ChatGPT: A tool to embrace or ban in Academia?

ChatGPT: A tool to embrace or ban in Academia? Opinion piec

Minimal excision technique for epidermoid (sebaceous) cysts

This issue of eMedRef provides information to clinicians on the indications, procedures and follow up for the minimal excision technique for epidermoid (sebaceous) cysts

Combining BERT with Contextual Linguistic Features for Identification of Propaganda Spans in News Articles

Recent endeavours at detection of propaganda in news articles treat this as a fine-grained problem of detecting it within fragments; and hence, transformer based embeddings perform decently in such detection. We build our propaganda detection framework on top of a transformer model simultaneously enriching it with contextual linguistic information of surrounding part-of-speech tags and LIWC categories the word itself belongs to. The evaluation outcomes being encouraging indicate a huge potential for this line of reasoning in natural language processing of news text

A Framework for Sexism Detection on Social Media via ByT5 and TabNet

Hateful and offensive content on social media platforms particularly content directed towards a specific gender is a great impediment towards equality, diversity and inclusion. Social media platforms are facing increasing pressure to work towards regulation of such content; and this has directed researchers in text mining to work towards hate speech identification algorithms. One such attempt is sexism detection for which mostly transformer-based text methods have been proposed. We propose a combination of byte-level model ByT5 with tabular modeling via TabNet that has at its core an ability to take into account platform and language aspects of the challenging task of sexism detection. Despite not performing well in the sexism detection task for IberLEF our approach shows promise for future research in the area

Toward Inclusive Online Environments: Counterfactual-Inspired XAI for Detecting and Interpreting Hateful and Offensive Tweets

The prevalence of hate speech and offensive language on social media platforms such as Twitter has significant consequences, ranging from psychological harm to the polarization of societies. Consequently, social media companies have implemented content moderation measures to curb harmful or discriminatory language. However, a lack of consistency and transparency hinders their ability to achieve desired outcomes. This article evaluates various ML models, including an ensemble, Explainable Boosting Machine (EBM), and Linear Support Vector Classifier (SVC), on a public dataset of 24,792 tweets by T. Davidson, categorizing tweets into three classes: hate, offensive, and neither. The top-performing model achieves a weighted F1-Score of 0.90. Furthermore, this article interprets the output of the best-performing model using LIME and SHAP, elucidating how specific words and phrases within a tweet contextually impact its classification. This analysis helps to shed light on the linguistic aspects of hate and offense. Additionally, we employ LIME to present a suggestive counterfactual approach, proposing no-hate alternatives for a tweet to further explain the influence of word choices in context. Limitations of the study include the potential for biased results due to dataset imbalance, which future research may address by exploring more balanced datasets or leveraging additional features. Ultimately, through these explanations, this work aims to promote digital literacy and foster an inclusive online environment that encourages informed and responsible use of digital technologies

Subjectivity Detection through Socio-Linguistic Features

Social media platforms have opened new dimensions within the information retrieval domain leading to a novel concept known as Social Information Retrieval. We argue that the concept of Social Information Retrieval can be extended by augmenting the huge amount of content on the traditional Web with the ever-growing rich Social Web content to increase the information richness of today’s search engines. This paper proposes a subjectivity detection framework which can lead towards a proposed emotion-aware search engine interface. Our proposed method differs from previous subjectivity analysis approaches in that it is the first method that takes into account social features of social media platforms for the subjectivity classification task. Through experimental evaluations, we observe the accuracy of the proposed method to be 86.21% which demonstrates a promising outcome for large-scale application of our proposed subjectivity analysis technique

Ground Demonstration of New Robotic Technologies for On Orbit Servicing to Enable Maneuver Without Regret for Small Sat Missions Beyond GEO

In this paper we will discuss the demonstration of on-orbit servicing capabilities by a new robotic manipulator, designed from the bottom up to fit within the Size, Weight, and Power (SWaP) constraints and budget expectations of smallsat missions. There is a recognized need for extreme mobility to meet the space domain awareness goals of the U.S. government in cis-lunar space, and on orbit servicing is the key to establishing this capability. In addition, a small spacecraft profile is critical, with transportation costs particularly high beyond earth orbit. In the past, robotic systems capable of on-orbit servicing have resulted from years of expensive development and typically weighed more than 70 kg. This makes them ill-suited to the needs and constraints of small sat missions. The new Modular Robotic Manipulator (MRM) is right-sized in terms of performance, has mass in the range of 10 – 20 kg, and can be rapidly reconfigured for minimal recurring development in order to fit within smallsat mission budget constraints. In this paper, we will provide more details about the MRM, and describe our efforts to better understand its performance, and to demonstrate its ability to perform typical on-orbit servicing tasks. And finally, we will discuss the generation of manipulators beyond the MRM, and our efforts to further improve the accessibility of robotic systems