Abstract Can we control the writing style of large language models (LLMs) by specifying desired linguistic features? We address this question by investigating the impact of handcrafted linguistic feature (HLF) instructions on LLM-generated text. Our experiment evaluates various state-of-the-art LLMs using prompts incorporating HLF statistics derived from corpora of CNN articles and Yelp reviews.…
Abstract As virtual assistants (VAs) become essential to contemporary interactions, it is imperative to understand how to evaluate their functionalities. This study offers a comparison framework for assessing the design and execution of Amazon Alexa and Microsoft Copilot Studio, emphasizing their capabilities in question-answering activities. Through the examination of their deterministic and probabilistic approaches,…
Abstract This article examines the use of image augmentation techniques to improve icon detection in mobile interfaces, a critical task due to the small size of graphical user interface (GUI) elements and the insufficiency of comprehensive datasets. It evaluates whether diversifying the dataset or using specific augmentation methods alone can enhance detection performance. The…
Abstract Entity resolution, the process of discerning whether multiple data refer to the same real-world entity, is crucial across various domains, including education. Its quality assessment is vital due to the extensive practical applications in fields such as analytics, personalized learning or academic integrity. With Python emerging as the predominant programming language in these…
Abstract The latest improvements regarding the online world have come with great benefits, but, as well as dangerous drawbacks (i.e., web-malware). This article proposes to investigate the reliability and accuracy of a novel web-malware detection method by using images and deep learning. The web links are transformed into colored and grayscale images and then…
Abstract The integration of Artificial Intelligence (AI), particularly deep learning models like VGG16 and ResNet50, in the analysis of functional magnetic resonance imaging (fMRI) data has significantly advanced our understanding of brain functionality and the diagnosis of neurological disorders. This paper explores the application of Convolutional Neural Networks (CNNs) to enhance the accuracy and…
Abstract The critical node detection problem is a central task in computational graph theory due to its large applicability, consisting in deleting $k$ nodes to minimize a certain graph measure. In this article, we propose a new Extremal Optimization-based approach, the Pseudo-Deterministic Noisy Extremal Optimization (PDNEO) algorithm, to solve the Critical Node Detection variant…
Abstract Social network analysis is a rapidly evolving research area having several real-life application areas, e.g. digital marketing, epidemiology, spread of misinformation. Influence maximization aims to select a subset of nodes in such manner that the information propagated over the network is maximized. Competitive influence maximization, which describes the phenomena of multiple actors competing…
Abstract Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining…
Abstract Our research focuses on creating a framework for extracting 3D deformable objects from 2D scenes. We research the possibility of using multiple graph convolutional operators and depth estimators to extract the object, while also using predefined segmentation masks for the objects in the images. The experiments focus on a dataset from 2017, containing…