H.3.8. Natural Language Processing
Mozhgan Akaberi; Maryam Khodabakhsh; Seyedehfatemeh Karimi; Hoda Mashayekhi
Abstract
The exponential growth of digital information has increased the demand for robust and efficient Information Retrieval (IR) systems. Query Performance Prediction (QPP) is a critical task for identifying difficult queries and enhancing retrieval strategies. However, existing QPP methods suffer from several ...
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The exponential growth of digital information has increased the demand for robust and efficient Information Retrieval (IR) systems. Query Performance Prediction (QPP) is a critical task for identifying difficult queries and enhancing retrieval strategies. However, existing QPP methods suffer from several limitations: (1) score-based approaches fail to capture the structural relationships among retrieved documents, (2) supervised methods require labeled training data, making them costly and impractical for new domains, and (3) unsupervised post-retrieval predictors often rely solely on retrieval score dispersion, neglecting document clustering effects. To address these challenges, we propose a novel clustering-based post-retrieval QPP method. Specifically, we introduce three unsupervised predictors: Clustered Distinction, which measures query-specific separability of retrieved clusters; Clustered Query Drift, which estimates the deviation of top-ranked documents from query intent; and a hybrid approach combining both. By analyzing the clustering structure of retrieved documents, our method improves interpretability while eliminating the need for labeled data. We evaluate our approach on three standard datasets: the large-scale MS MARCO Passage Ranking dataset, TREC DL 2019, and TREC DL 2020. Experimental results demonstrate that our method significantly outperforms state-of-the-art score-based QPP models. These findings highlight the potential of cluster-aware QPP for enhancing IR systems and reducing the impact of difficult queries.
M. Rahimi; A. A. Taheri; H. Mashayekhi
Abstract
Finding an effective way to combine the base learners is an essential part of constructing a heterogeneous ensemble of classifiers. In this paper, we propose a framework for heterogeneous ensembles, which investigates using an artificial neural network to learn a nonlinear combination of the base classifiers. ...
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Finding an effective way to combine the base learners is an essential part of constructing a heterogeneous ensemble of classifiers. In this paper, we propose a framework for heterogeneous ensembles, which investigates using an artificial neural network to learn a nonlinear combination of the base classifiers. In the proposed framework, a set of heterogeneous classifiers are stacked to produce the first-level outputs. Then these outputs are augmented using several combination functions to construct the inputs of the second-level classifier. We conduct a set of extensive experiments on 121 datasets and compare the proposed method with other established and state-of-the-art heterogeneous methods. The results demonstrate that the proposed scheme outperforms many heterogeneous ensembles, and is superior compared to singly tuned classifiers. The proposed method is also compared to several homogeneous ensembles and performs notably better. Our findings suggest that the improvements are even more significant on larger datasets.
H.3.12. Distributed Artificial Intelligence
Z. Amiri; A. Pouyan; H Mashayekhi
Abstract
Recently, data collection from seabed by means of underwater wireless sensor networks (UWSN) has attracted considerable attention. Autonomous underwater vehicles (AUVs) are increasingly used as UWSNs in underwater missions. Events and environmental parameters in underwater regions have a stochastic nature. ...
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Recently, data collection from seabed by means of underwater wireless sensor networks (UWSN) has attracted considerable attention. Autonomous underwater vehicles (AUVs) are increasingly used as UWSNs in underwater missions. Events and environmental parameters in underwater regions have a stochastic nature. The target area must be covered by sensors to observe and report events. A ‘topology control algorithm’ characterizes how well a sensing field is monitored and how well pairs of sensors are mutually connected in UWSNs. It is prohibitive to use a central controller to guide AUVs’ behavior due to ever changing, unknown environmental conditions, limited bandwidth and lossy communication media. In this research, a completely decentralized three-dimensional topology control algorithm for AUVs is proposed. It is aimed at achieving maximal coverage of the target area. The algorithm enables AUVs to autonomously decide on and adjust their speed and direction based on the information collected from their neighbors. Each AUV selects the best movement at each step by independently executing a Particle Swarm Optimization (PSO) algorithm. In the fitness function, the global average neighborhood degree is used as the upper limit of the number of neighbors of each AUV. Experimental results show that limiting number of neighbors for each AUV can lead to more uniform network topologies with larger coverage. It is further shown that the proposed algorithm is more efficient in terms of major network parameters such as target area coverage, deployment time, and average travelled distance by the AUVs.
