Solving combinatorial optimization problems involve satisfying a set of hard constraints while optimizing some objectives. In this context, exact or approximate methods can be used. While exact methods guarantee the optimal solution, they often come with an exponential running time as opposed to approximate methods that trade the solutions quality for a better running time. In this context, we tackle the Nurse Scheduling Problem (NSP). The NSP consist in assigning nurses to daily shifts within a planning horizon such that workload constraints are satisfied while hospitals costs and nurses preferences are optimized. To solve the NSP, we propose implicit and explicit approaches. In the implicit solving approach, we rely on Machine Learning methods using historical data to learn and generate new solutions through the constraints and objectives that may be embedded in the learned patterns. To quantify the quality of using our implicit approach in capturing the embedded constraints and objectives, we rely on the Frobenius Norm, a quality measure used to compute the average error between the generated solutions and historical data. To compensate for the uncertainty related to the implicit approach given that the constraints and objectives may not be concretely visible in the produced solutions, we propose an alternative explicit approach where we first model the NSP using the Constraint Satisfaction Problem (CSP) framework. Then we develop Stochastic Local Search methods and a new Branch and Bound algorithm enhanced with constraint propagation techniques and variables/values ordering heuristics. Since our implicit approach may not guarantee the feasibility or optimality of the generated solution, we propose a data-driven approach to passively learn the NSP as a constraint network. The learned constraint network, formulated as a CSP, will then be solved using the methods we listed earlier.

People deeply care about how fairly they are treated by robots. The established paradigm for probing fairness in Human-Robot Interaction (HRI) involves measuring the perception of the fairness of a robot at the conclusion of an interaction. However, such an approach is limited as interactions vary over time, potentially causing changes in fairness perceptions as well. To validate this idea, we conducted a 2x2 user study with a mixed design (N=40) where we investigated two factors: the timing of unfair robot actions (early or late in an interaction) and the beneficiary of those actions (either another robot or the participant). Our results show that fairness judgments are not static. They can shift based on the timing of unfair robot actions. Further, we explored using perceptions of three key factors (reduced welfare, conduct, and moral transgression) proposed by a Fairness Theory from Organizational Justice to predict momentary perceptions of fairness in our study. Interestingly, we found that the reduced welfare and moral transgression factors were better predictors than all factors together. Our findings reinforce the idea that unfair robot behavior can shape perceptions of group dynamics and trust towards a robot and pave the path to future research directions on moment-to-moment fairness perceptions

Personalized Federated Learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy. However, achieving optimal performance in pFL often requires a careful balancing act between memory overhead costs and model accuracy. This paper delves into the trade-offs inherent in pFL, offering valuable insights for selecting the right algorithms for diverse real-world scenarios. We empirically evaluate ten prominent pFL techniques across various datasets and data splits, uncovering significant differences in their performance. Our study reveals interesting insights into how pFL methods that utilize personalized (local) aggregation exhibit the fastest convergence due to their efficiency in communication and computation. Conversely, fine-tuning methods face limitations in handling data heterogeneity and potential adversarial attacks while multi-objective learning methods achieve higher accuracy at the cost of additional training and resource consumption. Our study emphasizes the critical role of communication efficiency in scaling pFL, demonstrating how it can significantly affect resource usage in real-world deployments.

This paper presents Adaptive Meta-Domain Transfer Learning (AMDTL), a novel methodology that combines principles of meta-learning with domain-specific adaptations to enhance the transferability of artificial intelligence models across diverse and unknown domains. AMDTL aims to address the main challenges of transfer learning, such as domain misalignment, negative transfer, and catastrophic forgetting, through a hybrid framework that emphasizes both generalization and contextual specialization. The framework integrates a meta-learner trained on a diverse distribution of tasks, adversarial training techniques for aligning domain feature distributions, and dynamic feature regulation mechanisms based on contextual domain embeddings. Experimental results on benchmark datasets demonstrate that AMDTL outperforms existing transfer learning methodologies in terms of accuracy, adaptation efficiency, and robustness. This research provides a solid theoretical and practical foundation for the application of AMDTL in various fields, opening new perspectives for the development of more adaptable and inclusive AI systems.

Hallucinations is a major challenge for large language models (LLMs), prevents adoption in diverse fields. Uncertainty estimation could be used for alleviating the damages of hallucinations. The skeptical emotion of human could be useful for enhancing the ability of self estimation. Inspirited by this observation, we proposed a new approach called Skepticism Modeling (SM). This approach is formalized by combining the information of token and logits for self estimation. We construct the doubt emotion aware data, perform continual pre-training, and then fine-tune the LLMs, improve their ability of self estimation. Experimental results demonstrate this new approach effectively enhances a model's ability to estimate their uncertainty, and validate its generalization ability of other tasks by out-of-domain experiments.

We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have been proposed in the recent literature, of which each is subject to partially overlapping limitations. In particular, current text-based approaches are limited by manual feature engineering. Path-based and rule-based approaches are limited by their exclusive use of knowledge graphs as background knowledge, and embedding-based approaches suffer from low accuracy scores on current fact-checking tasks. We propose a hybrid approach -- dubbed HybridFC -- that exploits the diversity of existing categories of fact-checking approaches within an ensemble learning setting to achieve a significantly better prediction performance. In particular, our approach outperforms the state of the art by 0.14 to 0.27 in terms of Area Under the Receiver Operating Characteristic curve on the FactBench dataset. Our code is open-source and can be found at https://github.com/dice-group/HybridFC.

In this work, we apply state-of-the-art self-supervised learning techniques on a large dataset of seafloor imagery, \textit{BenthicNet}, and study their performance for a complex hierarchical multi-label (HML) classification downstream task. In particular, we demonstrate the capacity to conduct HML training in scenarios where there exist multiple levels of missing annotation information, an important scenario for handling heterogeneous real-world data collected by multiple research groups with differing data collection protocols. We find that, when using smaller one-hot image label datasets typical of local or regional scale benthic science projects, models pre-trained with self-supervision on a larger collection of in-domain benthic data outperform models pre-trained on ImageNet. In the HML setting, we find the model can attain a deeper and more precise classification if it is pre-trained with self-supervision on in-domain data. We hope this work can establish a benchmark for future models in the field of automated underwater image annotation tasks and can guide work in other domains with hierarchical annotations of mixed resolution.

With the rapid advancement of technologies like text-to-speech (TTS) and voice conversion (VC), detecting deepfake voices has become increasingly crucial. However, both academia and industry lack a comprehensive and intuitive benchmark for evaluating detectors. Existing datasets are limited in language diversity and lack many manipulations encountered in real-world production environments. To fill this gap, we propose VoiceWukong, a benchmark designed to evaluate the performance of deepfake voice detectors. To build the dataset, we first collected deepfake voices generated by 19 advanced and widely recognized commercial tools and 15 open-source tools. We then created 38 data variants covering six types of manipulations, constructing the evaluation dataset for deepfake voice detection. VoiceWukong thus includes 265,200 English and 148,200 Chinese deepfake voice samples. Using VoiceWukong, we evaluated 12 state-of-the-art detectors. AASIST2 achieved the best equal error rate (EER) of 13.50%, while all others exceeded 20%. Our findings reveal that these detectors face significant challenges in real-world applications, with dramatically declining performance. In addition, we conducted a user study with more than 300 participants. The results are compared with the performance of the 12 detectors and a multimodel large language model (MLLM), i.e., Qwen2-Audio, where different detectors and humans exhibit varying identification capabilities for deepfake voices at different deception levels, while the LALM demonstrates no detection ability at all. Furthermore, we provide a leaderboard for deepfake voice detection, publicly available at {https://voicewukong.github.io}.

To assess memorization and information leakage in models, Membership Inference Attacks (MIAs) aim to determine if a data point was part of a model's training set [1]. However, MIAs designed for pre-trained Large Language Models (LLMs) have been largely ineffective [2, 3]. This is primarily because these MIAs, originally developed for classification models, fail to account for the sequential nature of LLMs. Unlike classification models, which produce a single prediction, LLMs generate text token-by-token, adjusting each prediction based on the context of preceding tokens (i.e., prefix). Prior MIAs overlook token-level loss dynamics and the influence of prefixes on next-token predictability, which contributes to memorization.

Early detection of factory machinery malfunctions is crucial in industrial applications. In machine anomalous sound detection (ASD), different machines exhibit unique vibration-frequency ranges based on their physical properties. Meanwhile, the human auditory system is adept at tracking both temporal and spectral dynamics of machine sounds. Consequently, integrating the computational auditory models of the human auditory system with machine-specific properties can be an effective approach to machine ASD. We first quantified the frequency importances of four types of machines using the Fisher ratio (F-ratio). The quantified frequency importances were then used to design machine-specific non-uniform filterbanks (NUFBs), which extract the log non-uniform spectrum (LNS) feature. The designed NUFBs have a narrower bandwidth and higher filter distribution density in frequency regions with relatively high F-ratios. Finally, spectral and temporal modulation representations derived from the LNS feature were proposed. These proposed LNS feature and modulation representations are input into an autoencoder neural-network-based detector for ASD. The quantification results from the training set of the Malfunctioning Industrial Machine Investigation and Inspection dataset with a signal-to-noise (SNR) of 6 dB reveal that the distinguishing information between normal and anomalous sounds of different machines is encoded non-uniformly in the frequency domain. By highlighting these important frequency regions using NUFBs, the LNS feature can significantly enhance performance using the metric of AUC (area under the receiver operating characteristic curve) under various SNR conditions. Furthermore, modulation representations can further improve performance. Specifically, temporal modulation is effective for fans, pumps, and sliders, while spectral modulation is particularly effective for valves.