Food touches our lives through various endeavors, including flavor, nourishment, health, and sustainability. Recipes are cultural capsules transmitted across generations via unstructured text. Automated protocols for recognizing named entities, the building blocks of recipe text, are of immense value for various applications ranging from information extraction to novel recipe generation. Named entity recognition is a technique for extracting information from unstructured or semi-structured data with known labels. Starting with manually-annotated data of 6,611 ingredient phrases, we created an augmented dataset of 26,445 phrases cumulatively. Simultaneously, we systematically cleaned and analyzed ingredient phrases from RecipeDB, the gold-standard recipe data repository, and annotated them using the Stanford NER. Based on the analysis, we sampled a subset of 88,526 phrases using a clustering-based approach while preserving the diversity to create the machine-annotated dataset. A thorough investigation of NER approaches on these three datasets involving statistical, fine-tuning of deep learning-based language models and few-shot prompting on large language models (LLMs) provides deep insights. We conclude that few-shot prompting on LLMs has abysmal performance, whereas the fine-tuned spaCy-transformer emerges as the best model with macro-F1 scores of 95.9%, 96.04%, and 95.71% for the manually-annotated, augmented, and machine-annotated datasets, respectively.

Self and Semi-Supervised Learning have shown promising results in language and computer vision but are still underexplored in the context of tabular data. This paper focuses on exploring self and semi-supervised methods for tabular data. Towards this, we have proposed Auto-Tab Transformer, a method for training hierarchical transformers in a self and semi-supervised setup using redundancy reduction. The technique focuses on key aspects of self and semi-supervised learning: feature encoding, pre-training objective, training methodology and neural architecture. Performing extensive experiments on four publically accessible datasets, we show that Auto-Tab Transformer achieves state of the art (SOTA) results in the less labelled data domain. We conduct extensive ablation studies detailing the importance of all the components used.

The discovery of the Spectre attack in 2018 has sent shockwaves through the computer industry, affecting processor vendors, OS providers, programming language developers, and more. Because web browsers execute untrusted code while potentially accessing sensitive information, they were considered prime targets for attacks and underwent significant changes to protect users from speculative execution attacks. In particular, the Google Chrome browser adopted the strict site isolation policy that prevents leakage by ensuring that content from different domains is not shared in the same address space. The perceived level of risk that Spectre poses to web browsers stands in stark contrast with the paucity of published demonstrations of the attack. Before mid-March 2021, there was no public proof-of-concept demonstrating leakage of information that is otherwise inaccessible to an attacker. Moreover, Google’s leaky.page, the only current proof-of-concept that can read such information, is severely restricted to only a subset of the address space and does not perform cross-website accesses. In this paper, we demonstrate that the absence of published attacks does not indicate that the risk is mitigated. We present Spook.js, a JavaScript-based Spectre attack that can read from the entire address space of the attacking webpage. We further investigate the implementation of strict site isolation in Chrome, and demonstrate limitations that allow Spook.js to read sensitive information from other webpages. We further show that Spectre adversely affects the security model of extensions in Chrome, demonstrating leaks of usernames and passwords from the LastPass password manager. Finally, we show that the problem also affects other Chromium-based browsers, such as Microsoft Edge and Brave.

The advancements in the field of Information Communication Technology have engendered revolutionary changes in the journalism industry, not only on the part of the journalists and the media personnel, but also on the people consuming these news stories, who today, are only a click away from all the updates they need. However, these advances have also exposed the prevailing venality, wearying off the trust of the public in news media. How then, does an individual discern that which, out of the countless news stories for an incident, should be trusted? This work introduces a system that presents the user a multidimensional analysis for trust in news from various media sources based on the textual content of the articles, assessment of the journalists' perspectives and the temporal diversity of the issues being covered by the media houses publishing the news articles. Our experiments on a self-collected dataset confirm that the system aids in a comprehensive analysis of trust.

Detection, segmentation and classification of nuclei are fundamental analysis operations in digital pathology. Existing state-of-the-art approaches demand extensive amount of supervised training data from pathologists and may still perform poorly in images from unseen tissue types. We propose an unsupervised approach for histopathology image segmentation that synthesizes heterogeneous sets of training image patches, of every tissue type. Although our synthetic patches are not always of high quality, we harness the motley crew of generated samples through a generally applicable importance sampling method. This proposed approach, for the first time, re-weighs the training loss over synthetic data so that the ideal (unbiased) generalization loss over the true data distribution is minimized. This enables us to use a random polygon generator to synthesize approximate cellular structures (i.e., nuclear masks) for which no real examples are given in many tissue types, and hence, GAN-based methods are not suited. In addition, we propose a hybrid synthesis pipeline that utilizes textures in real histopathology patches and GAN models, to tackle heterogeneity in tissue textures. Compared with existing state-of-the-art supervised models, our approach generalizes significantly better on cancer types without training data. Even in cancer types with training data, our approach achieves the same performance without supervision cost. We release code and segmentation results on over 5000 Whole Slide Images (WSI) in The Cancer Genome Atlas (TCGA) repository, a dataset that would be orders of magnitude larger than what is available today.