Generative AI is rapidly transforming the art world, creating significant tensions not only between artists and model creators but also among artists. These tools are capable of producing almost instantaneous art-like outputs on an unprecedented scale, which is changing the means of production not only for many artists but also for consumers of creative outputs. While some celebrate Generative AI for its potential to enhance their processes and democratize creativity, making artistic expression accessible to more people, others criticize its ethical implications. This tension highlights the interplay between technological innovation and the rights of creators in this complex socio-technical system. A major concern in this context is the exploitation of artists, whose original works are often used as training data without proper credit or compensation and who now have to compete with Generative AI art models that are hyper-efficient, inexpensive digital twins of their past selves.

The field of affective computing has seen significant advancements in exploring the relationship between emotions and emerging technologies. This paper presents a novel and valuable contribution to this field with the introduction of a comprehensive French multimodal dataset designed specifically for emotion recognition. The dataset encompasses three primary modalities: facial expressions, speech, and gestures, providing a holistic perspective on emotions. Moreover, the dataset has the potential to incorporate additional modalities, such as Natural Language Processing (NLP) to expand the scope of emotion recognition research. The dataset was curated through engaging participants in card game sessions, where they were prompted to express a range of emotions while responding to diverse questions. The study included 10 sessions with 20 participants (9 females and 11 males). The dataset serves as a valuable resource for furthering research in emotion recognition and provides an avenue for exploring the intricate connections between human emotions and digital technologies.

The literature review is a crucial form of academic writing that involves complex processes of literature collection, organization, and summarization. The emergence of large language models (LLMs) has introduced promising tools to automate these processes. However, their actual capabilities in writing comprehensive literature reviews remain underexplored, such as whether they can generate accurate and reliable references. To address this gap, we propose a framework to assess the literature review writing ability of LLMs automatically. We evaluate the performance of LLMs across three tasks: generating references, writing abstracts, and writing literature reviews. We employ external tools for a multidimensional evaluation, which includes assessing hallucination rates in references, semantic coverage, and factual consistency with human-written context. By analyzing the experimental results, we find that, despite advancements, even the most sophisticated models still cannot avoid generating hallucinated references. Additionally, different models exhibit varying performance in literature review writing across different disciplines.

Federated Learning (FL) has emerged as a powerful collaborative learning paradigm where multiple clients jointly train a machine learning model without sharing their local data. In the classical FL setting, a central server coordinates multiple clients (e.g., mobile devices, edge devices) to collaboratively learn a shared global model without exchanging their own training data [48, 54, 79, 64]. In this scenario, each client performs local computations on its private data and periodically communicates model updates to the server, which aggregates them to update the global model. This collaborative approach has been successfully applied in various domains, including natural language processing [69, 43], computer vision [70, 63], and healthcare [4, 116]. Despite their success, traditional FL frameworks rely on the key assumption that all participants are fully cooperative and share aligned objectives, collectively working towards optimizing the performance of a shared global model (e.g., minimizing the average of individual loss functions). This assumption overlooks situations where participants have individual objectives, or competitive interests that may not align with the collective goal. Diverse examples of such scenarios have been extensively considered in the game theory literature, including Cournot competition in economics [2], optical networks [91], electricity markets [98], energy consumption control in smart grid [120], or mobile robot control [49]. Despite their relevance, these applications have yet to be associated with FL, presenting an unexplored opportunity to bridge game theory and FL for more robust and realistic frameworks.

Wang et al., 2024b; Wu et al., 2024; Zhang et al., 2024b), as Recent studies have revealed significant shown in Figure 1. Initial successes in LLM-based human gaps between LLM-based human simulations and simulations have been demonstrated across diverse fields, real-world observations, highlighting these dual including society, economics, policy, and psychology (Chen challenges. To address these gaps, we present a et al., 2024a; Li et al., 2024b;f; Lin et al., 2023; Park et al., comprehensive analysis of LLM limitations and 2023b; Yang et al., 2024b). Moreover, reliable LLM simulations our design issues, proposing targeted solutions can generate high-quality data for LLM training for both aspects. Furthermore, we explore future (Tang et al., 2024; Zhang et al., 2024a) and evaluate data directions that address both challenges simultaneously, quality (Chiang et al., 2024; Moniri et al., 2024; Xu et al., particularly in data collection, LLM generation, 2023b; Zheng et al., 2023b), serving as a data generator and and evaluation. To support further research evaluator (Gu et al., 2024; Li et al., 2024c; Son et al., 2024) in this field, we provide a curated collection of to enhance LLM pre-training and simulation abilities.

In human-robot teams, human situational awareness is the operator's conscious knowledge of the team's states, actions, plans and their environment. Appropriate human situational awareness is critical to successful human-robot collaboration. In human-robot teaming, it is often assumed that the best and required level of situational awareness is knowing everything at all times. This view is problematic, because what a human needs to know for optimal team performance varies given the dynamic environmental conditions, task context and roles and capabilities of team members. We explore this topic by interviewing 16 participants with active and repeated experience in diverse human-robot teaming applications. Based on analysis of these interviews, we derive a framework explaining the dynamic nature of required situational awareness in human-robot teaming. In addition, we identify a range of factors affecting the dynamic nature of required and actual levels of situational awareness (i.e., dynamic situational awareness), types of situational awareness inefficiencies resulting from gaps between actual and required situational awareness, and their main consequences. We also reveal various strategies, initiated by humans and robots, that assist in maintaining the required situational awareness. Our findings inform the implementation of accurate estimates of dynamic situational awareness and the design of user-adaptive human-robot interfaces. Therefore, this work contributes to the future design of more collaborative and effective human-robot teams.

In this position paper, we review the eclectic recent history of academic and artistic works involving computational systems for humor generation, and focus specifically on live performance. We make the case that AI comedy should be evaluated in live conditions, in front of audiences sharing either physical or online spaces, and under real-time constraints. We further suggest that improvised comedy is therefore the perfect substrate for deploying and assessing computational humor systems. Using examples of successful AI-infused shows, we demonstrate that live performance raises three sets of challenges for computational humor generation: 1) questions around robotic embodiment, anthropomorphism and competition between humans and machines, 2) questions around comedic timing and the nature of audience interaction, and 3) questions about the human interpretation of seemingly absurd AI-generated humor. We argue that these questions impact the choice of methodologies for evaluating computational humor, as any such method needs to work around the constraints of live audiences and performance spaces. These interrogations also highlight different types of collaborative relationship of human comedians towards AI tools.

Human Activity Recognition (HAR) has gained significant importance with the growing use of sensor-equipped devices and large datasets. This paper evaluates the performance of three categories of models : classical machine learning, deep learning architectures, and Restricted Boltzmann Machines (RBMs) using five key benchmark datasets of HAR (UCI-HAR, OPPORTUNITY, PAMAP2, WISDM, and Berkeley MHAD). We assess various models, including Decision Trees, Random Forests, Convolutional Neural Networks (CNN), and Deep Belief Networks (DBNs), using metrics such as accuracy, precision, recall, and F1-score for a comprehensive comparison. The results show that CNN models offer superior performance across all datasets, especially on the Berkeley MHAD. Classical models like Random Forest do well on smaller datasets but face challenges with larger, more complex data. RBM-based models also show notable potential, particularly for feature learning. This paper offers a detailed comparison to help researchers choose the most suitable model for HAR tasks.

Unlocking the potential of Large Language Models (LLMs) in data classification represents a promising frontier in natural language processing. In this work, we evaluate the performance of different LLMs in comparison with state-of-the-art deep-learning and machine-learning models, in two different classification scenarios: i) the classification of employees' working locations based on job reviews posted online (multiclass classification), and 2) the classification of news articles as fake or not (binary classification). Our analysis encompasses a diverse range of language models differentiating in size, quantization, and architecture. We explore the impact of alternative prompting techniques and evaluate the models based on the weighted F1-score. Also, we examine the trade-off between performance (F1-score) and time (inference response time) for each language model to provide a more nuanced understanding of each model's practical applicability. Our work reveals significant variations in model responses based on the prompting strategies. We find that LLMs, particularly Llama3 and GPT-4, can outperform traditional methods in complex classification tasks, such as multiclass classification, though at the cost of longer inference times. In contrast, simpler ML models offer better performance-to-time trade-offs in simpler binary classification tasks.

In this paper we introduce Keras Sig a high-performance pythonic library designed to compute path signature for deep learning applications. Entirely built in Keras 3, \textit{Keras Sig} leverages the seamless integration with the mostly used deep learning backends such as PyTorch, JAX and TensorFlow. Inspired by Kidger and Lyons (2021),we proposed a novel approach reshaping signature calculations to leverage GPU parallelism. This adjustment allows us to reduce the training time by 55\% and 5 to 10-fold improvements in direct signature computation compared to existing methods, while maintaining similar CPU performance. Relying on high-level tensor operations instead of low-level C++ code, Keras Sig significantly reduces the versioning and compatibility issues commonly encountered in deep learning libraries, while delivering superior or comparable performance across various hardware configurations. We demonstrate through extensive benchmarking that our approach scales efficiently with the length of input sequences and maintains competitive performance across various signature parameters, though bounded by memory constraints for very large signature dimensions.