People in Pensacola, Florida shared their thoughts on Cracker Barrel's new logo with Fox News Digital. Few things in American life have felt as trapped in the amber of history as Cracker Barrel restaurants, with their recipe of comfort food served up in cozy confines that evoke a bygone era. It's little wonder Americans routinely wait for an hour to get a table after church, or welcome a road-trip diversion when they see the classic logo on a highway sign. Now, the cracker-jack whiz-kid marketing team at the iconic eatery's corprate headquarters has decided to forgo all of this, including possibly, based on public reaction to their changes, the long lines. CRACKER BARREL UNVEILS NEW SIMPLIFIED LOGO: 'OUR STORY HASN'T CHANGED' This may not exactly be wokeness at work, as we have seen with so many brands such as Target and Bud Light, but it is something similarly lifeless and cold.

AI humanoid robots are stepping into showrooms to greet customers, explain features and pour coffee. Why settle for a regular robot when you can have a robot coyote? That's the innovative question the U.S. Army Engineer Research and Development Center (ERDC) is answering as it rolls out robot coyotes for airfield wildlife control. These cybernetic prairie predators are a creative solution to a very real problem. Sign up for my FREE CyberGuy Report Get my best tech tips, urgent security alerts and exclusive deals delivered straight to your inbox.

Breakthroughs, discoveries, and DIY tips sent every weekday. Sometimes, high-tech solutions aren't the best way to solve a problem. The US Army apparently came to that realization recently while exploring new methods to deter birds and other "problematic wildlife" from air bases. The military initially considered using Boston Dynamics' dog-like Spot robot to scare off the intruders, but they quickly realized it wasn't fast enough to effectively shoo the critters away. A far more effective--and affordable--solution presented itself in the form of three life-sized plastic coyote decoys mounted on top of toy-sized autonomous vehicles.

Former FBI special agent Nicole Parker joins'Fox & Friends First' to discuss why the U.S. is on'high alert' for Iranian threats inside the country after U.S. airstrikes on three nuclear sites. In times like this, you hear the concern from your neighbors. You talk about it with people at the gym. It's the topic of conversation over morning coffee -- from small towns to big cities -- "Are we going to see an increase in terror attacks here at home?" Now, there are news that Iranian "sleeper cells" pose a dangerous threat. Such cells could carry out attacks on U.S. citizens in retaliation for recent military operations in Iran, it's understandable that Americans are feeling concerned for their safety here at home.

Sparse attacks are to optimize the magnitude of adversarial perturbations for fooling deep neural networks (DNNs) involving only a few perturbed pixels (i.e., under the l0 constraint), suitable for interpreting the vulnerability of DNNs. However, existing solutions fail to yield interpretable adversarial examples due to their poor sparsity. Worse still, they often struggle with heavy computational overhead, poor transferability, and weak attack strength. In this paper, we aim to develop a sparse attack for understanding the vulnerability of CNNs by minimizing the magnitude of initial perturbations under the l0 constraint, to overcome the existing drawbacks while achieving a fast, transferable, and strong attack to DNNs. In particular, a novel and theoretical sound parameterization technique is introduced to approximate the NP-hard l0 optimization problem, making directly optimizing sparse perturbations computationally feasible. Besides, a novel loss function is designed to augment initial perturbations by maximizing the adversary property and minimizing the number of perturbed pixels simultaneously. Extensive experiments are conducted to demonstrate that our approach, with theoretical performance guarantees, outperforms state-of-the-art sparse attacks in terms of computational overhead, transferability, and attack strength, expecting to serve as a benchmark for evaluating the robustness of DNNs. In addition, theoretical and empirical results validate that our approach yields sparser adversarial examples, empowering us to discover two categories of noises, i.e., "obscuring noise" and "leading noise", which will help interpret how adversarial perturbation misleads the classifiers into incorrect predictions. Our code is available at https://github.com/fudong03/SparseAttack.

Network Intrusion Detection Systems (NIDS) play a vital role in protecting digital infrastructures against increasingly sophisticated cyber threats. In this paper, we extend ODXU, a Neurosymbolic AI (NSAI) framework that integrates deep embedded clustering for feature extraction, symbolic reasoning using XGBoost, and comprehensive uncertainty quantification (UQ) to enhance robustness, interpretability, and generalization in NIDS. The extended ODXU incorporates score-based methods (e.g., Confidence Scoring, Shannon Entropy) and metamodel-based techniques, including SHAP values and Information Gain, to assess the reliability of predictions. Experimental results on the CIC-IDS-2017 dataset show that ODXU outperforms traditional neural models across six evaluation metrics, including classification accuracy and false omission rate. While transfer learning has seen widespread adoption in fields such as computer vision and natural language processing, its potential in cybersecurity has not been thoroughly explored. To bridge this gap, we develop a transfer learning strategy that enables the reuse of a pre-trained ODXU model on a different dataset. Our ablation study on ACI-IoT-2023 demonstrates that the optimal transfer configuration involves reusing the pre-trained autoencoder, retraining the clustering module, and fine-tuning the XGBoost classifier, and outperforms traditional neural models when trained with as few as 16,000 samples (approximately 50% of the training data). Additionally, results show that metamodel-based UQ methods consistently outperform score-based approaches on both datasets.

Modern foundational models are often compressed via a combination of structured pruning and re-training to meet the strict compute, memory, and connectivity constraints of edge deployments. While state-of-the-art pruning schemes target the entire Transformer, we adopt a simple, layer-wise L2-norm pruning on only the MLP blocks as a fixed baseline. Our focus is not on achieving maximal compression, but on isolating the impact of the re-training loss function: (i) Fine-tuning with Cross- Entropy (L2PFT), which requires labeled data, versus (ii) Self-Distillation with KL-divergence, which leverages only teacher logits (no labels) (L2PSD). We evaluate both pipelines on the OLMo2- 7B-SFT model for CommonsenseQA suitable for intermittent or denied connectivity scenarios typical of edge networks. Under identical pruning schedules, KL-based distillation matches or exceeds CE fine-tuning in test accuracy, demonstrating that, even with a basic MLP-only pruning, the choice of loss function materially affects compressed model recovery in resource-constrained environments.

Breakthroughs, discoveries, and DIY tips sent every weekday. Drones can be a divisive subject, but they do have their uses (beyond causing mass panic). Professional unpiloted aerial vehicles (UAVs) are already instrumental in conservation efforts and engineering projects, but even personal use drones do more than recording cool aerial shots of your vacation. In the case of a recent emergency in Florida, one man's drone helped save a teenager's life. Earlier this month, amateur shark fisherman Andrew Smith was convinced by a friend to come with them to Pensacola Beach after getting off from work.

Hallucinations are inevitable in downstream tasks using large language models (LLMs). While addressing hallucinations becomes a substantial challenge for LLM-based ontology matching (OM) systems, we introduce a new benchmark dataset called OAEI-LLM-T. The dataset evolves from the TBox (i.e. schema-matching) datasets in the Ontology Alignment Evaluation Initiative (OAEI), capturing hallucinations of different LLMs performing OM tasks. These OM-specific hallucinations are carefully classified into two primary categories and six sub-categories. We showcase the usefulness of the dataset in constructing the LLM leaderboard and fine-tuning foundational LLMs for LLM-based OM systems.

Vision-Language Models (VLMs) have demonstrated impressive capabilities in learning joint representations of visual and textual data, making them powerful tools for tasks such as Compositional Zero-Shot Learning (CZSL). CZSL requires models to generalize to novel combinations of visual primitives--such as attributes and objects--that were not explicitly encountered during training. Recent works in prompting for CZSL have focused on modifying inputs for the text encoder, often using static prompts that do not change across varying visual contexts. However, these approaches struggle to fully capture varying visual contexts, as they focus on text adaptation rather than leveraging visual features for compositional reasoning. To address this, we propose Visual Adaptive Prompting System (VAPS) that leverages a learnable visual prompt repository and similarity-based retrieval mechanism within the framework of VLMs to bridge the gap between semantic and visual features. Our method introduces a dynamic visual prompt repository mechanism that selects the most relevant attribute and object prompts based on the visual features of the image. Our proposed system includes a visual prompt adapter that encourages the model to learn a more generalizable embedding space. Experiments on three CZSL benchmarks, across both closed and open-world scenarios, demonstrate state-of-the-art results.