Description: This paper discusses non-preemptive, real-time scheduling. Abstract: Traditional real-time systems are designed using preemptive scheduling and worst-case execution time estimates to guarantee the execution of high priority tasks. There is, however, an interest in exploring non-preemptive scheduling models for real-time systems, particularly for soft real-time multimedia applications. In this paper, we propose a new algorithm that uses multiple scheduling strategies. The goal of this research is to improve the success rate of the well-known Earliest Deadline First (EDF) approach even when the load on the system is very high. The approach, known as group-EDF (gEDF) is based on (dynamic) grouping of tasks with deadlines that are very close to each other, and using Shortest Job First (SJF) technique to schedule tasks within the group. We present results comparing gEDF and EDF using randomly generated tasks with varying execution times, release times, deadlines and tolerance to missing deadlines, under varying workloads. We believe that the grouping of tasks with similar deadlines and utilizing information other than deadlines (such as execution times, priorities or resource availability) for scheduling tasks within a group can lead to new and more efficient real-time scheduling algorithms.

Description: This paper discusses enhanced channel assignment and load distribution in IEEE 802.11 WLANs. Abstract: An algorithm to reduce congestion and balance users' load in IEEE 802.11b/g wireless local area networks (WLANs) is presented, which takes into account overlapping channel interference between access points (APs) and the signal-to-interference ratio (SIR) experienced by the users. After finding the best channel assignment at the APs, the algorithm then finds the most congested access point (MCAP). It reexamines the users' association with APS by minimizing the congestion at the MCAP. Simulation results show that the proposed algorithm is capable of significantly reducing the overall congestion in the WLAN while mitigating channel interference. Our algorithm has also been shown to be scalable and it performs well for networks of different topologies.

Description: This paper discusses an evaluation exercise for word alignment. Abstract: This paper presents the task definition, resources, participating systems, and comparative results for the shared task on word alignment, which was organized as part of the HLT/NAACL 2003 Workshop on Building and Using Parallel Texts. The shared task included Romanian-English and English-French sub-tasks, and drew the participation of seven teams from around the world.

Description: This paper discusses explorations in automatic book summarization. Abstract: Most of the text summarization research carried out to date has been concerned with the summarization of short documents (e.g., news stories, technical reports), and very little work if any has been done on the summarization of very long documents. In this paper, we try to address this gap and explore the problem of book summarization. We introduce a new data set specifically designed for the evaluation of systems for book summarization, and describe summarization techniques that explicitly account for the length of the documents.

Description: This paper discusses FALCON. Abstract: This paper presents the features of FALCON, an answer engine that integrates different forms of syntactic, semantic and pragmatic knowledge for the goal of achieving better performance. The answer engine handles question reformulations, finds the expected answer type from a large hierarchy that incorporates the WordNet semantic net and extracts answers after performing unifications on the semantic forms of the question and its candidate answers. To rule out erroneous answers, it provides justification option, implemented as an abductive proof. In TREC-9, FALCON generated a score of 58% for short answers and 76% for long answers.