Recent advancements in generative AI, particularly in speech synthesis, have enabled the generation of highly natural-sounding synthetic speech that closely mimics human voices. While these innovations hold promise for applications like assistive technologies, they also pose significant risks, including misuse for fraudulent activities, identity theft, and security threats. Current research on spoofing detection countermeasures remains limited by generalization to unseen deepfake attacks and languages. To address this, we propose a gating mechanism extracting relevant feature from the speech foundation XLS-R model as a front-end feature extractor. For downstream back-end classifier, we employ Multi-kernel gated Convolution (MultiConv) to capture both local and global speech artifacts. Additionally, we introduce Centered Kernel Alignment (CKA) as a similarity metric to enforce diversity in learned features across different MultiConv layers. By integrating CKA with our gating mechanism, we hypothesize that each component helps improving the learning of distinct synthetic speech patterns. Experimental results demonstrate that our approach achieves state-of-the-art performance on in-domain benchmarks while generalizing robustly to out-of-domain datasets, including multilingual speech samples. This underscores its potential as a versatile solution for detecting evolving speech deepfake threats.

The insurance company uses different factors to classify the policyholders. In this study, we apply several machine learning models such as nearest neighbour and logistic regression to the Actuarial Challenge dataset used by Qazvini (2019) to classify liability insurance policies into two groups: 1 - policies with claims and 2 - policies without claims. The applications of Machine Learning (ML) models and Artificial Intelligence (AI) in areas such as medical diagnosis, economics, banking, fraud detection, agriculture, etc, have been known for quite a number of years. ML models have changed these industries remarkably. However, despite their high predictive power and their capability to identify nonlinear transformations and interactions between variables, they are slowly being introduced into the insurance industry and actuarial fields.

Crowdsourcing is the outsourcing of tasks to a crowd of contributors on a dedicated platform. The crowd on these platforms is very diversified and includes various profiles of contributors which generates data of uneven quality. However, majority voting, which is the aggregating method commonly used in platforms, gives equal weight to each contribution. To overcome this problem, we propose a method, MONITOR, which estimates the contributor's profile and aggregates the collected data by taking into account their possible imperfections thanks to the theory of belief functions. To do so, MONITOR starts by estimating the profile of the contributor through his qualification for the task and his behavior.Crowdsourcing campaigns have been carried out to collect the necessary data to test MONITOR on real data in order to compare it to existing approaches. The results of the experiments show that thanks to the use of the MONITOR method, we obtain a better rate of correct answer after aggregation of the contributions compared to the majority voting. Our contributions in this article are for the first time the proposal of a model that takes into account both the qualification of the contributor and his behavior in the estimation of his profile. For the second one, the weakening and the aggregation of the answers according to the estimated profiles.

Crowdsourcing is defined as the outsourcing of tasks to a crowd of contributors. The crowd is very diverse on these platforms and includes malicious contributors attracted by the remuneration of tasks and not conscientiously performing them. It is essential to identify these contributors in order to avoid considering their responses. As not all contributors have the same aptitude for a task, it seems appropriate to give weight to their answers according to their qualifications. This paper, published at the ICTAI 2019 conference, proposes a method, MONITOR, for estimating the profile of the contributor and aggregating the responses using belief function theory.

This dataset challenges the time series community with the task of satellite-based vegetation identification on large scale real-world dataset of satellite data acquired during one entire year. It consists of time series data with associated crop types from 580k field parcels in Brittany, France (Breizh in local language). Along with this dataset, we provide results and code of a Long Short-Term Memory network and Transformer network as baselines. We release dataset, along with preprocessing scripts and baseline models in https://github.com/TUM-LMF/BreizhCrops and encourage methodical researchers to benchmark and develop novel methods applied to satellite-based crop monitoring.

How will our societies evolve with new technologies? This is the question asked by photographer Nicolas Bigot with this series « The Robot Next Door ». This resident of the côtes d'Armor, France, is passionate about science-fiction films and has been a UI/UX designer for ten years. From his studies years in interface design between man and machine for mobile and computer, Nicolas retains an appeal for fantastical subjects. This series is a combination of all his passions.

In The News This Week is a page intended to keep my readers up to speed with anything new that I pickup online or offline that has to do with the robotic world that we are now entering very rapidly. Other pages of this website reviews a number robots that we are using on a daily basis in order to help make our lives easier, or as hobby, sport, or for professional purposes. If you would like to share your experience with any kind of robots you are using, or simply comment or ask questions, please feel free to do so at the bottom of any page or article. My latest article of "In The News This Week" starts from here. A house of 95 m2 / 1,022 sq ft has already been built thanks to this new technique. On the slab of freshly poured concrete, a robot moves on its wheels and makes work tirelessly with his articulated arm. He draws expansive foam cords one above the other to form a shuttering in which he then pour the concrete. This is how he manages to build perfectly insulated walls on each side at a bewildering speed. "It's been an hour and a half since the work began and the walls are already over 80 cm / 31 in. It is not a prototype, he pointed out, but a place that is meant to be useful. The 95 m2 / 1,022 sq ft house was finished by the end of that week and ready for the coming Christmas once the finishing work was completed. After being opened to the public, this T 5 will then be inhabited, a year later, by "traditional" tenants on the Nantes Métropole Habitat waiting list. "This house, which is already certified, says Benoit Furet, teacher-researcher at the University of Nantes at the heart of this project is called Yhnova.