Bhaskar Gautam

Lei Sun, Andrea Alfarano, Peiqi Duan et al.

This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring, with performance quantitatively assessed using Peak Signal-to-Noise Ratio (PSNR). Notably, there are no restrictions on computational complexity or model size. The task focuses on leveraging both events and images as inputs for single-image deblurring. A total of 199 participants registered, among whom 15 teams successfully submitted valid results, offering valuable insights into the current state of event-based image deblurring. We anticipate that this challenge will drive further advancements in event-based vision research.

B. Gautam, O. Ramos Terrades, J. M. Pujades et al.

Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Record linkage advance is key in these disciplines since it allows to increase the volume and the data complexity to be analyzed. However, current methods are constrained to link data coming from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner. In this paper we propose the knowledge graph use to tackle record linkage task. The proposed method, named {\bf WERL}, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results.

Bhaskar Gautam, Annappa Basava, Abhishek Singh et al.

The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach.

Bhaskar Gautam, Annappa Basava

Online social microblogging platforms including Twitter are increasingly used for aiding relief operations during disaster events. During most of the calamities that can be natural disasters or even armed attacks, non-governmental organizations look for critical information about resources to support effected people. Despite the recent advancement of natural language processing with deep neural networks, retrieval and ranking of short text becomes a challenging task because a lot of conversational and sympathy content merged with the critical information. In this paper, we address the problem of categorical information retrieval and ranking of most relevance information while considering the presence of short-text and multilingual languages that arise during such events. Our proposed model is based on the formation of embedding vector with the help of textual and statistical preprocessing, and finally, entire training 2,100,000 vectors were normalized using feed-forward neural network for need and availability tweets. Another important contribution of this paper lies in novel weighted Ranking Key algorithm based on top five general terms to rank the classified tweets in most relevance with classification. Lastly, we test our model on Nepal Earthquake dataset (contains short text and multilingual language tweets) and achieved 6.81% of mean average precision on 5,250,000 unlabeled embedding vectors of disaster relief tweets.