Ultrasound (US) interpretation is hampered by multiplicative speckle, acquisition blur from the point-spread function (PSF), and scanner- and operator-dependent artifacts. Supervised enhancement methods assume access to clean targets or known degradations; conditions rarely met in practice. We present a blind, self-supervised enhancement framework that jointly deconvolves and denoises B-mode images using a Swin Convolutional U-Net trained with a \emph{physics-guided} degradation model. From each training frame, we extract rotated/cropped patches and synthesize inputs by (i) convolving with a Gaussian PSF surrogate and (ii) injecting noise via either spatial additive Gaussian noise or complex Fourier-domain perturbations that emulate phase/magnitude distortions. For US scans, clean-like targets are obtained via non-local low-rank (NLLR) denoising, removing the need for ground truth; for natural images, the originals serve as targets. Trained and validated on UDIAT~B, JNU-IFM, and XPIE Set-P, and evaluated additionally on a 700-image PSFHS test set, the method achieves the highest PSNR/SSIM across Gaussian and speckle noise levels, with margins that widen under stronger corruption. Relative to MSANN, Restormer, and DnCNN, it typically preserves an extra $\sim$1--4\,dB PSNR and 0.05--0.15 SSIM in heavy Gaussian noise, and $\sim$2--5\,dB PSNR and 0.05--0.20 SSIM under severe speckle. Controlled PSF studies show reduced FWHM and higher peak gradients, evidence of resolution recovery without edge erosion. Used as a plug-and-play preprocessor, it consistently boosts Dice for fetal head and pubic symphysis segmentation. Overall, the approach offers a practical, assumption-light path to robust US enhancement that generalizes across datasets, scanners, and degradation types.

Multimodal Artificial Intelligence (AI) systems, particularly Vision-Language Models (VLMs), have become integral to critical applications ranging from autonomous decision-making to automated document processing. As these systems scale, they rely heavily on preprocessing pipelines to handle diverse inputs efficiently. However, this dependency on standard preprocessing operations, specifically image downscaling, creates a significant yet often overlooked security vulnerability. While intended for computational optimization, scaling algorithms can be exploited to conceal malicious visual prompts that are invisible to human observers but become active semantic instructions once processed by the model. Current adversarial strategies remain largely static, failing to account for the dynamic nature of modern agentic workflows. To address this gap, we propose Chameleon, a novel, adaptive adversarial framework designed to expose and exploit scaling vulnerabilities in production VLMs. Unlike traditional static attacks, Chameleon employs an iterative, agent-based optimization mechanism that dynamically refines image perturbations based on the target model's real-time feedback. This allows the framework to craft highly robust adversarial examples that survive standard downscaling operations to hijack downstream execution. We evaluate Chameleon against Gemini 2.5 Flash model. Our experiments demonstrate that Chameleon achieves an Attack Success Rate (ASR) of 84.5% across varying scaling factors, significantly outperforming static baseline attacks which average only 32.1%. Furthermore, we show that these attacks effectively compromise agentic pipelines, reducing decision-making accuracy by over 45% in multi-step tasks. Finally, we discuss the implications of these vulnerabilities and propose multi-scale consistency checks as a necessary defense mechanism.

Deep neural networks (DNNs) have provided brilliant performance across various tasks. However, this success often comes at the cost of unnecessarily large model sizes, high computational demands, and substantial memory footprints. Typically, powerful architectures are trained at full depths but not all datasets or tasks require such high model capacity. Training big and deep architectures on relatively low-complexity datasets frequently leads to wasted computation, unnecessary energy consumption, and excessive memory usage, which in turn makes deployment of models on resource-constrained devices impractical. To address this problem, we introduce the concept of Optimally Deep Networks (ODNs), which provides a balance between model depth and task complexity. Specifically, we propose a NAS like training strategy called progressive depth expansion, which begins by training neural networks at shallower depths and incrementally increases their depth as the earlier blocks converge, continuing this process until the target accuracy is reached. ODNs use only the optimal depth for the tasks at hand, removing redundant layers. This cuts down future training and inference costs, lowers the model memory footprint, enhances computational efficiency, and facilitates deployment on edge devices. Empirical results show that the optimal depths of ResNet-18 and ResNet-34 for MNIST and SVHN, achieve up to 98.64 % and 96.44 % reduction in memory footprint, while maintaining a competitive accuracy of 99.31 % and 96.08 %, respectively.

Depth perception is crucial for spatial understanding and has traditionally been achieved through stereoscopic imaging. However, the precision of depth estimation using stereoscopic methods depends on the accurate calibration of binocular vision sensors. Monocular cameras, while more accessible, often suffer from reduced accuracy, especially under challenging imaging conditions. Optical sensors, too, face limitations in adverse environments, leading researchers to explore radar technology as a reliable alternative. Although radar provides coarse but accurate signals, its integration with fine-grained monocular camera data remains underexplored. In this research, we propose DepthSense, a novel radar-assisted monocular depth enhancement approach. DepthSense employs an encoder-decoder architecture, a Radar Residual Network, feature fusion with a spatial attention mechanism, and an ordinal regression layer to deliver precise depth estimations. We conducted extensive experiments on the nuScenes dataset to validate the effectiveness of DepthSense. Our methodology not only surpasses existing approaches in quantitative performance but also reduces parameter complexity and inference times. Our findings demonstrate that DepthSense represents a significant advancement over traditional stereo methods, offering a robust and efficient solution for depth estimation in autonomous driving. By leveraging the complementary strengths of radar and monocular camera data, DepthSense sets a new benchmark in the field, paving the way for more reliable and accurate spatial perception systems.

Advances in object tracking and acoustic beamforming are driving new capabilities in surveillance, human-computer interaction, and robotics. This work presents an embedded system that integrates deep learning-based tracking with beamforming to achieve precise sound source localization and directional audio capture in dynamic environments. The approach combines single-camera depth estimation and stereo vision to enable accurate 3D localization of moving objects. A planar concentric circular microphone array constructed with MEMS microphones provides a compact, energy-efficient platform supporting 2D beam steering across azimuth and elevation. Real-time tracking outputs continuously adapt the array's focus, synchronizing the acoustic response with the target's position. By uniting learned spatial awareness with dynamic steering, the system maintains robust performance in the presence of multiple or moving sources. Experimental evaluation demonstrates significant gains in signal-to-interference ratio, making the design well-suited for teleconferencing, smart home devices, and assistive technologies.

Current studies perceive LLMs' knowledge boundary

Many training data attribution (TDA) methods aim to estimate how a model's behavior would change if one or more data points were removed from the training set.

With more and more existing networks being transformed to Software-Defined Networking (SDN), they need to be more secure and demand smarter ways of traffic control. This work, SmartSecChain-SDN, is a platform that combines machine learning based intrusion detection, blockchain-based storage of logs, and application-awareness-based priority in SDN networks. To detect network intrusions in a real-time, precision and low-false positives setup, the framework utilizes the application of advanced machine learning algorithms, namely Random Forest, XGBoost, CatBoost, and CNN-BiLSTM. SmartSecChain-SDN is based on the Hyperledger Fabric, which is a permissioned blockchain technology, to provide secure, scalable, and privacy-preserving storage and, thus, guarantee that the Intrusion Detection System (IDS) records cannot be altered and can be analyzed comprehensively. The system also has Quality of Service (QoS) rules and traffic shaping based on applications, which enables prioritization of critical services, such as VoIP, video conferencing, and business applications, as well as de-prioritization of non-essential traffic, such as downloads and updates. Mininet can simulate real-time SDN scenarios because it is used to prototype whole architectures. It is also compatible with controllers OpenDaylight and Ryu. It has tested the framework using the InSDN dataset and proved that it can identify different kinds of cyberattacks and handle bandwidth allocation efficiently under circumstances of resource constraints. SmartSecChain-SDN comprehensively addresses SDN system protection, securing and enhancing. The proposed study offers an innovative, extensible way to improve cybersecurity, regulatory compliance, and the administration of next-generation programmable networks.

Story understanding and analysis have long been challenging areas within Natural Language Understanding. Automated narrative analysis requires deep computational semantic representations along with syntactic processing. Moreover, the large volume of narrative data demands automated semantic analysis and computational learning rather than manual analytical approaches. In this paper, we propose a framework that analyzes the sentiment arcs of movie scripts and performs extended analysis related to the context of the characters involved. The framework enables the extraction of high-level and low-level concepts conveyed through the narrative. Using dictionary-based sentiment analysis, our approach applies a custom lexicon built with the LabMTsimple storylab module. The custom lexicon is based on the Valence, Arousal, and Dominance scores from the NRC-VAD dataset. Furthermore, the framework advances the analysis by clustering similar sentiment plots using Wards hierarchical clustering technique. Experimental evaluation on a movie dataset shows that the resulting analysis is helpful to consumers and readers when selecting a narrative or story.