The concentration of technical knowledge among a limited group of contributors can undermine the sustainability and stability of critical software systems. In software engineering, the truck factor highlights the risk related to knowledge concentration. The truck factor refers to the minimum number of key contributors whose sudden departure would jeopardize the project’s development. A higher truck factor indicates a more robust project that is less dependent on a small subset of individuals, making it a useful metric to evaluate the reliability of external libraries in mission-critical applications. In this work, we propose a novel algorithm for estimating the truck factor that addresses limitations of previous studies. We employ a knowledge-based and time-based framework that assesses the distribution of expertise across the repository. Notably, the framework recognizes that multiple contributors may possess partial familiarity with specific segments of code, even if they have not directly authored them. Furthermore, we propose a knowledge decay model, inspired by psychological principles, which adjusts the expertise metrics over time. This model aims to account for the diminishing relevance of historical contributions, ensuring that the truck factor estimation accurately reflects the current resilience of the project.

Satellite images suffer from inevitable degradations such as blur, noise, and other artifacts making them less suitable for final applications unless they are restored using processing techniques. Modern methods based on deep learning have achieved impressive results in key tasks such as deblurring, denoising, super-resolution, etc. in the domain of terrestrial images but their applicability for satellite image restoration remains limited due to the unavailability of paired datasets for supervised learning. In this work, we propose a framework to address this problem by utilizing a Generative Adversarial Network along with conventional degradation estimation techniques to generate a paired dataset for deep learning. The paired dataset is generated from a set of images with desirable characteristics and hence acts as a surrogate for the actual clean data. We perform experiments to demonstrate that our strategy is able to employ recent deep learning models for standard datasets to achieve impressive restoration results on satellite images for which no paired datasets are available. Visual as well as quantitative results using 30 cm Worldview-3 and Cartosat-3 images show that our strategy is a simple, yet effective way of generating a realistic dataset for utilizing deep-learning solutions for satellite image restoration.

One-anaphora has figured prominently in theoretical linguistic literature, but computational linguistics research on the phenomenon is sparse.Not only that, the long standing linguistic controversy between the determinative and the nominal anaphoric element one has propagated in the limited body of computational work on one-anaphora resolution, making this task harder than it is.In the present paper, we resolve this by drawing from an adequate linguistic analysis of the word one in different syntactic environments -once again highlighting the significance of linguistic theory in Natural Language Processing (NLP) tasks.We prepare an annotated corpus marking actual instances of one-anaphora with their textual antecedents, and use the annotations to experiment with state-of-the art neural models for one-anaphora resolution.Apart from presenting a strong neural baseline for this task, we contribute a gold-standard corpus, which is, to the best of our knowledge, the biggest resource on one-anaphora till date.

Sentiment analysis is a widely researched NLP problem with state-of-the-art solutions capable of attaining human-like accuracies for various languages. However, these methods rely heavily on large amounts of labeled data or sentiment weighted language-specific lexical resources that are unavailable for low-resource languages. Our work attempts to tackle this data scarcity issue by introducing a neural architecture for language invariant sentiment analysis capable of leveraging various monolingual datasets for training without any kind of cross-lingual supervision. The proposed architecture attempts to learn language agnostic sentiment features via adversarial training on multiple resource-rich languages which can then be leveraged for inferring sentiment information at a sentence level on a low resource language. Our model outperforms the current state-of-the-art methods on the Multilingual Amazon Review Text Classification dataset [REF] and achieves significant performance gains over prior work on the low resource Sentiraama corpus [REF]. A detailed analysis of our research highlights the ability of our architecture to perform significantly well in the presence of minimal amounts of training data for low resource languages.