Ironic identification is a challenging task in Natural Language Processing, particularly when dealing with languages that differ in syntax and cultural context. In this work, we aim to detect irony in Urdu by translating an English Ironic Corpus into the Urdu language. We evaluate ten state-of-the-art machine learning algorithms using GloVe and Word2Vec embeddings, and compare their performance with classical methods. Additionally, we fine-tune advanced transformer-based models, including BERT, RoBERTa, LLaMA 2 (7B), LLaMA 3 (8B), and Mistral, to assess the effectiveness of large-scale models in irony detection. Among machine learning models, Gradient Boosting achieved the best performance with an F1-score of 89.18%. Among transformer-based models, LLaMA 3 (8B) achieved the highest performance with an F1-score of 94.61%. These results demonstrate that combining transliteration techniques with modern NLP models enables robust irony detection in Urdu, a historically low-resource language.

In this paper, we propose AUREXA-SE (Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement), a progressive bimodal framework tailored for audio-visual speech enhancement (AVSE). AUREXA-SE jointly leverages raw audio waveforms and visual cues by employing a U-Net-based 1D convolutional encoder for audio and a Swin Transformer V2 for efficient and expressive visual feature extraction. Central to the architecture is a novel bidirectional cross-attention mechanism, which facilitates deep contextual fusion between modalities, enabling rich and complementary representation learning. To capture temporal dependencies within the fused embeddings, a stack of lightweight Squeezeformer blocks combining convolutional and attention modules is introduced. The enhanced embeddings are then decoded via a U-Net-style decoder for direct waveform reconstruction, ensuring perceptually consistent and intelligible speech output. Experimental evaluations demonstrate the effectiveness of AUREXA-SE, achieving significant performance improvements over noisy baselines, with STOI of 0.516, PESQ of 1.323, and SI-SDR of -4.322 dB. The source code of AUREXA-SE is available at https://github.com/mtanveer1/AVSEC-4-Challenge-2025.

Large Language Models (LLMs) are increasingly deployed for task automation and content generation, yet their safety mechanisms remain vulnerable to circumvention through different jailbreaking techniques. In this paper, we introduce \textit{Content Concretization} (CC), a novel jailbreaking technique that iteratively transforms abstract malicious requests into concrete, executable implementations. CC is a two-stage process: first, generating initial LLM responses using lower-tier, less constrained safety filters models, then refining them through higher-tier models that process both the preliminary output and original prompt. We evaluate our technique using 350 cybersecurity-specific prompts, demonstrating substantial improvements in jailbreak Success Rates (SRs), increasing from 7\% (no refinements) to 62\% after three refinement iterations, while maintaining a cost of 7.5\textcent~per prompt. Comparative A/B testing across nine different LLM evaluators confirms that outputs from additional refinement steps are consistently rated as more malicious and technically superior. Moreover, manual code analysis reveals that generated outputs execute with minimal modification, although optimal deployment typically requires target-specific fine-tuning. With eventual improved harmful code generation, these results highlight critical vulnerabilities in current LLM safety frameworks.

The last night of tech mogul Mike Lynch's life has become fodder for conspiracy theories. For the first time, the whole story can be told. In the predawn hours of August 19, 2024, bolts of lightning began to fork through the purple-black clouds above the Mediterranean. From the rail of a 184-foot vessel, a 22-year-old named Matthew Griffiths took out his phone to record a video. The British deckhand was just a week and a half into his first official yacht job, and he wasn't on just any boat. The yacht, the $40 million, was a star of the superyacht world, considered to be a feat of minimal design and precision engineering. As thunder rolled toward the anchored vessel, Griffiths set the video to AC/DC's "Thunderstruck" and posted it to Instagram. In the video, the's aluminum mast, one of the tallest in the world, is briefly visible against the roiling sky. Below deck, the yacht's owner, Michael Lynch, had every reason to be sleeping soundly. The boat trip had been organized as a celebration. Months earlier, Lynch had walked out of a San Francisco federal courthouse a free man, acquitted of all charges in one of the largest fraud cases in Silicon Valley history. Lynch had built his fortune on understanding probability, on turning the unlikely into the possible. He had named his yacht in honor of the statistical theorem that made him a billionaire, after the sale, in 2011, of his company Autonomy. The British tech giant sold software that could find meaningful signals amid the flood of unstructured data in emails, videos, and phone calls, but it would be better known as the company that allegedly defrauded, and nearly destroyed, Hewlett-Packard. The cabins aboard the contained the people who had stood by Lynch through his 13-year-long legal ordeal. Beside him in the master suite was his wife of 22 years, Angela Bacares, a former vice president in the investment division of Deutsche Bank who had caught his eye while working an Autonomy deal. Other cabins housed the Clifford Chance attorneys who had orchestrated Lynch's legal victory, as well as longtime colleagues, their partners, and a 1-year-old baby, all supported by 10 crew members. Also onboard was Lynch's younger daughter, Hannah, 18, who was about to begin her studies at Oxford.

The presence of post-stroke grasping deficiencies highlights the critical need for the development and implementation of advanced compensatory strategies. This paper introduces a novel system to aid chronic stroke survivors through the development of a soft, vision-based, tactile-enabled extra robotic finger. By incorporating vision-based tactile sensing, the system autonomously adjusts grip force in response to slippage detection. This synergy not only ensures mechanical stability but also enriches tactile feedback, mimicking the dynamics of human-object interactions. At the core of our approach is a transformer-based framework trained on a comprehensive tactile dataset encompassing objects with a wide range of morphological properties, including variations in shape, size, weight, texture, and hardness. Furthermore, we validated the system's robustness in real-world applications, where it successfully manipulated various everyday objects. The promising results highlight the potential of this approach to improve the quality of life for stroke survivors.

In this work, a large area, low cost and flexible polymer/paper-based double touch mode capacitive pressure sensor is demonstrated. Garage fabrication processes are used which only require cutting, taping and assembly of aluminum (Al) coated polyimide (PI) foil, PI tape and double-sided scotch tape. The presented pressure sensor operates in different pressure regions i.e. normal (0 to 7.5 kPa), transition (7.5 to 14.24 kPa), linear (14.24 to 54.9 kPa) and saturation (above 54.9 kPa). The advantages of the demonstrated double touch mode capacitive pressure sensors are low temperature drift, long linear range, high pressure sensitivity, precise pressure measurement and large die area. The linear output along with a high sensitivity range (14.24 to 54.9 kPa pressure range) of the sensor are utilized to wirelessly control the movement of a robotic arm with precise rotation and tilt movement capabilities.

Capacitive pressure sensors have several advantages in areas such as robotics, automation, aerospace, biomedical and consumer electronics. We present mathematical modelling, finite element analysis (FEA), fabrication and experimental characterization of ultra-low cost and paper-based, touch-mode, flexible capacitive pressure sensor element using Do-It-Yourself (DIY) technology. The pressure sensing element is utilized to design large-area electronics skin for low-cost prosthetic hands. The presented sensor is characterized in normal, transition, touch and saturation modes. The sensor has higher sensitivity and linearity in touch mode operation from 10 to 40 kPa of applied pressure compared to the normal (0 to 8 kPa), transition (8 to 10 kPa) and saturation mode (after 40 kPa) with response time of 15.85 ms. Advantages of the presented sensor are higher sensitivity, linear response, less diaphragm area, less von Mises stress at the clamped edges region, low temperature drift, robust structure and less separation gap for large pressure measurement compared to normal mode capacitive pressure sensors. The linear range of pressure change is utilized for controlling the position of a servo motor for precise movement in robotic arm using wireless communication, which can be utilized for designing skin-like structure for low-cost prosthetic hands.

In the early morning hours of May 15, the cargo vessel Borkum stopped off the Spanish coast, lingering in the waters a short distance from Cartagena. At the port, protesters waved Palestinian flags and called on authorities to inspect the ship based on suspicions that it carried weapons bound for Israel. Leftist members of the European Parliament sent a letter to Spanish President Pedro Sánchez requesting that the ship be prevented from docking. "Allowing a ship loaded with weapons destined for Israel is to allow the transit of arms to a country currently under investigation for genocide against the Palestinian people," the group of nine MEPs warned. Before the Spanish government could take a stand, the Borkum cancelled its planned stopover and continued to the Slovenian port of Koper.

Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT). A major limitation of existing procedures is not accounting for censoring, despite the abundance of RCTs and OSes that report right-censored time-to-event outcomes. We consider two cases where censoring time (1) is independent of time-to-event and (2) depends on time-to-event the same way in OS and RCT. For the former, we adopt a censoring-doubly-robust signal for the conditional average treatment effect (CATE) to facilitate an equivalence test of CATEs in OS and RCT, which serves as a proxy for testing if the validity assumptions hold. For the latter, we show that the same test can still be used even though unbiased CATE estimation may not be possible. We verify the effectiveness of our censoring-aware tests via semi-synthetic experiments and analyze RCT and OS data from the Women's Health Initiative study.

This paper proposes the MBURST, a novel multimodal solution for audio-visual speech enhancements that consider the most recent neurological discoveries regarding pyramidal cells of the prefrontal cortex and other brain regions. The so-called burst propagation implements several criteria to address the credit assignment problem in a more biologically plausible manner: steering the sign and magnitude of plasticity through feedback, multiplexing the feedback and feedforward information across layers through different weight connections, approximating feedback and feedforward connections, and linearizing the feedback signals. MBURST benefits from such capabilities to learn correlations between the noisy signal and the visual stimuli, thus attributing meaning to the speech by amplifying relevant information and suppressing noise. Experiments conducted over a Grid Corpus and CHiME3-based dataset show that MBURST can reproduce similar mask reconstructions to the multimodal backpropagation-based baseline while demonstrating outstanding energy efficiency management, reducing the neuron firing rates to values up to \textbf{$70\%$} lower. Such a feature implies more sustainable implementations, suitable and desirable for hearing aids or any other similar embedded systems.