Visual Object Tracking (VOT) remains challenging under adverse conditions such as illumination variations, motion blur, fast object dynamics, and background clutter. RGB-based trackers often degrade in low light or rapid motion due to reliance on appearance cues, while event-based trackers are robust to fast motion but lack semantic and contextual information for disambiguating targets in cluttered scenes. We propose RELTrack, a multi-modal tracker that integrates RGB, Event, and Language in a unified framework. RELTrack uses a three-level captioning strategy (object-, scene-, and video-level) to provide hierarchical textual guidance and a multi-branch fusion module to align and integrate RGB, event streams, and language embeddings for robust target localization. Experiments on four RGB–Event benchmarks, VisEvent (820 sequences), FE108 (108 sequences), COESOT (1354 videos), and FELT (742 videos), show that RELTrack achieves 63.1/80.5/73.3 (SR/PR/NPR) on VisEvent, 61.69/89.30 (SR/PR) on FE108, 66.7/79.3/77.6 (SR/PR/NPR) on COESOT, and 61.3/71.2/68.9 (SR/PR/NPR) on FELT, demonstrating state-of-the-art performance across datasets. Code and models are publicly available at: https://github.com/XXXXX/RELTrack .

Research in crash severity prediction is necessary to allow safety planners to take precautionary measures and enable first aiders to remain prepared for assisting the injured. Existing literature in the field of crash severity prediction is mostly focused on generating the attributes for predicting the severity. However, in reality, not all features are discriminating, and certain classes are challenging to detect even employing the entire feature set. Although to tackle these problems several techniques are developed in the Machine Learning (ML) literature. But their application to crash severity prediction and an optimal strategy for the best combination of features or classifiers for achieving high accuracy is a less studied area. To address these problems, this work first provides a comparison of widely used classifiers for predicting crash severity; and secondly, by combining class-wise majority voting with One-vs-Rest (OvR) approach, a novel classification framework named, OvR consensus learning (OvRCL) is proposed. The proposed method avail a feature selection technique, Mutual information (MI), to acquire the most relevant feature set regarding the output class (i.e. severity). Moreover, to differentiate each class in the multi-class data, OvRCL iteratively runs ML algorithms as binary classifiers in an ensemble framework to significantly ameliorate classification performance. In our experiments, we use four classifiers, namely, the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Random Forest (RF), and Bagging classifier, to get the consensus. Analysis was done using a real crash dataset obtained from an open data source of Leeds city council. A four-year crash data (2015–2018) is used for training and the OvRCL is tested on the 2019 data. Moreover, to validate the performance of OvRCL, this study also utilizes two more datasets with high-class imbalance. In contrast to conventional ML algorithms, our experiments depict that the OvRCL is a potent method for forecasting crash severity levels on the data under test.

Gene Network Graphs (GNGs) are comprised of biomedical data. Deriving structural information from these graphs remains a prime area of research in the domain of biomedical and health informatics. In this paper, we propose Gene Vectors Embodied Deep Attentional Factorization Machines (GDFMs) for the gene to gene interaction prediction. We first initialize GDFM with vector embeddings learned from gene locality configuration and an expression equivalence criterion that preserves their innate similar traits. GDFM uses an attention-based mechanism that manipulates different positions, to learn the representation of sequence, before calculating the pairwise factorized interactions. We further use hidden layers, batch normalization, and dropout to stabilize the performance of our deep structured architecture. An extensive comparison with several state-of-the-art approaches, using Ecoli and Yeast datasets for gene-gene interaction prediction shows the significance of our proposed framework.

Co-clustering has been defined as a way to organize simultaneously subsets of instances and subsets of features in order to improve the clustering of both of them. In previous work, we proposed an efficient co-similarity measure allowing to simultaneously compute two similarity matrices between objects and features, each built on the basis of the other. Here we propose a generalization of this approach by introducing a notion of pseudo-norm and a pruning algorithm. Our experiments show that this new algorithm significantly improves the accuracy of the results when using either supervised or unsupervised feature selection data and that it outperforms other algorithms on various corpora.

In this paper, we propose an extension of the χ-Sim co-clustering algorithm to deal with the text categorization task. The idea behind χ-Sim method [1] is to iteratively learn the similarity matrix between documents using similarity matrix between words and vice-versa. Thus, two documents are said to be similar if they share similar (but not necessary identical) words and two words are similar if they occur in similar documents. The algorithm has been shown to work well for unsupervised document clustering. By introducing some “a priori” knowledge about the class labels of documents in the initialization step of χ-Sim, we are able to extend the method to deal for the supervised task. The proposed approach is tested on different classical textual datasets and our experiments show that the proposed algorithm compares favorably or surpass both traditional and state-of-the-art algorithms like k-NN, supervised LSI and SVM.

Co-clustering has been widely studied in recent years. Exploiting the duality between objects and features efficiently helps in better clustering both objects and features. In contrast with current co-clustering algorithms that focus on directly finding some patterns in the data matrix, in this paper we define a (co-)similarity measure, named X-Sim, which iteratively computes the similarity between objects and their features. Thus, it becomes possible to use any clustering methods (k-means, ...) to co-cluster data. The experiments show that our algorithm not only outperforms the classical similarity measure but also outperforms some co-clustering algorithms on the document-clustering task.