Prompt learning has emerged as an efficient alternative to full fine-tuning for adapting large audio-language models (ALMs) to downstream tasks.While this paradigm enables scalable deployment via Prompt-as-a-Service frameworks, it also introduces a critical yet underexplored security risk of backdoor attacks.In this work, we present TrojanWave, the first backdoor attack tailored to the prompt-learning setting in frozen ALMs.Unlike prior audio backdoor methods that require training from scratch on full datasets, TrojanWave injects backdoors solely through learnable prompts, making it highly scalable and effective in few-shot settings.TrojanWave injects imperceptible audio triggers in both time and spectral domains to effectively induce targeted misclassification during inference.To mitigate this threat, we further propose TrojanWave-Defense, a lightweight prompt purification method that neutralizes malicious prompts without hampering the clean performance.Extensive experiments across 11 diverse audio classification benchmarks demonstrate the robustness and practicality of both the attack and defense.Our code is publicly available at Github † .

Audio-Language Models (ALMs) have recently achieved success in zero-shot audio recognition tasks, which match features of audio waveforms with class-specific text prompt features, inspired by advancements in Vision-Language Models (VLMs).Given the sensitivity of zeroshot performance to the choice of hand-crafted text prompts, many prompt learning techniques have been developed for VLMs.We explore the efficacy of these approaches in ALMs and propose a novel method, Prompt Learning in Audio Language Models (PALM), which optimizes the feature space of the ALM text encoder.Unlike existing methods that work in the input space, our approach results in greater training efficiency.We demonstrate the effectiveness of our approach on 11 audio recognition datasets, encompassing a variety of speech-processing tasks, and compare the results with three baselines in a few-shot learning setup.Our results show that PALM performs on a par with or outperforms the baselines while being more computationally efficient.Our code is publicly available at Github † .

Propaganda is a form of communication intended to influence the opinions and the mindset of the public to promote a particular agenda.With the rise of social media, propaganda has spread rapidly, leading to the need for automatic propaganda detection systems.Most work on propaganda detection has focused on high-resource languages, such as English, and little effort has been made to detect propaganda for low-resource languages.Yet, it is common to find a mix of multiple languages in social media communication, a phenomenon known as code-switching.Code-switching combines different languages within the same text, which poses a challenge for automatic systems.Considering this premise, we propose a novel task of detecting propaganda techniques in codeswitched text.To support this task, we create a corpus of 1,030 texts code-switching between English and Roman Urdu, annotated with 20 propaganda techniques at the fragment level.We perform a number of experiments contrasting different experimental setups, and we find that it is important to model the multilinguality directly rather than using translation as well as to use the right fine-tuning strategy.The code and the dataset are publicly available at https://github.com/mbzuai-nlp/ propaganda-codeswitched-text

Fault diagnosis in industrial facilities has traditionally been done using rule-based approaches, heuristics or expert-knowledge. Bayesian network provides a flexible and data-driven alternative that can reason under uncertainty. Most of the data being generated by sensors in industrial setups are continuous and the underlying data-generating models are essentially non-linear. This paper employs Bayesian network and proposes a framework that learns parameters of probability density functions of a continuous Bayesian network using neural network/s without requiring assumption of linear Gaussian model or discretization of continuous data. Moreover, an expression of probability query using learned parametric density functions and causal-inference based mathematical formulation of two tasks related to fault diagnosis –in the context of industrial plants– namely root-cause-analysis and identification of most-influential-path in Bayesian network have been provided.

In light of increasing interest in detection of concealed metallic weapons, there is a great need to have robust and non-obtrusive metal detection systems with large coverage areas. Conventional systems based on electromagnetic induction or X- rays are effective, but have small coverage areas in addition to requiring costly infrastructure. In this paper, we explore the use of ubiquitously present WiFi signals for non-obtrusive detection of concealed metal objects. For the purpose, we build a prototype system consisting of a single- antenna commodity WiFi radio as a transmitter, and two multi-antenna radios as receivers placed in an indoor environment of approximately 42 ft by 39 ft. We conduct extensive experiments with subjects walking through the setup with (or without) a sheet of metal placed around their chests. We use the channel state-information collected from the receivers to train a deep convolutional neural network, and find that the proposed system can differentiate between the metal and non-metal cases with an average accuracy of 86.44.