Abstract

Abstract

Abstract
Various relevant aspects of our lives relate to the places we visit and our daily activities. The movement of individuals between regular places, such as work, school, or other important personal locations is getting increasing attention due to the pervasiveness of geolocation devices and the amount of data they generate. This work presents an approach for location prediction using a probabilistic model and data mining techniques over mobility data streams. We evaluate the method over 5 real-world datasets. The results show the usefulness of the proposal in comparison with other-well-known approaches.

Abstract
We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams. The core idea behind S+t-SNE is to update the t-SNE embedding incrementally as new data arrives, ensuring scalability and adaptability to handle streaming scenarios. By selecting the most important points at each step, the algorithm ensures scalability while keeping informative visualisations. By employing a blind method for drift management, the algorithm adjusts the embedding space, which facilitates the visualisation of evolving data dynamics. Our experimental evaluations demonstrate the effectiveness and efficiency of S+t-SNE, whilst highlighting its ability to capture patterns in a streaming scenario. We hope our approach offers researchers and practitioners a real-time tool for understanding and interpreting high-dimensional data.

Abstract
Deep neural networks (DNN) are used to analyze images, videos, signals and texts require a lot of memory and intensive computing power. For example, the very successful GPT4 model contains more than a few trillion parameters. Although such models are of great impact, but they have been used very little in real-world applications, including industrial Internet of Things, self-driving cars, algorithmic health monitoring for use in limited mobile or edge devices. The requirement to run large models on resource-constrained peripherals has led to significant research interest in compressing DNN models. Signal processing researchers have traditionally advocated data (image/video/audio) compression, and by the way, many of these techniques are used for DNN compression. For example, source coding is a basic technique that has been widely used to compress various DNN models. In this paper, we present our views on the use of signal processing methods for DNN model compression.

Abstract
The classic literature about innovation conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. The advent of Artificial Intelligence (AI) however reverts this paradigm in the context of Industry 5.0. The focus is moving from how innovation fosters AI to how AI fosters innovation. Therefore, our research question can be stated as follows: What factors influence the effect of AI on Innovation Capacity in the context of Industry 5.0? To address this question we conduct a scoping review of a vast body of literature spanning engineering, human sciences, and management science. We conduct a keyword-based literature search completed by bibliographic analysis, then classify the resulting 333 works into 3 classes and 15 clusters which we critically analyze. We extract 3 hypotheses setting associations between 4 factors: company age, AI maturity, manufacturing strategy, and innovation capacity. The review uncovers several debates and research gaps left unsolved by the existing literature. In particular, it raises the debate whether the Industry5.0 promise can be achieved while Artificial General Intelligence (AGI) remains out of reach. It explores diverging possible futures driven toward social manufacturing or mass customization. Finally, it discusses alternative AI policies and their incidence on open and internal innovation. We conclude that the effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors. Moreover, we identify a set of 12 indicators enabling us to measure these factors to predict AI's effect on innovation capacity. These findings provide researchers with a new understanding of the interplay between artificial intelligence and human intelligence. They provide practitioners with decision metrics for a successful transition to Industry 5.0.