Integrating the document object model with hyperlinks for enhanced topic distillation and information extraction

Topic distillation is the process of finding authoritative Web pages and comprehensive “hubs” which reciprocally endorse each other and are relevant to a given query. Hyperlink-based topic distillation has been traditionally applied to a macroscopic Web model where documents are nodes in a directed graph and hyperlinks are edges. Macroscopic models miss valuable clues such as banners, navigation panels, and template-based inclusions, which are embedded in HTML pages using markup tags. Consequently, results of macroscopic distillation algorithms have been deteriorating in quality as Web pages are becoming more complex. We propose a uniform fine-grained model for the Web in which pages are represented by their tag trees (also called their Document Object Models or DOMs) and these DOM trees are interconnected by ordinary hyperlinks. Surprisingly, macroscopic distillation algorithms do not work in the finegrained scenario. We present a new algorithm suitable for the fine-grained model. It can dis-aggregate hubs into coherent regions by segmenting their DOM trees. Mutual endorsement between hubs and authorities involve these regions, rather than single nodes representing complete hubs. Anecdotes and measurements using a 28-query, 366000-document benchmark suite, used in earlier topic distillation research, reveal two benefits from the new algorithm: distillation quality improves and a by-product of distillation is the ability to extract relevant snippets from hubs which are only partially relevant to the query

Instruction and Quality Control in the College of Continuing Education at Embry-Riddle Aeronautical University

The Faculty of Embry-Riddle Aeronautical University, in adopting its constitution, chose to have as its primary responsibility 11 assurance of academic excellence through delivery of the academic process which includes quality teaching, scholarly activity, and service. It is with continued improvement in the first of these listed items, quality teaching and support of improvement of the second, scholarly activity, that this symposium series has been initiated. It is but one of numerous ways in which we, as a Faculty, seek continuous improvement in our quest for academic excellence. This is a product of the Faculty and by the Faculty, and we hope that all who teach, wherever you teach, will benefit from it. You will notice that much of the emphasis of the papers presented here deals with the adult student. For those of you who are not familiar with the Embry - Riddle Aeronautical University and its College of Continuing Education, some idea of the background, size, and scope is presented by Dr. Hal Gray in the first paper

Exploring the factors related to academic publication productivity among selected Malaysian academic engineers and scientists

This is an exploratory study, which aims to examine the factors affecting the research publication productivity of academic engineers and scientists from the National University of Malaysia (UKM) and University of Malaya (UM). This study aims to identify problems, as well as increase the understanding of factors conducive for a productive academic research environment. [Continues.

Mining and Analyzing the Academic Network

Social Network research has attracted the interests of many researchers, not only in analyzing the online social networking applications, such as Facebook and Twitter, but also in providing comprehensive services in scientific research domain. We define an Academic Network as a social network which integrates scientific factors, such as authors, papers, affiliations, publishing venues, and their relationships, such as co-authorship among authors and citations among papers. By mining and analyzing the academic network, we can provide users comprehensive services as searching for research experts, published papers, conferences, as well as detecting research communities or the evolutions hot research topics. We can also provide recommendations to users on with whom to collaborate, whom to cite and where to submit.In this dissertation, we investigate two main tasks that have fundamental applications in the academic network research. In the first, we address the problem of expertise retrieval, also known as expert finding or ranking, in which we identify and return a ranked list of researchers, based upon their estimated expertise or reputation, to user-specified queries. In the second, we address the problem of research action recommendation (prediction), specifically, the tasks of publishing venue recommendation, citation recommendation and coauthor recommendation. For both tasks, to effectively mine and integrate heterogeneous information and therefore develop well-functioning ranking or recommender systems is our principal goal. For the task of expertise retrieval, we first proposed or applied three modified versions of PageRank-like algorithms into citation network analysis; we then proposed an enhanced author-topic model by simultaneously modeling citation and publishing venue information; we finally incorporated the pair-wise learning-to-rank algorithm into traditional topic modeling process, and further improved the model by integrating groups of author-specific features. For the task of research action recommendation, we first proposed an improved neighborhood-based collaborative filtering approach for publishing venue recommendation; we then applied our proposed enhanced author-topic model and demonstrated its effectiveness in both cited author prediction and publishing venue prediction; finally we proposed an extended latent factor model that can jointly model several relations in an academic environment in a unified way and verified its performance in four recommendation tasks: the recommendation on author-co-authorship, author-paper citation, paper-paper citation and paper-venue submission. Extensive experiments conducted on large-scale real-world data sets demonstrated the superiority of our proposed models over other existing state-of-the-art methods

Biases in scholarly recommender systems: impact, prevalence, and mitigation

We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market