CybAI news

arXiv:2407.00402v1 Announce Type: new Abstract: Improvements in language models' capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area. However, many disparate use-cases are grouped together under the umbrella term of "long-context", defined simply by the total length of the model's input, including - for example - Needle-in-a-Haystack tasks, book summarization, and information aggregation. Given their varied difficulty, in this position paper we argue that conflating different tasks by their context length is unproductive. As a community, we require a more precise vocabulary to understand what makes long-context tasks similar or different. We…

arXiv:2407.00134v1 Announce Type: new Abstract: Big data contain rich information for machine learning algorithms to utilize when learning important features during classification tasks. Human beings express their emotion using certain words, speech (tone, pitch, speed) or facial expression. Artificial Intelligence approach to emotion classification are largely based on learning from textual information. However, public datasets containing text and speech data provide sufficient resources to train machine learning algorithms for the tack of emotion classification. In this paper, we present novel bimodal deep learning-based architectures enhanced with attention mechanism trained and tested on text and speech data for emotion classification. We…

arXiv:2407.00301v1 Announce Type: new Abstract: This paper investigates the trade-off between computational resource utilization and image quality in the context of image fusion techniques for smartphone camera capture. The study explores various combinations of fusion methods, fusion weights, number of frames, and stacking (a.k.a. merging) techniques using a proprietary dataset of images captured with Motorola smartphones. The objective was to identify optimal configurations that balance computational efficiency with image quality. Our results indicate that multi-scale methods and their single-scale fusion counterparts return similar image quality measures and runtime, but single-scale ones have lower memory usage. Furthermore, we identified that fusion…