Tracy Mullen

Bio: Tracy Mullen is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Resource allocation & Wireless sensor network. The author has an hindex of 13, co-authored 32 publications receiving 820 citations. Previous affiliations of Tracy Mullen include Penn State College of Information Sciences and Technology.

Sponsored search: an overview of the concept, history, and technology

Abstract: The success of sponsored search has radically affected how people interact with the information, websites, and services on the web. Sponsored search provides the necessary revenue streams to web search engines and is critical to the success of many online businesses. However, there has been limited academic examination of sponsored search, with the exception of online auctions. In this paper, we conceptualise the sponsored search process as an aspect of information searching. We provide a brief history of sponsored search and an extensive examination of the technology making sponsored search possible. We critique this technology, highlighting possible implications for the future of the sponsored search process.

A framework for dynamic hard/soft fusion

Abstract: We report on the ongoing development of a research framework for dynamic integration of information from hard (electronic) and soft (human) sensors. We describe this framework, which includes representation of 2nd order uncertainty. We outline current and planned human-in-the-loop experiments in which an ldquoad hoc community of human observersrdquo provides input reports via mobile phones and PDAs. Our overall approach is based on three pillars: traditional sensing resources (ldquoS-spacerdquo), dynamic communities of human observers (ldquoH-spacerdquo) and resources such as archived sensor data, blogs, reports, dynamic news reports from citizen reporters via the Internet (ldquoI-spacerdquo). The sensors in all three of these pillars need to be characterized and calibrated. In H-space and I-space, calibration issues related to motivation, truthfulness, etc. must be considered in addition to the standard physical characterization and calibration issues that need to be considered in S-space.

Information markets vs. opinion pools: an empirical comparison

Abstract: In this paper, we examine the relative forecast accuracy of information markets versus expert aggregation. We leverage a unique data source of almost 2000 people's subjective probability judgments on 2003 US National Football League games and compare with the "market probabilities" given by two different information markets on exactly the same events. We combine assessments of multiple experts via linear and logarithmic aggregation functions to form pooled predictions. Prices in information markets are used to derive market predictions. Our results show that, at the same time point ahead of the game, information markets provide as accurate predictions as pooled expert assessments. In screening pooled expert predictions, we find that arithmetic average is a robust and efficient pooling function; weighting expert assessments according to their past performance does not improve accuracy of pooled predictions; and logarithmic aggregation functions offer bolder predictions than linear aggregation functions. The results provide insights into the predictive performance of information markets, and the relative merits of selecting among various opinion pooling methods.

Customer-Driven Sensor Management

Abstract: Customer-driven sensor management advocates applying e-commerce concepts and advances to sensor management. In e-commerce, customer wants essentially drive the production process. Sensor management has traditionally followed a much less capitalistic process, producing information "goods" based on predefined system goals and priorities. We explore here some of the possibilities and advantages of incorporating a customer-driven market-based approach to sensor management.This article is part of a special issue on Self-Managing Systems.

A Comparative Analysis of RFID Adoption in Retail and Manufacturing Sectors

Abstract: Radio frequency identification (RFID) technology mandates by large retailers and various government agencies has driven compliance requirements for many organizations to implement the technology. In this paper, we use content analysis methodology and examine open literature, news releases, industry white papers, and published journal and conference articles to identify and compare current implementation status, adoption drivers, potential benefits, supply chain activities, applicable tasks, and challenges of implementing RFID in the retail and manufacturing sectors. Our analysis concluded that whereas RFDD applicable tasks for retail and manufacturing sectors are significantly different, the adoption drivers, benefits, supply chain activities, and challenges are similar.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords

Abstract: We investigate the "generalized second-price" (GSP) auction, a new mechanism used by search engines to sell online advertising. Although GSP looks similar to the Vickrey-Clarke-Groves (VCG) mechanism, its properties are very different. Unlike the VCG mechanism, GSP generally does not have an equilibrium in dominant strategies, and truth-telling is not an equilibrium of GSP. To analyze the properties of GSP, we describe the generalized English auction that corresponds to GSP and show that it has a unique equilibrium. This is an ex post equilibrium, with the same payoffs to all players as the dominant strategy equilibrium of VCG.

Models of man

Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading models of man as one of the reading material to finish quickly.

Evaluating replicability of laboratory experiments in economics

Abstract: The replicability of some scientific findings has recently been called into question. To contribute data about replicability in economics, we replicated 18 studies published in the American Economic Review and the Quarterly Journal of Economics between 2011 and 2014. All of these replications followed predefined analysis plans that were made publicly available beforehand, and they all have a statistical power of at least 90% to detect the original effect size at the 5% significance level. We found a significant effect in the same direction as in the original study for 11 replications (61%); on average, the replicated effect size is 66% of the original. The replicability rate varies between 67% and 78% for four additional replicability indicators, including a prediction market measure of peer beliefs.