Federated learning (FL) allows multiple medical institutions to collaboratively train machine learning models without sharing sensitive patient data, preserving privacy. However, as medical guidelines and disease classifications change over time, existing models can become outdated and may need updates to stay relevant. We propose a novel approach that efficiently updates federated models by selectively removing outdated knowledge without requiring full retraining. Our approach uses gradient-based Shapley value approximations to identify and modify the most important model parameters linked to obsolete diagnostic categories. This enables precise unlearning of outdated information while preserving performance on current diagnoses. We validate our method on the PathMNIST and COVID-19 Radiography datasets, showing that it can effectively eliminate specific diagnostic classes with minimal loss in accuracy for relevant conditions. Our method only requires a single communication round among clients and offers better control than previous techniques by targeting individual parameters instead of whole convolutional feature channels (in Convolutional Neural Networks (CNN)). This makes it especially useful for keeping federated medical models aligned with evolving medical knowledge.1

Social media platforms have become an open door for users to share their views, resulting in a growing trend of offensive content being shared on social media. Detecting and addressing offensive content is crucial due to its significant impact on society. Although there has been extensive research on the detection of offensive content in the English language, there is a notable gap in detecting offensive content in multimodal settings involving code-mixed languages. In this article, we propose a large scale multimodal code-mixed dataset for Hinglish (Hindi+English)MultiSEAO-Mixfocusing on women and children. TheMultiSEAO-Mixis annotated with offensiveness, sentiment, emotion, and their respective intensities. Additionally, it is also annotated with author support. A multimodal, multitask framework is proposed that considers offensive detection, intensity prediction, and author support as the primary tasks and improves their performance using sentiment, emotion, and corresponding intensities as the auxiliary tasks. Further, we propose a fusion technique that captures the enhanced multimodal representation to improve the performance of our model. Experimental results demonstrate that the proposed multitask framework improves the model performance by more than 4.5 points compared to multitask system without sentiment and emotion as the auxiliary tasks.

Code-mixing refers to switching between two or more languages within the same utterance, which is very prevalent in multilingual societies. The amount of code-mixed content has increased due to the spike in multilingual users on review platforms. Analyzing these reviews can be beneficial for both consumers and service providers. Aspect category (AC) sentiment analysis (ACSA) provides a fine-grained analysis of reviews. ACSA identifies the AC and measures the sentiment expressed toward a given AC. The research in this direction has mostly focused on monolingual languages, which are insufficient for analyzing code-mixed reviews. To expedite research in this direction, we propose new tasks in the code-mixed language (ACSA-Mix). We develop a benchmark setup to create a code-mixed Hinglish (i.e., mixing of Hindi and English) dataset for ACSA-Mix, annotated with AC and sentiment values. To demonstrate the practical usage of the dataset, we solve ACSA-Mix tasks in the Seq2Seq framework, where natural language sentences are generated to represent the outputs that allow pretrained language models to be used effectively. Further, a multilingual multitask joint learning framework is proposed that transfers knowledge between Hinglish (ACSA-Mix) and English (ACSA) tasks. We consider ACSA-Mix tasks the primary tasks and enhance their performance by ACSA tasks (auxiliary) by sharing knowledge between them. We observe improvement over the single task ACSA-Mix models.11The dataset has been made available onhttps://www.iitp.ac.in/ai-nlp-ml/resources.htmland at Github repository:https://github.com/20118/ACSA-Mix

With the growing popularity of code-mixed data, there is an increasing need for better handling of this type of data, which poses a number of challenges, such as dealing with spelling variations, multiple languages, different scripts, and a lack of resources.Current language models face difficulty in effectively handling code-mixed data as they primarily focus on the semantic representation of words and ignore the auditory phonetic features.This leads to difficulties in handling spelling variations in code-mixed text.In this paper, we propose an effective approach for creating language models for handling code-mixed textual data using auditory information of words from SOUNDEX.Our approach includes a pre-training step based on masked-language-modelling, which includes SOUNDEX representations (SAMLM) and a new method of providing input data to the pretrained model.Through experimentation on various code-mixed datasets (of different languages) for sentiment, offensive and aggression classification tasks, we establish that our novel language modeling approach (SAMLM) results in improved robustness towards adversarial attacks on code-mixed classification tasks.Additionally, our SAMLM based approach also results in better classification results over the popular baselines for code-mixed tasks.We use the explainability technique, SHAP (SHapley Additive exPlanations) to explain how the auditory features incorporated through SAMLM assist the model to handle the code-mixed text effectively and increase robustness against adversarial attacks 1 .

Social media platforms such as Twitter have evolved into a vast information sharing platform, allowing people from a variety of backgrounds and expertise to share their opinions on numerous events such as terrorism, narcotics and many other social issues. People sometimes misuse the power of social media for their agendas, such as illegal trades and negatively influencing others. Because of this, sentiment analysis has won the interest of a lot of researchers to widely analyze public opinion for social media monitoring. Several benchmark datasets for sentiment analysis across a range of domains have been made available, especially for high-resource languages. A few datasets are available for low-resource Indian languages like Hindi, such as movie reviews and product reviews, which do not address the current need for social media monitoring. In this paper, we address the challenges of sentiment analysis in Hindi and socially relevant domains by introducing a balanced corpus annotated with the sentiment classes, viz. positive, negative and neutral. To show the effective usage of the dataset, we build several deep learning based models and establish them as the baselines for further research in this direction.

With the advent of the 21st century, healthcare systems all around the world are facing challenges at an unprecedented scale. The recent covid-19 outbreak is a glaring example of such challenges. After facing such situations anyone can conclude that there is a need to change the way our current health system works and the recent blockchain technology emerged as a way to bring out this change. From universal health blueprint to medical supply chain management and connecting vetted suppliers, blockchain is making an impact. This paper introduces the concept of blockchain along with its applications with the main focus on healthcare and medical systems.

Deep learning has become most prominent in solving various Natural Language Processing (NLP) tasks including sentiment analysis. However, these techniques require a considerably large amount of annotated corpus, which is not easy to obtain for most of the languages, especially under the scenario of low-resource settings. In this article, we propose a deep multi-task multi-lingual adversarial framework to solve the resource-scarcity problem of sentiment analysis by leveraging the useful and relevant knowledge from a high-resource language. To transfer the knowledge between the different languages, both the languages are mapped to the shared semantic space using cross-lingual word embeddings. We evaluate our proposed architecture on a low-resource language, Hindi, using English as the high-resource language. Experiments show that our proposed model achieves an accuracy of 60.09% for the movie review dataset and 72.14% for the product review dataset. The effectiveness of our proposed approach is demonstrated with significant performance gains over the state-of-the-art systems and translation-based baselines.

Due to the phenomenal growth of online content in recent time, sentiment analysis has attracted attention of the researchers and developers. A number of benchmark annotated corpora are available for domains like movie reviews, product reviews, hotel reviews, etc. The pervasiveness of social media has also lead to a huge amount of content posted by users who are misusing the power of social media to spread false beliefs and to negatively influence others. This type of content is coming from the domains like terrorism, cybersecurity, technology, social issues, etc. Mining of opinions from these domains is important to create a socially intelligent system to provide security to the public and to maintain the law and order situations. To the best of our knowledge, there is no publicly available tweet corpora for such pervasive domains. Hence, we firstly create a multi-domain tweet sentiment corpora and then establish a deep neural network based baseline framework to address the above mentioned issues. Annotated corpus has Cohen’s Kappa measurement for annotation quality of 0.770, which shows that the data is of acceptable quality. We are able to achieve 84.65% accuracy for sentiment analysis by using an ensemble of Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit(GRU).

Summary Attribute‐based searchable encryption (ABSE) is the combination of attribute‐based encryption (ABE) and searchable encryption with the inherent benefits of fine‐grained access control and expressive searching capabilities in multiuser setting. In this paper, we have used the key‐policy (KP) design framework of ABE and named the scheme KP‐ABSE. The proposed KP‐ABSE scheme efficiently supports user revocation where the computationally intensive tasks are delegated to the cloud server. Furthermore, the proposed scheme generates constant‐size user secret keys and trapdoors and has constant number of pairing operations, which in other schemes typically varies with the number of attributes associated with them. Thus, the proposed scheme reduces computational and storage costs and supports fast searching. Finally, the proposed scheme can be proven secure under a decision linear assumption in a selective security model.