Clustering Text has been an important problem in the domain of Natural Language Processing. While there are techniques to cluster text based on using conventional clustering techniques on top of contextual or non-contextual vector space representations, it still remains a prevalent area of research possible to various improvements in performance and implementation of these techniques. This paper discusses a novel technique to cluster text using attention mechanisms. Attention Mechanisms have proven to be highly effective in various NLP tasks in recent times. This paper extends the idea of attention mechanism in clustering space and sheds some light on a whole new area of research

The vulnerability of models to data aberrations and adversarial attacks influences their ability to demarcate distinct class boundaries efficiently. The network's confidence and uncertainty play a pivotal role in weight adjustments and the extent of acknowledging such attacks. In this paper, we address the trade-off between the accuracy and calibration potential of a classification network. We study the significance of ground-truth distribution changes on the performance and generalizability of various state-of-the-art networks and compare the proposed method's response to unanticipated attacks. Furthermore, we demonstrate the role of label-smoothing regularization and normalization in yielding better generalizability and calibrated probability distribution by proposing normalized soft labels to enhance the calibration of feature maps. Subsequently, we substantiate our inference by translating conventional convolutions to padding based partial convolution to establish the tangible impact of corrections in reinforcing the performance and convergence rate. We graphically elucidate the implication of such variations with the critical purpose of corroborating the reliability and reproducibility for multiple datasets.

Software development comprises the use of multiple Third-Party Libraries (TPLs). However, the irrelevant libraries present in software application's distributable often lead to excessive consumption of resources such as CPU cycles, memory, and modile-devices' battery usage. Therefore, the identification and removal of unused TPLs present in an application are desirable. We present a rapid, storage-efficient, obfuscation-resilient method to detect the irrelevant-TPLs in Java and Python applications. Our approach's novel aspects are i) Computing a vector representation of a .class file using a model that we call Lib2Vec. The Lib2Vec model is trained using the Paragraph Vector Algorithm. ii) Before using it for training the Lib2Vec models, a .class file is converted to a normalized form via semantics-preserving transformations. iii) A eXtra Library Detector (XtraLibD) developed and tested with 27 different language-specific Lib2Vec models. These models were trained using different parameters and >30,000 .class and >478,000 .py files taken from >100 different Java libraries and 43,711 Python available at MavenCentral.com and Pypi.com, respectively. XtraLibD achieves an accuracy of 99.48% with an F1 score of 0.968 and outperforms the existing tools, viz., LibScout, LiteRadar, and LibD with an accuracy improvement of 74.5%, 30.33%, and 14.1%, respectively. Compared with LibD, XtraLibD achieves a response time improvement of 61.37% and a storage reduction of 87.93% (99.85% over JIngredient). Our program artifacts are available at this https URL

Fake News Detection has been a challenging problem in the field of Machine Learning. Researchers have approached it via several techniques using old Statistical Classification models and modern Deep Learning. Today, with the growing amount of data, developments in the field of NLP and ML, and an increase in the computation power at disposal, there are infinite permutations and combinations to approach this problem from a different perspective. In this paper, we try different methods to tackle Fake News, and try to build, and propose the possibilities of a Hybrid Ensemble combining the classical Machine Learning techniques with the modern Deep Learning Approaches