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.
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.
This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.
In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations. Several interesting works and case studies of disaster analytics exploring different aspects of natural disasters have been already conducted. Along with the great potential, disaster analytics comes with several challenges mainly due to the nature of social media content. In this paper, we explore one such challenge and propose a text classification framework to deal with Twitter noisy data. More specifically, we employed several transformers both individually and in combination, so as to differentiate between relevant and non-relevant Twitter posts, achieving the highest F1-score of 0.87.
The recent advancement in Multimedia Analytical, Computer Vision (CV), and Artificial Intelligence (AI) algorithms resulted in several interesting tools allowing an automatic analysis and retrieval of multimedia content of users' interests. However, retrieving the content of interest generally involves analysis and extraction of semantic features, such as emotions and interestingness-level. The extraction of such meaningful information is a complex task and generally, the performance of individual algorithms is very low. One way to enhance the performance of the individual algorithms is to combine the predictive capabilities of multiple algorithms using fusion schemes. This allows the individual algorithms to complement each other, leading to improved performance. This paper proposes several fusion methods for the media interestingness score prediction task introduced in CLEF Fusion 2022. The proposed methods include both a naive fusion scheme, where all the inducers are treated equally and a merit-based fusion scheme where multiple weight optimization methods are employed to assign weights to the individual inducers. In total, we used six optimization methods including a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA), Nelder Mead, Trust Region Constrained (TRC), and Limited-memory Broyden Fletcher Goldfarb Shanno Algorithm (LBFGSA), and Truncated Newton Algorithm (TNA). Overall better results are obtained with PSO and TNA achieving 0.109 mean average precision at 10. The task is complex and generally, scores are low. We believe the presented analysis will provide a baseline for future research in the domain.
Prediction of a machine's Remaining Useful Life (RUL) is one of the key tasks in predictive maintenance. The task is treated as a regression problem where Machine Learning (ML) algorithms are used to predict the RUL of machine components. These ML algorithms are generally used as a black box with a total focus on the performance without identifying the potential causes behind the algorithms' decisions and their working mechanism. We believe, the performance (in terms of Mean Squared Error (MSE), etc.,) alone is not enough to build the trust of the stakeholders in ML prediction rather more insights on the causes behind the predictions are needed. To this aim, in this paper, we explore the potential of Explainable AI (XAI) techniques by proposing an explainable regression framework for the prediction of machines' RUL. We also evaluate several ML algorithms including classical and Neural Networks (NNs) based solutions for the task. For the explanations, we rely on two model agnostic XAI methods namely Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). We believe, this work will provide a baseline for future research in the domain.
Federated Learning (FL) is one of the hot research topics, and it utilizes Machine Learning (ML) in a distributed manner without directly accessing private data on clients. However, FL faces many challenges, including the difficulty to obtain high accuracy, high communication cost between clients and the server, and security attacks related to adversarial ML. To tackle these three challenges, we propose an FL algorithm inspired by evolutionary techniques. The proposed algorithm groups clients randomly in many clusters, each with a model selected randomly to explore the performance of different models. The clusters are then trained in a repetitive process where the worst performing cluster is removed in each iteration until one cluster remains. In each iteration, some clients are expelled from clusters either due to using poisoned data or low performance. The surviving clients are exploited in the next iteration. The remaining cluster with surviving clients is then used for training the best FL model (i.e., remaining FL model). Communication cost is reduced since fewer clients are used in the final training of the FL model. To evaluate the performance of the proposed algorithm, we conduct a number of experiments using FEMNIST dataset and compare the result against the random FL algorithm. The experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of accuracy, communication cost, and security.
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.
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.