Multiple Choice Questions (MCQs) are a popular assessment method because they enable automated evaluation, flexible administration and use with huge groups. Despite these benefits, the manual construction of MCQs is challenging, time-consuming and error-prone. This is because each MCQ is comprised of a question called the "stem", a correct option called the "key" along with alternative options called "distractors" whose construction demands expertise from the MCQ developers. In addition, there are different kinds of MCQs such as Wh-type, Fill-in-the-blank, Odd one out, and many more needed to assess understanding at different cognitive levels. Automatic Question Generation (AQG) for developing heterogeneous MCQ stems has generally followed two approaches: semantics-based and machine-learning-based. Questions generated via AQG techniques can be utilized only if they are grammatically correct. Semantics-based techniques have been able to generate a range of different types of grammatically correct MCQs but require the semantics to be specified. In contrast, most machine-learning approaches have been primarily able to generate only grammatically correct Fill-in-the-blank/Cloze by reusing the original text. This paper describes a technique for combining semantic-based and machine-learning-based techniques to generate grammatically correct MCQ stems of various types for a technical domain. Expert evaluation of the resultant MCQ stems demonstrated that they were promising in terms of their usefulness and grammatical correctness.

With the advent of new technologies, simplifying the text automatically has been very popular and of high importance among natural language researchers during the last decade. The predominant research done in the area of Automatic Sentence Simplification(ASS) is inclined to either lexical or syntactical simplification of sentences. From the literature survey, it is observed that existing research in lexical simplification makes use of word substitution technique. This causes word sense ambiguity in cases where the word synonyms are not appropriate for a sentence in the given context. In contrast syntactical simplification though is accurate and applicable to Natural Language Processing (NLP) tasks requires tremendous efforts to construct rules for a given domain. The research proposes a framework called, Pattern-based Automatic Syntactic Simplification(PASS) which identifies sentences and applies rules based on grammatical patterns to simplify the sentences thereby making it more generic for NLP tasks. PASS is evaluated by human experts to rate the usefulness of the framework based on fluency, adequacy and simplicity of the sentences. Furthermore, the framework is automatically evaluated with the available online corpus using automatic metrics of SARI, BLEU, and FKGL. The proposed approach generates promising results in the field of ASS and could be used as a preliminary module for NLP tasks as well as other natural language-related applications like summarization, anaphora resolution, question-answering, and many more.

As the nano scale device performance depends on the detailed engineering of the dopant distribution, advanced doping processes are required. Progressing towards 3D-structures like FinFETs, studying the dopant gate overlap and conformality of doping calls for metrology with 3D-resolution and the ability to confine the analyzed volume to a small 3D-structure. We demonstrate that through an appropriate methodology this is feasible using Atom Probe Tomography (APT). We extract the 3D-dopant profile and important parameters such as gate overlap and profile steepness, from transistor formed with plasma doping processes. Analyzing samples with different doping processes, the APT results are entirely consistent with device performances (I_off vs. I_on).

The proposed paper examines enhancements in Visual Question Answering (VQA) by systematically tuning hyperparameters and utilizing advanced image and text encoders. The study particularly explores the adaptation of these models to Multiple-Choice Question (MCQ) formats, aiming to refine their accuracy and applicability. MCQs consist of a question stem along with a set of options, from which the correct answer, the key needs to be identified among the distractors. Using MCQs provides the model with some context of the correct answer, improving its performance over a simple multiclass classification task. The research showcases the effectiveness of precise hyperparameter adjustments in improving the performance of VQA systems, through comparative analysis of varied sets of hyperparameters, highlighting their improved reasoning capabilities across various datasets, including samples from real world images and academic questions. This demonstrates the potential of VQA models for robust application in both educational and practical scenarios.