Tracy Mullen

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

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 Comparative Analysis of RFID Adoption in Retail and Manufacturing Sectors

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.

Differential evolution for discrete optimization: An experimental study on Combinatorial Auction problems

Differential evolution (DE) mutates solution vectors by the weighted difference of other vectors using arithmetic operations. As these operations cannot be directly extended to discrete combinatorial space, DE algorithms have been traditionally applied to optimization problems where the search space is continuous. In this paper, we use JADE, a self-adaptive DE algorithm, for winner determination in combinatorial auctions (CAs) where users place bids on combinations of items. To adapt JADE to discrete optimization, we use a rank-based representation schema that produces only feasible solutions and a regeneration operation that constricts the problem search space. It is shown that JADE compares favorably to a local stochastic search algorithm, Casanova, and a genetic algorithm based approach, SGA.

An Approximate Algorithm for Resource Allocation Using Combinatorial Auctions

Combinatorial auctions (CAs), where users bid on combination of items, have emerged as a useful tool for resource allocation in distributed systems. However, two main difficulties exist to the adoption of CAs in time-constrained environments. The first difficulty involves the computational complexity of winner determination. The second difficulty entails the computational complexity of eliciting utility valuations for all possible combinations of resources to different tasks. To address both issues, we developed a new algorithm, seeded genetic algorithm (SGA) for finding high quality solutions quickly. SGA uses a novel representational schema that produces only feasible solutions. We compare the winner determination performance of our algorithm with Casanova, another local stochastic search procedure, on typically hard-to-solve bid distributions. We show that SGA converges to a better solution than Casanova for large problem sizes. However, for many bid distributions, exact winner determination using integer programming approaches is very fast, even for large problem sizes. In these cases, SGA can still provide significant time savings by eliminating the requirement for formulating all possible bids.

An in-depth analysis of information markets with aggregate uncertainty

The novel idea of setting up Internet-based virtual markets, information markets, to aggregate dispersed information and predict outcomes of uncertain future events has empirically found its way into many domains. But the theoretical examination of information markets has lagged relative to their implementation and use. This paper proposes a simple theoretical model of information markets to understand their information dynamics. We investigate and provide initial answers to a series of research questions that are important to understanding how information markets work, which are: (1) Does an information market converge to a consensus equilibrium? (2) If yes, how fast is the convergence process? (3) What is the best possible equilibrium of an information market? and (4) Is an information market guaranteed to converge to the best possible equilibrium?

Dynamic vision sensor camera based bare hand gesture recognition

This paper proposes a method to recognize bare hand gestures using a dynamic vision sensor (DVS) camera. Different from conventional cameras, DVS cameras only respond to pixels with temporal luminance differences, which can greatly reduce the computational cost of comparing consecutive frames to track moving objects. Due to differences in available information, conventional vision techniques for gesture recognition may not be directly applicable in DVS based applications. This paper attempts to classify three different hand gestures made by a player during rock-paper-scissors game. We propose novel methods to detect the point where the player delivers a throw, to extract hand regions, and to extract useful features for machine learning based classification. Preliminary results show that our method produces enhanced accuracy of hand gesture recognition.

RPD-based Hypothesis Reasoning for Cyber Situation Awareness

Intelligence workers such as analysts, commanders, and soldiers often need a hypothesis reasoning framework to gain improved situation awareness of the highly dynamic cyber space. The development of such a framework requires the integration of interdisciplinary techniques, including supports for distributed cognition (human-in-the-loop hypothesis generation), supports for team collaboration (identification of information for hypothesis evaluation), and supports for resource-constrained information collection (hypotheses competing for information collection resources). We here describe a cognitively-inspired framework that is built upon Klein’s recognition-primed decision model and integrates the three components of Endsley’s situation awareness model. The framework naturally connects the logic world of tools for cyber situation awareness with the mental world of human analysts, enabling the perception, comprehension, and prediction of cyber situations for better prevention, survival, and response to cyber attacks by adapting missions at the operational, tactical, and strategic levels.

A Delphi Study of RFID Applicable Business Processes and Value Chain Activities in Retail

For this research we use Delphi technique to identify the key business processes and value chain activities that are improved by RFID. Our Delphi study involves 74 experts from different domains such as consulting, retail, academia, and third party service providers. We also explored whether there is any difference in expert perceptions about RFID applicable business processes and value chain activities across different business associations.

PREDICTING UNCERTAIN OUTCOMES USING INFORMATION MARKETS: TRADER BEHAVIOR AND INFORMATION AGGREGATION

Forecasting seems to be a ubiquitous endeavor in human societies. In this paper, information markets are introduced as a promising mechanism for predicting uncertain outcomes. Information markets are markets that are specially designed for aggregating information and making predictions on future events. A generic model of information markets is proposed. We derive some fundamental properties on when information markets can converge to the direct communications equilibrium, which aggregates all information across traders and is the best possible prediction for the event under consideration. Information markets, if properly designed, have substantial potential to facilitate organizations in making better informed decisions.

MASM: a market architecture for sensor management in distributed sensor networks

Rapid developments in sensor technology and its applications have energized research efforts towards devising a firm theoretical foundation for sensor management. Ubiquitous sensing, wide bandwidth communications and distributed processing provide both opportunities and challenges for sensor and process control and optimization. Traditional optimization techniques do not have the ability to simultaneously consider the wildly non-commensurate measures involved in sensor management in a single optimization routine. Market-oriented programming provides a valuable and principled paradigm to designing systems to solve this dynamic and distributed resource allocation problem. We have modeled the sensor management scenario as a competitive market, wherein the sensor manager holds a combinatorial auction to sell the various items produced by the sensors and the communication channels. However, standard auction mechanisms have been found not to be directly applicable to the sensor management domain. For this purpose, we have developed a specialized market architecture MASM (Market architecture for Sensor Management). In MASM, the mission manager is responsible for deciding task allocations to the consumers and their corresponding budgets and the sensor manager is responsible for resource allocation to the various consumers. In addition to having a modified combinatorial winner determination algorithm, MASM has specialized sensor network modules that address commensurability issues between consumers and producers in the sensor network domain. A preliminary multi-sensor, multi-target simulation environment has been implemented to test the performance of the proposed system. MASM outperformed the information theoretic sensor manager in meeting the mission objectives in the simulation experiments.