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Over the last decade, similar to other application domains, social media content has been proven very effective in disaster informatics. However, due to the unstructured nature of the data, several challenges are associated with disaster analysis in social media content. To fully explore the potential of social media content in disaster informatics, access to relevant content and the correct geo-location information is very critical. In this paper, we propose a three-step solution to tackling these challenges. Firstly, the proposed solution aims to classify social media posts into relevant and irrelevant posts followed by the automatic extraction of location information from the posts' text through Named Entity Recognition (NER) analysis. Finally, to quickly analyze the topics covered in large volumes of social media posts, we perform topic modeling resulting in a list of top keywords, that highlight the issues discussed in the tweet. For the Relevant Classification of Twitter Posts (RCTP), we proposed a merit-based fusion framework combining the capabilities of four different models namely BERT, RoBERTa, Distil BERT, and ALBERT obtaining the highest F1-score of 0.933 on a benchmark dataset. For the Location Extraction from Twitter Text (LETT), we evaluated four models namely BERT, RoBERTa, Distil BERTA, and Electra in an NER framework obtaining the highest F1-score of 0.960. For topic modeling, we used the BERTopic library to discover the hidden topic patterns in the relevant tweets. The experimental results of all the components of the proposed end-to-end solution are very encouraging and hint at the potential of social media content and NLP in disaster management., Comment: 15 pages; 4 tables; 4 figures

In recent years, the increasing use of Artificial Intelligence based text generation tools has posed new challenges in document provenance, authentication, and authorship detection. However, advancements in stylometry have provided opportunities for automatic authorship and author change detection in multi-authored documents using style analysis techniques. Style analysis can serve as a primary step toward document provenance and authentication through authorship detection. This paper investigates three key tasks of style analysis: (i) classification of single and multi-authored documents, (ii) single change detection, which involves identifying the point where the author switches, and (iii) multiple author-switching detection in multi-authored documents. We formulate all three tasks as classification problems and propose a merit-based fusion framework that integrates several state-of-the-art natural language processing (NLP) algorithms and weight optimization techniques. We also explore the potential of special characters, which are typically removed during pre-processing in NLP applications, on the performance of the proposed methods for these tasks by conducting extensive experiments on both cleaned and raw datasets. Experimental results demonstrate significant improvements over existing solutions for all three tasks on a benchmark dataset., Comment: Tables 7, pages 4

Nanobodies (Nb) are monomeric heavy-chain fragments derived from heavy-chain only antibodies naturally found in Camelids and Sharks. Their considerably small size (~3-4 nm; 13 kDa) and favorable biophysical properties make them attractive targets for recombinant production. Furthermore, their unique ability to bind selectively to specific antigens, such as toxins, chemicals, bacteria, and viruses, makes them powerful tools in cell biology, structural biology, medical diagnostics, and future therapeutic agents in treating cancer and other serious illnesses. However, a critical challenge in nanobodies production is the unavailability of nanobodies for a majority of antigens. Although some computational methods have been proposed to screen potential nanobodies for given target antigens, their practical application is highly restricted due to their reliance on 3D structures. Moreover, predicting nanobodyantigen interactions (binding) is a time-consuming and labor-intensive task. This study aims to develop a machine-learning method to predict Nanobody-Antigen binding solely based on the sequence data. We curated a comprehensive dataset of Nanobody-Antigen binding and nonbinding data and devised an embedding method based on gapped k-mers to predict binding based only on sequences of nanobody and antigen. Our approach achieves up to 90% accuracy in binding prediction and is significantly more efficient compared to the widely-used computational docking technique.

Cricket is the second most popular sport after soccer in terms of viewership. However, the assessment of individual player performance, a fundamental task in team sports, is currently primarily based on aggregate performance statistics, including average runs and wickets taken. We propose Context-Aware Metric of player Performance, CAMP, to quantify individual players' contributions toward a cricket match outcome. CAMP employs data mining methods and enables effective data-driven decision-making for selection and drafting, coaching and training, team line-ups, and strategy development. CAMP incorporates the exact context of performance, such as opponents' strengths and specific circumstances of games, such as pressure situations. We empirically evaluate CAMP on data of limited-over cricket matches between 2001 and 2019. In every match, a committee of experts declares one player as the best player, called Man of the M}atch (MoM). The top two rated players by CAMP match with MoM in 83\% of the 961 games. Thus, the CAMP rating of the best player closely matches that of the domain experts. By this measure, CAMP significantly outperforms the current best-known players' contribution measure based on the Duckworth-Lewis-Stern (DLS) method.

Parallel I/O refers to the ability of scientific programs to concurrently read/write from/to a single file from multiple processes executing on distributed memory platforms like compute clusters. In the HPC world, I/O becomes a significant bottleneck for many real-world scientific applications. In the last two decades, there has been significant research in improving the performance of I/O operations in scientific computing for traditional languages including C, C++, and Fortran. As a result of this, several mature and high-performance libraries including ROMIO (implementation of MPI-IO), parallel HDF5, Parallel I/O (PIO), and parallel netCDF are available today that provide efficient I/O for scientific applications. However, there is very little research done to evaluate and improve I/O performance of Java-based HPC applications. The main hindrance in the development of efficient parallel I/O Java libraries is the lack of a standard API (something equivalent to MPI-IO). Some adhoc solutions have been developed and used in proprietary applications, but there is no general-purpose solution that can be used by performance hungry applications. As part of this project, we plan to develop a Java-based parallel I/O API inspired by the MPI-IO bindings (MPI 2.0 standard document) for C, C++, and Fortran. Once the Java equivalent API of MPI-IO has been developed, we will develop a reference implementation on top of existing Java messaging libraries. Later, we will evaluate and compare performance of our reference Java Parallel I/O library with C/C++ counterparts using benchmarks and real-world applications., Comment: 10 pages

Style analysis, which is relatively a less explored topic, enables several interesting applications. For instance, it allows authors to adjust their writing style to produce a more coherent document in collaboration. Similarly, style analysis can also be used for document provenance and authentication as a primary step. In this paper, we propose an ensemble-based text-processing framework for the classification of single and multi-authored documents, which is one of the key tasks in style analysis. The proposed framework incorporates several state-of-the-art text classification algorithms including classical Machine Learning (ML) algorithms, transformers, and deep learning algorithms both individually and in merit-based late fusion. For the merit-based late fusion, we employed several weight optimization and selection methods to assign merit-based weights to the individual text classification algorithms. We also analyze the impact of the characters on the task that are usually excluded in NLP applications during pre-processing by conducting experiments on both clean and un-clean data. The proposed framework is evaluated on a large-scale benchmark dataset, significantly improving performance over the existing solutions., Comment: 7 pages; 3 tables; 1 figure

Anomalous sound detection for machine condition monitoring has great potential in the development of Industry 4.0. However, these anomalous sounds of machines are usually unavailable in normal conditions. Therefore, the models employed have to learn acoustic representations with normal sounds for training, and detect anomalous sounds while testing. In this article, we propose a self-supervised dual-path Transformer (SSDPT) network to detect anomalous sounds in machine monitoring. The SSDPT network splits the acoustic features into segments and employs several DPT blocks for time and frequency modeling. DPT blocks use attention modules to alternately model the interactive information about the frequency and temporal components of the segmented acoustic features. To address the problem of lack of anomalous sound, we adopt a self-supervised learning approach to train the network with normal sound. Specifically, this approach randomly masks and reconstructs the acoustic features, and jointly classifies machine identity information to improve the performance of anomalous sound detection. We evaluated our method on the DCASE2021 task2 dataset. The experimental results show that the SSDPT network achieves a significant increase in the harmonic mean AUC score, in comparison to present state-of-the-art methods of anomalous sound detection.

Environmental sound recognition (ESR) is an emerging research topic in audio pattern recognition. Many tasks are presented to resort to computational models for ESR in real-life applications. However, current models are usually designed for individual tasks, and are not robust and applicable to other tasks. Cross-task models, which promote unified knowledge modeling across various tasks, have not been thoroughly investigated. In this article, we propose a cross-task model for three different tasks of ESR: 1) acoustic scene classification; 2) urban sound tagging; and 3) anomalous sound detection. An architecture named SE-Trans is presented that uses attention mechanism-based Squeeze-and-Excitation and Transformer encoder modules to learn the channelwise relationship and temporal dependencies of the acoustic features. FMix is employed as the data augmentation method that improves the performance of ESR. Evaluations for the three tasks are conducted on the recent databases of detection and classification of acoustic scenes and event challenges. The experimental results show that the proposed cross-task model achieves state-of-the-art performance on all tasks. Further analysis demonstrates that the proposed cross-task model can effectively utilize acoustic knowledge across different ESR tasks.

This paper focuses on an important environmental challenge; namely, water quality by analyzing the potential of social media as an immediate source of feedback. The main goal of the work is to automatically analyze and retrieve social media posts relevant to water quality with particular attention to posts describing different aspects of water quality, such as watercolor, smell, taste, and related illnesses. To this aim, we propose a novel framework incorporating different preprocessing, data augmentation, and classification techniques. In total, three different Neural Networks (NNs) architectures, namely (i) Bidirectional Encoder Representations from Transformers (BERT), (ii) Robustly Optimized BERT Pre-training Approach (XLM-RoBERTa), and (iii) custom Long short-term memory (LSTM) model, are employed in a merit-based fusion scheme. For merit-based weight assignment to the models, several optimization and search techniques are compared including a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA), Brute Force (BF), Nelder-Mead, and Powell's optimization methods. We also provide an evaluation of the individual models where the highest F1-score of 0.81 is obtained with the BERT model. In merit-based fusion, overall better results are obtained with BF achieving an F1-score score of 0.852. We also provide comparison against existing methods, where a significant improvement for our proposed solutions is obtained. We believe such rigorous analysis of this relatively new topic will provide a baseline for future research., Comment: 10 pages, 2 figures, 8 tables

This paper presents our contributions to the MediaEval 2021 task namely "WaterMM: Water Quality in Social Multimedia". The task aims at analyzing social media posts relevant to water quality with particular focus on the aspects like watercolor, smell, taste, and related illnesses. To this aim, a multimodal dataset containing both textual and visual information along with meta-data is provided. Considering the quality and quantity of available content, we mainly focus on textual information by employing three different models individually and jointly in a late-fusion manner. These models include (i) Bidirectional Encoder Representations from Transformers (BERT), (ii) Robustly Optimized BERT Pre-training Approach (XLM-RoBERTa), and a (iii) custom Long short-term memory (LSTM) model obtaining an overall F1-score of 0.794, 0.717, 0.663 on the official test set, respectively. In the fusion scheme, all the models are treated equally and no significant improvement is observed in the performance over the best performing individual model., Comment: 3 pages, 2 tables, 1 figure

This paper presents a solutions for the MediaEval 2021 task namely "Visual Sentiment Analysis: A Natural Disaster Use-case". The task aims to extract and classify sentiments perceived by viewers and the emotional message conveyed by natural disaster-related images shared on social media. The task is composed of three sub-tasks including, one single label multi-class image classification task, and, two multi-label multi-class image classification tasks, with different sets of labels. In our proposed solutions, we rely mainly on two different state-of-the-art models namely, Inception-v3 and VggNet-19, pre-trained on ImageNet, which are fine-tuned for each of the three task using different strategies. Overall encouraging results are obtained on all the three tasks. On the single-label classification task (i.e. Task 1), we obtained the weighted average F1-scores of 0.540 and 0.526 for the Inception-v3 and VggNet-19 based solutions, respectively. On the multi-label classification i.e., Task 2 and Task 3, the weighted F1-score of our Inception-v3 based solutions was 0.572 and 0.516, respectively. Similarly, the weighted F1-score of our VggNet-19 based solution on Task 2 and Task 3 was 0.584 and 0.495, respectively., Comment: 3 pages