic motives of customers being Muslims for opening a bank account in Islamic banks and serviceability with the level of customer satisfaction. Customer awareness was found to be the most impacting variable on level of satisfaction. Although there is evidence for the association between serviceability and customer satisfaction, the combined relation of Islamic motives and customer awareness by a mixed method approach is unexplored till now. The result of quantitative analysis (questionnaires) was counter checked by qualitative analysis (interviews) that brings novelty to this research. An important finding of research revealed that most of the customers are not aware of Islamic products and even those who were aware were not using most of the Islamic banking products. Keywords: Islamic Motives, Serviceability, Customer Awareness, Customer Satisfaction, Islamic Banks, Mix Method Approac