Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains

There has been increased interest in devising learning techniques that combine unlabeled data with labeled data ? i.e. semi-supervised learning. However, to the best of our knowledge, no study has been performed across various techniques and different types and amounts of labeled and unlabeled data. Moreover, most of the published work on semi-supervised learning techniques assumes that the labeled and unlabeled data come from the same distribution. It is possible for the labeling process to be associated with a selection bias such that the distributions of data points in the labeled and unlabeled sets are different. Not correcting for such bias can result in biased function approximation with potentially poor performance. In this paper, we present an empirical study of various semi-supervised learning techniques on a variety of datasets. We attempt to answer various questions such as the effect of independence or relevance amongst features, the effect of the size of the labeled and unlabeled sets and the effect of noise. We also investigate the impact of sample-selection bias on the semi-supervised learning techniques under study and implement a bivariate probit technique particularly designed to correct for such bias

Global Leadership and Managerial Competencies of Indian Managers

A review of the literature on the qualities of effective managers, leaders and world class or global manager indicates a good degree of consistency in the qualities required to be called a global manager. In these days when mergers and acquisition have become common and national boundaries are crossed with ease in acquiring new businesses and setting up new businesses it is necessary to understand and acquire the competencies needed to be globally successful leader. This paper identifies 25 such qualities from a 360 feedback survey of 762 senior and top level managers from manufacturing, services and pharma sectors combined with those from a mix of organizations belonging to two leading business houses of India. An analysis of the open ended assessments given by nearly 7600 managers indicated the most frequently perceived strengths and weaknesses of Indian management. Job knowledge comes out as the most frequently observed strong point of Indian managers and this cuts across various sectors and business houses. Communication, team work, and hard work come out as other strong points of more than 20 per cent of Indian managers. Short temper, open-mindedness, and inability to build juniors are the most frequently mentioned areas needing improvement. Vision, values, strategic thinking, decision making skills, risk taking, innovativeness, ability to learn from mistakes, learning orientation and self renewal efforts, and cross cultural sensitivity are other qualities lacking in Indian managers to be called as global managers. These qualities are either not exhibited dominantly or are not received bye fellow managers. Future management education and management development programmes should focus on these qualities to prepare Indian managers to be world class managers.

Impact of 360 Degree Feedback: A Follow-up study of Four Organizations

A large number of organizations have been using 360 degree feedback in India as leadership development intervention. This paper is based on the feedback of 43 participants from four companies where the 360 Degree Feedback program was initiated. The study was done using a questionnaire method. The results indicated that there has been an overall positive impact reported of 360 Degree intervention on ones professional life after 360DF. More than 60% of the participants report that they visited 360DF data every quarter. 24 participants reported that about 50% of their action plans prepared at the end of the 360 intervention were implemented. At least 30% of the action plans were achieved by 6 of the participants and 2 participants reported achievement of all their action plans. The participants also reported that the RSDQ model based 360DF tool provided detailed insight covering various parameters of one’s role. The participants also recommend that with more periodic follow up and review sessions (every quarter) anchored by internal HR and more focus and seriousness among the participants to work on the action plans will result in using 360 DF for change and growth

Will This Paper Increase Your h-index? Scientific Impact Prediction

Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."Comment: Proc. of the 8th ACM International Conference on Web Search and Data Mining (WSDM'15