We propose a novel approach for the automatic equalization of individual musical instrument tracks. Our method begins by identifying the instrument present within a source recording in order to choose its corresponding ideal spectrum as a target. Next, the spectral difference between the recording and the target is calculated, and accordingly, an equalizer matching model is used to predict settings for a parametric equalizer. To this end, we build upon a differentiable parametric equalizer matching neural network, demonstrating improvements relative to previously established state-of-the-art. Unlike past approaches, we show how our system naturally allows real-world audio data to be leveraged during the training of our matching model, effectively generating suitably produced training targets in an automated manner mirroring conditions at inference time. Consequently, we illustrate how fine-tuning our matching model on such examples considerably improves parametric equalizer matching performance in real-world scenarios, decreasing mean absolute error by 24% relative to methods relying solely on random parameter sampling techniques as a self-supervised learning strategy. We perform listening tests, and demonstrate that our proposed automatic equalization solution subjectively enhances the tonal characteristics for recordings of common instrument types.

While the search for new solvents in the chemical industry is of uttermost importance with respect to environmental considerations, this domain remains strongly tied to highly manual and visual inspection tasks by human experts. As the manipulated chemicals may imply a critical danger (CMR substances), mechanical protection barrier are used (fume hoods, gloveboxes). This, in turn, can induce postural discomfort in the long term. Carrying out this task using a remotely controlled robot to reproduce the desired vial motions would alleviate these postural constraints. Nevertheless, the adoption of such a system will depend on its ability to transcribe the users' expertise. Particular attention must be paid to the intuitiveness of the system : transparency of the actions performed, relevance of the perceptual feedback, etc. and, in particular, the fidelity of the movements performed in relation to the user's commands. However, the extent of the rotational movements to be generated and the task interactivity complicates the problem both from the point of view of the motor capacities of industrial robots and for the transparency/responsiveness of the control.To tackle the problen of guaranteeing a secure and reactive expression of the manual characteristics of this task, we propose to separate the control of movement into two parts: control of the path (set of spatial poses) and of the trajectories associated with this path (speed, direction of travel along the path). The user can then partially control the robot's movements, by choosing the type of generic, secure path and modulating the trajectory performed on this path in real time. Although this drastically limits the possibilities for interaction, we assume that this teleoperated system can enable this type of observation task to be carried out as effectively as for direct manipulation. This hypothesis was tested through an experiment in which a reading task, less dangerous but with similar characteristics to the application task, had to be performed using different variants of trajectory modulation. This experiment consisted in reading words printed on four white capsules (dimensions 6 x 12 mm) placed into cylindrical vials ( dimensions 16 mm x 70 mm). Four randomly selected vials were tested by each variant. Firstly, users had to perform the task via direct handling, then under conditions secured by a protection barrier. Users were then invited to perform the task using different trajectory modulation variants (modulation and passive viewing of a pre-recorded video, modulation of the trajectory of a Franka-Emika Panda robot performing the task in real time in front of a monocular Logitech Brio 4K camera). After each trial of a variant, users evaluate different aspects of this variant (manual and visual performance, ease of use, acceptability of the interface) through a questionnaire. During the trials, various objective criteria are also measured (number and nature of interaction with the interface, time and degree of success in the task). This experiment was carried out with 37 subjects (age : 27$\pm$5, 20 females). The data recorded showed that the proportion of successes, as well as the subjects' perceptions of visual performance, comfort of use and acceptability of the interface, were similar and high for all the variants. This suggests that this task is indeed achievable via the proposed interface. However, data also showed that average task completion times when using the trajectory modulation variants were significantly higher than handling by hand variants, which implies that the proposed remote semi-automatic control procedure fails to achieve satisfactory performance regarding execution time. An interface allowing more reactive manipulation of the vial's movements seems necessary, and will be tested in a future experiment.

Although outdoor localization is already available to the general public and businesses through the wide spread use of the GPS, it is not supported by low-end phones, requires a direct line of sight to satellites and can drain phone battery quickly. The current fingerprinting solutions can provide high-accuracy localization but are based on the client side. This limits their ubiquitous deployment and accuracy. In this paper, we introduce DeepCell: a provider-side fingerprinting localization system that can provide high accuracy localization for any cell phone. To build its fingerprint, DeepCell leverages the unlabeled cellular measurements recorded by the cellular provider while opportunistically synchronizing with selected client devices to get location labels. The fingerprint is then used to train a deep neural network model that is harnessed for localization. To achieve this goal, DeepCell need to address a number of challenges including using unlabeled data from the provider side, handling noise and sparsity, scaling the data to large areas, and finally providing enough data that is required for training deep models without overhead. Evaluation of DeepCell in a typical realistic environment shows that it can achieve a consistent median accuracy of 29m. This accuracy outperforms the state-of-the-art client-based cellular-based systems by more than 75.4%. In addition, the same accuracy is extended to low-end phones.

Developing and certifying safe - or so-called trustworthy - AI has become an increasingly salient issue, especially in light of upcoming regulation such as the EU AI Act. In this context, the black-box nature of machine learning models limits the use of conventional avenues of approach towards certifying complex technical systems. As a potential solution, methods to give insights into this black-box - devised in the field of eXplainable AI (XAI) - could be used. In this study, the potential and shortcomings of such methods for the purpose of safe AI development and certification are discussed in 15 qualitative interviews with experts out of the areas of (X)AI and certification. We find that XAI methods can be a helpful asset for safe AI development, as they can show biases and failures of ML-models, but since certification relies on comprehensive and correct information about technical systems, their impact is expected to be limited.

Geolocalization of social media content is the task of determining the geographical location of a user based on textual data, that may show linguistic variations and informal language. In this project, we address the GeoLingIt challenge of geolocalizing tweets written in Italian by leveraging large language models (LLMs). GeoLingIt requires the prediction of both the region and the precise coordinates of the tweet. Our approach involves fine-tuning pre-trained LLMs to simultaneously predict these geolocalization aspects. By integrating innovative methodologies, we enhance the models' ability to understand the nuances of Italian social media text to improve the state-of-the-art in this domain. This work is conducted as part of the Large Language Models course at the Bertinoro International Spring School 2024. We make our code publicly available on GitHub https://github.com/dawoz/geolingit-biss2024.

Vision-Language Models (VLMs) have shown remarkable capabilities in a large number of downstream tasks. Nonetheless, compositional image understanding remains a rather difficult task due to the object bias present in training data. In this work, we investigate the reasons for such a lack of capability by performing an extensive bench-marking of compositional understanding in VLMs. We compare contrastive models with generative ones and analyze their differences in architecture, pre-training data, and training tasks and losses. Furthermore, we leverage In-Context Learning (ICL) as a way to improve the ability of VLMs to perform more complex reasoning and understanding given an image. Our extensive experiments demonstrate that our proposed approach outperforms baseline models across multiple compositional understanding datasets.

AI-Driven Fast and Early Detection of IoT Botnet Threats: A Comprehensive Network Traffic Analysis Approach Abdelaziz Amara korba 1,2, Aleddine Diaf 1, and Y acine Ghamri-Doudane 2 1 LRS, Badji Mokhtar University of Annaba, Algeria 2 L3I, University of La Rochelle, France Abstract --In the rapidly evolving landscape of cyber threats targeting the Internet of Things (IoT) ecosystem, and in light of the surge in botnet-driven Distributed Denial of Service (DDoS) and brute force attacks, this study focuses on the early detection of IoT bots. It specifically addresses the detection of stealth bot communication that precedes and orchestrates attacks. This study proposes a comprehensive methodology for analyzing IoT network traffic, including considerations for both unidirectional and bidirectional flow, as well as packet formats. It explores a wide spectrum of network features critical for representing network traffic and characterizing benign IoT traffic patterns effectively. Moreover, it delves into the modeling of traffic using various semi-supervised learning techniques. Through extensive experimentation with the IoT -23 dataset--a comprehensive collection featuring diverse botnet types and traffic scenarios--we have demonstrated the feasibility of detecting botnet traffic corresponding to different operations and types of bots, specifically focusing on stealth command and control (C2) communications.The results obtained have demonstrated the feasibility of identifying C2 communication with a 100% success rate through packet-based methods and 94% via flow-based approaches, with a false positive rate of 1.53%.

As scaling laws in generative AI push performance, they also simultaneously concentrate the development of these models among actors with large computational resources. With a focus on text-to-image (T2I) generative models, we aim to address this bottleneck by demonstrating very low-cost training of large-scale T2I diffusion transformer models. As the computational cost of transformers increases with the number of patches in each image, we propose to randomly mask up to 75% of the image patches during training. We propose a deferred masking strategy that preprocesses all patches using a patch-mixer before masking, thus significantly reducing the performance degradation with masking, making it superior to model downscaling in reducing computational cost. We also incorporate the latest improvements in transformer architecture, such as the use of mixture-of-experts layers, to improve performance and further identify the critical benefit of using synthetic images in micro-budget training. Finally, using only 37M publicly available real and synthetic images, we train a 1.16 billion parameter sparse transformer with only \$1,890 economical cost and achieve a 12.7 FID in zero-shot generation on the COCO dataset. Notably, our model achieves competitive FID and high-quality generations while incurring 118$\times$ lower cost than stable diffusion models and 14$\times$ lower cost than the current state-of-the-art approach that costs \$28,400. We aim to release our end-to-end training pipeline to further democratize the training of large-scale diffusion models on micro-budgets.

Over summer 2024, the world will be looking at Paris to encourage their favorite athletes win the Olympic gold medal. In handball, few nations will fight hard to win the precious metal with speculations predicting the victory for France or Denmark for men and France or Norway for women. However, there is so far no scientific method proposed to predict the final results of the competition. In this work, we leverage a deep learning model to predict the results of the handball tournament of the 2024 Olympic Games. This model, coupled with explainable AI (xAI) techniques, allows us to extract insightful information about the main factors influencing the outcome of each match. Notably, xAI helps sports experts understand how factors like match information or individual athlete performance contribute to the predictions. Furthermore, we integrate Large Language Models (LLMs) to generate human-friendly explanations that highlight the most important factors impacting the match results. By providing human-centric explanations, our approach offers a deeper understanding of the AI predictions, making them more actionable for coaches and analysts.

Coalition formation is a key capability in multi-agent systems. An important problem in coalition formation is coalition structure generation: partitioning agents into coalitions to optimize the social welfare. This is a challenging problem that has been the subject of active research for the past three decades. In this paper, we present a novel algorithm, SMART, for the problem based on a hybridization of three innovative techniques. Two of these techniques are based on dynamic programming, where we show a powerful connection between the coalitions selected for evaluation and the performance of the algorithms. These algorithms use offline phases to optimize the choice of coalitions to evaluate. The third one uses branch-and-bound and integer partition graph search to explore the solution space. Our techniques bring a new way of approaching the problem and a new level of precision to the field. In experiments over several common value distributions, we show that the hybridization of these techniques in SMART is faster than the fastest prior algorithms (ODP-IP, BOSS) in generating optimal solutions across all the value distributions.