The increasing realism of synthetic speech, driven by advancements in text-to-speech models, raises ethical concerns regarding impersonation and disinformation. Audio watermarking offers a promising solution via embedding human-imperceptible watermarks into AI-generated audios. However, the robustness of audio watermarking against common/adversarial perturbations remains understudied. We present AudioMarkBench, the first systematic benchmark for evaluating the robustness of audio watermarking against watermark removal and watermark forgery. AudioMarkBench includes a new dataset created from Common-Voice across languages, biological sexes, and ages, 3 state-of-the-art watermarking methods, and 15 types of perturbations. We benchmark the robustness of these methods against the perturbations in no-box, black-box, and white-box settings. Our findings highlight the vulnerabilities of current watermarking techniques and emphasize the need for more robust and fair audio watermarking solutions. Our dataset and code are publicly available at \url{https://github.com/moyangkuo/AudioMarkBench}.

To leverage prediction models to make optimal scheduling decisions in service systems, we must understand how predictive errors impact congestion due to externalities on the delay of other jobs. Motivated by applications where prediction models interact with human servers (e.g., content moderation), we consider a large queueing system comprising of many single server queues where the class of a job is estimated using a prediction model. By characterizing the impact of mispredictions on congestion cost in heavy traffic, we design an index-based policy that incorporates the predicted class information in a near-optimal manner. Our theoretical results guide the design of predictive models by providing a simple model selection procedure with downstream queueing performance as a central concern, and offer novel insights on how to design queueing systems with AI-based triage. We illustrate our framework on a content moderation task based on real online comments, where we construct toxicity classifiers by finetuning large language models.

Since the era of deep learning, convolutional neural networks (CNNs) and vision transformers (ViTs) have been extensively studied and widely used in medical image classification tasks. Unfortunately, CNN's limitations in modeling long-range dependencies result in poor classification performances. In contrast, ViTs are hampered by the quadratic computational complexity of their self-attention mechanism, making them difficult to deploy in real-world settings with limited computational resources. Recent studies have shown that state space models (SSMs) represented by Mamba can effectively model long-range dependencies while maintaining linear computational complexity. Inspired by it, we proposed MedMamba, the first vision Mamba for generalized medical image classification. Concretely, we introduced a novel hybrid basic block named SS-Conv-SSM, which integrates the convolutional layers for extracting local features with the abilities of SSM to capture long-range dependencies, aiming to model medical images from different image modalities efficiently. By employing the grouped convolution strategy and channel-shuffle operation, MedMamba successfully provides fewer model parameters and a lower computational burden for efficient applications. To demonstrate the potential of MedMamba, we conducted extensive experiments using 16 datasets containing ten imaging modalities and 411,007 images. Experimental results show that the proposed MedMamba demonstrates competitive performance in classifying various medical images compared with the state-of-the-art methods. Our work is aims to establish a new baseline for medical image classification and provide valuable insights for developing more powerful SSM-based artificial intelligence algorithms and application systems in the medical field. The source codes and all pre-trained weights of MedMamba are available at https://github.com/YubiaoYue/MedMamba.

Chronic Kidney Disease (CKD) is one of the widespread Chronic diseases with no known ultimo cure and high morbidity. Research demonstrates that progressive Chronic Kidney Disease (CKD) is a heterogeneous disorder that significantly impacts kidney structure and functions, eventually leading to kidney failure. With the progression of time, chronic kidney disease has moved from a life-threatening disease affecting few people to a common disorder of varying severity. The goal of this research is to visualize dominating features, feature scores, and values exhibited for early prognosis and detection of CKD using ensemble learning and explainable AI. For that, an AI-driven predictive analytics approach is proposed to aid clinical practitioners in prescribing lifestyle modifications for individual patients to reduce the rate of progression of this disease. Our dataset is collected on body vitals from individuals with CKD and healthy subjects to develop our proposed AI-driven solution accurately. In this regard, blood and urine test results are provided, and ensemble tree-based machine-learning models are applied to predict unseen cases of CKD. Our research findings are validated after lengthy consultations with nephrologists. Our experiments and interpretation results are compared with existing explainable AI applications in various healthcare domains, including CKD. The comparison shows that our developed AI models, particularly the Random Forest model, have identified more features as significant contributors than XgBoost. Interpretability (I), which measures the ratio of important to masked features, indicates that our XgBoost model achieved a higher score, specifically a Fidelity of 98\%, in this metric and naturally in the FII index compared to competing models.

Bipartite networks serve as highly suitable models to represent systems involving interactions between two distinct types of entities, such as online dating platforms, job search services, or ecommerce websites. These models can be leveraged to tackle a number of tasks, including link prediction among the most useful ones, especially to design recommendation systems. However, if this task has garnered much interest when conducted on unipartite (i.e. standard) networks, it is far from being the case for bipartite ones. In this study, we address this gap by performing an experimental comparison of 19 link prediction methods able to handle bipartite graphs. Some come directly from the literature, and some are adapted by us from techniques originally designed for unipartite networks. We also propose to repurpose recommendation systems based on graph convolutional networks (GCN) as a novel link prediction solution for bipartite networks. To conduct our experiments, we constitute a benchmark of 3 real-world bipartite network datasets with various topologies. Our results indicate that GCN-based personalized recommendation systems, which have received significant attention in recent years, can produce successful results for link prediction in bipartite networks. Furthermore, purely heuristic metrics that do not rely on any learning process, like the Structural Perturbation Method (SPM), can also achieve success.

A popular approach to ensuring that probabilistic predictions from machine learning algorithms are meaningful is model calibration. Intuitively, calibration requires that amongst all samples given score p [0, 1] by an ML algorithm, exactly a p-fraction of those samples have positive label. Calibration ensures that a predictor has an accurate estimate of its own predictive uncertainty, and is a fundamental requirement in applications where probabilities may be taken into account for high-stake decisions such as disease diagnosis (Dahabreh et al., 2017) or credit/lending decisions (Bequé et al., 2017). Miscalibration can result in undesirable downstream consequences when probabilistic predictions are thresholded into decisions: if a predictor has high calibration error in disease diagnosis, for example, the individuals assigned lower predicted probabilities may be unfairly denied treatment. Calibration has a long history in the machine learning community (Guo et al., 2017; Minderer et al., 2021; Niculescu-Mizil and Caruana, 2005; Platt et al., 1999), but was arguably first introduced in fairness contexts by Cleary (1968). More recently, it has appeared in the algorithmic fairness community via the seminal works of Chouldechova (2017); Kleinberg et al. (2017). Although calibration ensures meaningful uncertainty estimates aggregated over the entire population, it does not preclude potential discrimination at the level of groups of individuals: a model may be well calibrated overall but systematically underestimate the risk or qualification probability on historically underrepresented subsets of individuals. For example, Obermeyer et al. (2019) show differing calibration error rates across groups defined by race for prediction in high-risk patient care management systems. As pointed out by Obermeyer et al. (2019), in the

Software-Defined Networks (SDN) are the standard architecture for network deployment. Intrusion Detection Systems (IDS) are a pivotal part of this technology as networks become more vulnerable to new and sophisticated attacks. Machine Learning (ML)-based IDS are increasingly seen as the most effective approach to handle this issue. However, IDS datasets suffer from high class imbalance, which impacts the performance of standard ML models. We propose Sequential Binary Classification (SBC) - an algorithm for multi-class classification to address this issue. SBC is a hierarchical cascade of base classifiers, each of which can be modelled on any general binary classifier. Extensive experiments are reported on benchmark datasets that evaluate the performance of SBC under different scenarios.

In autonomous driving, the detection of anomalies is crucial for ensuring safety and reliability. Video anomaly detection (VAD) focuses on identifying events in video data that deviate from an expected normality. In autonomous driving, the challenges of detection are complicated by factors such as camera movements, ever-changing backgrounds, and rapid changes in vehicle speed. Many different types of anomalies exist [1, 2, 3], with many approaches trying to detect them [4]. We can distinguish between five key techniques: Reconstruction, prediction, generative modeling, feature extraction, and confidence evaluation [5].

This work aims to reproduce the findings of the paper "Fair Attribute Completion on Graph with Missing Attributes" written by Guo, Chu, and Li arXiv:2302.12977 by investigating the claims made in the paper. This paper suggests that the results of the original paper are reproducible and thus, the claims hold. However, the claim that FairAC is a generic framework for many downstream tasks is very broad and could therefore only be partially tested. Moreover, we show that FairAC is generalizable to various datasets and sensitive attributes and show evidence that the improvement in group fairness of the FairAC framework does not come at the expense of individual fairness. Lastly, the codebase of FairAC has been refactored and is now easily applicable for various datasets and models.

Deep learners tend to perform well when trained under the closed set assumption but struggle when deployed under open set conditions. This motivates the field of Open Set Recognition in which we seek to give deep learners the ability to recognize whether a data sample belongs to the known classes trained on or comes from the surrounding infinite world. Existing open set recognition methods typically rely upon a single function for the dual task of distinguishing between knowns and unknowns as well as making known class distinction. This dual process leaves performance on the table as the function is not specialized for either task. In this work, we introduce Cascading Unknown Detection with Known Classification (Cas-DC), where we instead learn specialized functions in a cascading fashion for both known/unknown detection and fine class classification amongst the world of knowns. Our experiments and analysis demonstrate that Cas-DC handily outperforms modern methods in open set recognition when compared using AUROC scores and correct classification rate at various true positive rates.