This enables the identification of the text for which AI assistance has been used. How AI was used AI tools can be used for many different tasks: summarizing, translation, paraphrasing, finding related work and citations, etc. So, it is important to have information on how AI tools were used in the paper. For example, we can encode in the metadata that GPT -4 (so the "which") was used to summarize (the "how") and write the abstract (the "where").UNDERSTANDING THE IMP ACT OF AI IN ACADEMIC WRITING Let us consider now that we have a large corpus of papers and we want to know how many of them have used AI to summarize the abstract. Without metadata, all papers look the same (Figure 2, left), so we have to extract the text and either try to detect the use of AI in the abstract or find a disclosure of the authors that states the use of AI in the abstract. Instead if the proposed metadata has been added to the paper, we can just look at the how (summarizing) and where (abstract) to find the papers. The papers are now marked and can be easily identified (Figure 2, right). The metadata can be used to analyze many aspects of the use of AI in academic writing, for example, we can analyze: 1) The adoption of the different AI tools and their variations over time.

In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use cases that may be affected by such methods. In addition, we explore the limitations of such techniques in order to determine their potential scalability.

This systematic review focuses on analyzing the use of machine learning techniques for identifying and quantifying analytes in various electrochemical applications, presenting the available applications in the literature. Machine learning is a tool that can facilitate the analysis and enhance the understanding of processes involving various analytes. In electrochemical biosensors, it increases the precision of medical diagnostics, improving the identification of biomarkers and pathogens with high reliability. It can be effectively used for the classification of complex chemical products; in environmental monitoring, using low-cost sensors; in portable devices and wearable systems; among others. Currently, the analysis of some analytes is still performed manually, requiring the expertise of a specialist in the field and thus hindering the generalization of results. In light of the advancements in artificial intelligence today, this work proposes to carry out a systematic review of the literature on the applications of artificial intelligence techniques. A set of articles has been identified that address electrochemical problems using machine learning techniques, more specifically, supervised learning.

Recently, there has been an interest in improving the resources available in Intrusion Detection System (IDS) techniques. In this sense, several studies related to cybersecurity show that the environment invasions and information kidnapping are increasingly recurrent and complex. The criticality of the business involving operations in an environment using computing resources does not allow the vulnerability of the information. Cybersecurity has taken on a dimension within the universe of indispensable technology in corporations, and the prevention of risks of invasions into the environment is dealt with daily by Security teams. Thus, the main objective of the study was to investigate the Ensemble Learning technique using the Stacking method, supported by the Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) algorithms aiming at an optimization of the results for DDoS attack detection. For this, the Intrusion Detection System concept was used with the application of the Data Mining and Machine Learning Orange tool to obtain better results

A study by researchers at the Universitat Politècnica de València (UPV), part of BDSLab-ITACA group and the Institute of Pure and Applied Mathematics (IUMPA), has become an international benchmark for the reliable use of artificial intelligence in monitoring and managing COVID-19, writes the Technical University of Valencia in this press release. In the article, published in the Journal of the American Medical Informatics Association, the team from the UPV demonstrates the limitations that the variability or heterogeneity of data may have in reliably applying artificial intelligence when it comes from multiple sources, e.g. a range of hospitals or countries. Furthermore, the UPV team has developed new tools based on this study to help describe and classify patients with COVID-19. "The results of our study may, combined with these tools, assist in clinically assessing patients, and help with automated early classification by risk level both before and after hospital admission. They can even help to plan the allocation of resources, which is particularly beneficial for patients that will be admitted to the ICU," says Carlos Sáez, a member of the BDSLab-ITACA group research team at Universitat Politècnica de València, who coordinated the study.

Graph-based Neural Networks (GNNs) are recent models created for learning representations of nodes (and graphs), which have achieved promising results when detecting patterns that occur in large-scale data relating different entities. Among these patterns, financial fraud stands out for its socioeconomic relevance and for presenting particular challenges, such as the extreme imbalance between the positive (fraud) and negative (legitimate transactions) classes, and the concept drift (i.e., statistical properties of the data change over time). Since GNNs are based on message propagation, the representation of a node is strongly impacted by its neighbors and by the network's hubs, amplifying the imbalance effects. Recent works attempt to adapt undersampling and oversampling strategies for GNNs in order to mitigate this effect without, however, accounting for concept drift. In this work, we conduct experiments to evaluate existing techniques for detecting network fraud, considering the two previous challenges. For this, we use real data sets, complemented by synthetic data created from a new methodology introduced here. Based on this analysis, we propose a series of improvement points that should be investigated in future research.

Cutting Edge Deep Learning For Coders, Part 2 from fast.ai

Cutting Edge Deep Learning For Coders, Part 2 from fast.ai

These workshops, although quite specialized, have covered areas of great interest for the conference delegates, namely: "Multiagent System Robotics" (MARS), "Biosignal Processing and Classification" (BPC), and "Artificial Neural Networks and Intelligent Information Processing" (ANNIIP). In the program of this conference for publication in the proceedings were included oral presentations (full and for presentation at the conference; papers and short papers) and posters, of these, 166 papers were organized in three simultaneous selected for oral presentation (67 full tracks: "Intelligent Control Systems papers and 99 short papers) and 63 papers and Optimization," "Robotics and Automation," were accepted for poster presentation. Furthermore, less than 60 percent, and the full paper (ICINCO 2005) was held in Barcelona ICINCO 2005 included acceptance ratio was 17 percent.