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The first international workshop on Neuro-Physiological (NP) Methods in Information Retrieval Research took place at SIGIR 2015 in Santiago, Chile on August 13, 2015. The workshop included a mixture of short introductions to selected NP methods, paper presentations and a panel discussion by invited scholars. Each session included significant time allotted to open discussions among the participants. The high attendance and the lively discussions demonstrated significant interest in the use of NP methods in information retrieval research. The panel and discussions pointed out many advantages of using these methods as a data source complementary to traditional measures but also potentially offering deeper insights into humans interacting with information. Significant challenges, such as the lack of baseline data and reference tasks, and a need to collaborate with neuroscientists, were also noted. Overall, the NP-methods were found to be very promising and worth continued research efforts. [ABSTRACT FROM AUTHOR]

We proposed and implemented a novel clustering algorithm called LAIR2, which has constant running time average for on-the-fly Scatter/Gather browsing. Our experiments showed that when running on a single processor, the LAIR2 on-line clustering algorithm was several hundred times faster than a parallel Buckshot algorithm running on multiple processors. This paper reports on a study that examined the effectiveness of the LAIR2 algorithm in terms of clustering quality and its impact on retrieval performance. We conducted a user study on 24 subjects to evaluate on-the-fly LAIR2 clustering in Scatter/Gather search tasks by comparing its performance to the Buckshot algorithm, a classic method for Scatter/Gather browsing. Results showed significant differences in terms of subjective perceptions of clustering quality. Subjects perceived that the LAIR2 algorithm produced significantly better quality clusters than the Buckshot method did. Subjects felt that it took less effort to complete the tasks with the LAIR2 system, which was more effective in helping them in the tasks. Interesting patterns also emerged from subjects' comments in the final open-ended questionnaire. We discuss implications and future research. [ABSTRACT FROM AUTHOR]

Agentbased simulation is a popular technology for studying social and information systems. Information visualization in such simulations is potentially useful for communicating real time information but involves several levels of challenges. It remains unclear what patterns of agent activities are useful for visualization. To motivate and investigate potentially useful patterns for visualization, we identified four factors: 1 Agent Involvement; 2 Dominant Player; 3 Learning and Adaptation; and 4 Influence of Task and Content Stream. To demonstrate the usefulness of the factors, we operationalized them to permit analysis and visualization in the context of a problem involving coordination among agents conducting document classification.

Service integration is important in digital library implementation. In this paper, we present a project called Extensible Networked Associationbased Bioinformatics Learning Environment, or ENABLE. It computerizes a cataloging workflow by integrating backend services and facilitates interleaving between automatic cataloging & human curation activities. To the user end, it provides various interaction means that support bioinformatics learning. Features of the current implementation include automatic bioinformatics resource harvesting, basic information retrieval operations, resource cataloging and management, visualization and learning tools.

As a type of relevance feedback, ScatterGather demonstrates an interactive approach to relevance mapping and reinforcement. The ScatterGather model, proposed by Cutting, Karger, Pedersen, and Tukey 1992, is well known for its effectiveness in situations where it is difficult to precisely specify a query. However, online clustering on a large data corpus is computationally complex and extremely time consuming. This has prohibited the methods real world application for responsive services. In this paper, we proposed and evaluated a new clustering algorithm called LAIR2, which has linear worstcase time complexity and constant running time average for ScatterGather browsing. Our experiment showed when running on a single processor, the LAIR2 online clustering algorithm is several hundred times faster than a classic parallel algorithm running on multiple processors. The efficiency of the LAIR2 algorithm promises realtime ScatterGather browsing services. We have implemented an online visualization prototype, namely, LAIR2 ScatterGather browser, to demonstrate its utility and usability.

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This paper describes an application of IR and text categorization methods to a highly practical problem in biomedicine, specifically, Gene Ontology (GO) annotation. GO annotation is a major activity in most model organism database projects and annotates gene functions using a controlled vocabulary. As a first step toward automatic GO annotation we aim to assign GO domain codes given a specific gene and an article in which the gene appears, which is one of the task challenges at the TREC 2004 Genomics Track. We approached the task with careful consideration of the specialized terminology and paid special attention to dealing with various forms of gene synonyms, so as to exhaustively locate the occurrences of the target gene. We extracted the words around the gene occurrences and used them to represent the gene for GO domain code annotation. As a classifier, we adopted a variant of k-Nearest Neighbor (kNN) with supervised term weighting schemes to improve the performance, making our method among the top-performing systems in the TREC official evaluation. Moreover, it is demonstrated that our proposed framework is successfully applied to another task of the Genomics Track, showing comparable results to the best performing system. [ABSTRACT FROM AUTHOR]

In this paper, we describe the COBioSIFTER project that aims at constructing a framework for managing information from the biological domain. It uses a distributed multi-agent approach based on the CORBA middleware and contains an architecture made up of functionally heterogeneous agents. A series of experiments has been carried out with a prototype of COBioSIFTER to illustrate its advantages such as high performance, scalability, heterogeneity, and fault tolerance. The results of these experiments demonstrate the effectiveness of such an agent-based biological information framework.

Modeling users in information filtering systems is a difficult challenge due to dimensions such as nature, scope, and variability of interests. Numerous machine-learning approaches have been proposed for user modeling in filtering systems. The focus has been primarily on techniques for user model capture and representation, with relatively simple assumptions made about the type of users' interests. Although many studies claim to deal with “adaptive” techniques and thus they pay heed to the fact that different types of interests must be modeled or even changes in interests have to be captured, few studies have actually focused on the dynamic nature and the variability of user-interests and their impact on the modeling process. A simulation based information filtering environment called SIMSFITER was developed to overcome some of the barriers associated with conducting studies on user-oriented factors that can impact interests. SIMSIFTER implemented a user modeling approach known as reinforcement learning that has proven to be effective in previous filtering studies involving humans. This paper reports on several studies conducted using SIMSIFTER that examined the impact of key dimensions such as type of interests, rate of change of interests and level of user-involvement on modeling accuracy and ultimately on filtering effectiveness.