Chicago's top prosecutor turns on woke judge who freed 72-time arrestee accused of setting devout Christian woman, 26, on fire on train What HAS happened to Beyoncé? Suddenly so desperate, I know what's really going on... and it's ugly: CAROLINE BULLOCK LIZ JONES: Sorry, but it's now time for Kate to stop making excuses Fani Willis' election interference case against Trump thrown out by Georgia prosecutor Teenager dragged from car'by migrant gang' and raped in front of her fiancé describes her night of hell and reveals they warned her'if you scream we'll kill you' Trump slams'ugly' female reporter behind NYT'hit piece' after'creepy' health rumors Joy Reid suggests JD Vance may dump'brown Hindu' wife Usha for'white queen' Erika Kirk to win 2028 election during vile chat with podcaster who laughed at Charlie's murder Here's the proof of a Democrat plot to make our troops pawns in their petty political war on Trump: LT COL DANIEL DAVIS Meghan Markle slammed for unsanitary mistake while preparing Thanksgiving turkey: 'It's basic knowledge' Karoline Leavitt's sister-in-law wrote VERY provocative Instagram post about stepson, 11, whose Brazilian mother has now been seized by ICE Marlo Thomas gives rare comment on how she's coping after loss of husband Phil Donahue a year after his death Troubled 350lb son of Hollywood icon is forced to humiliating new low... as his movie star brother luxuriates in $7m Montecito mansion Shocking reason brother of Anna Kepler'murder' suspect jumped out of moving car before Carnival cruise death horror Sir Richard Branson reveals his wife Joan died'quickly and painlessly' while in hospital for a back injury - as he says'life will never be the same' without his'shining star' The disgusting sex claims and'explicit videos' that allegedly expose World's Strongest Woman as'biological male' Human remains confirmed to be missing rodeo star, 25, who vanished in 2019 after argument with wife's family Calorie labels are a SHAM! Calorie labels on menus are pointless unless you're actively trying to lose weight, a study has found. Researchers have discovered that being told your beef pie is 1,362 calories or your cheeseburger is 2,133 calories makes no difference to what you order unless you're watching what you eat. It casts further doubt on the effectiveness of the government's policy, introduced in 2022, that ordered the use of calorie labels by all food outlets with more than 250 employees.

Class Activation Mapping (CAM) and its gradient-based variants (e.g., GradCAM) have become standard tools for explaining Convolutional Neural Network (CNN) predictions. However, these approaches typically focus on individual logits, while for neural networks using softmax, the class membership probability estimates depend \textit{only} on the \textit{differences} between logits, not on their absolute values. This disconnect leaves standard CAMs vulnerable to adversarial manipulation, such as passive fooling, where a model is trained to produce misleading CAMs without affecting decision performance. We introduce \textbf{Salience-Hoax Activation Maps (SHAMs)}, an \emph{entropy-aware form of passive fooling} that serves as a benchmark for CAM robustness under adversarial conditions. To address the passive fooling vulnerability, we then propose \textbf{DiffGradCAM}, a novel, lightweight, and contrastive approach to class activation mapping that is both non-suceptible to passive fooling, but also matches the output of standard CAM methods such as GradCAM in the non-adversarial case. Together, SHAM and DiffGradCAM establish a new framework for probing and improving the robustness of saliency-based explanations. We validate both contributions across multi-class tasks with few and many classes.

'ZERO CHANCE TO STOP IT' - Family sounds alarm on son's'sextortion' suicide after arrests of three Nigerian men. Continue reading … DICTATOR'S DILEMMA - Putin's major admission about Russian military as Ukraine launches counteroffensive. 'EXTREMELY ILLUMINATING' - Two questions Hannity asked Newsom make for an epic 2024, writes Chuck DeVore. HARROWING TESTIMONY - Senate urged to punish US companies that help China build its AI-driven'surveillance state.' DEAFENING SILENCE - Cruz accuses FBI official of'covering up' details of alleged Biden bribery scheme.

Autonomous agents operating in a dynamic environment must be able to reason and make decisions about actions in pursuit of their goals. In addition, in a normative environment an agent's actions are not only directed by the agent's goals, but also by the norms imposed on the agent. Practical reasoning is reasoning about what to do in a given situation, particularly in the presence of conflicts between the agent's practical attitude such as goals, plans and norms. In this thesis we aim: (i) to introduce a model for normative practical reasoning that allows the agents to plan for multiple and potentially conflicting goals and norms at the same time (ii) to implement the model both formally and computationally, (iii) to identify the best plan for the agent to execute by means of argumentation framework and grounded semantics, (iv) to justify the best plan via argumentation-based persuasion dialogue for grounded semantics.

Recent advances in deep learning have made available large, powerful convolutional neural networks (CNN) with state-of-the-art performance in several real-world applications. Unfortunately, these large-sized models have millions of parameters, thus they are not deployable on resource-limited platforms (e.g. where RAM is limited). Compression of CNNs thereby becomes a critical problem to achieve memory-efficient and possibly computationally faster model representations. In this paper, we investigate the impact of lossy compression of CNNs by weight pruning and quantization, and lossless weight matrix representations based on source coding. We tested several combinations of these techniques on four benchmark datasets for classification and regression problems, achieving compression rates up to $165$ times, while preserving or improving the model performance.

Rex Sham and his WALL-E-inspired robots are improving fire detection to slash carbon emissions and make the world better. If he were in a movie, Rex Sham would be the bad guy, or at the very least the well-meaning scientist who unwittingly wipes out the human race. In reality, Sham, the co-founder and chief science officer of Insight Robotics, is using his ingenious, WALL-E-like fire-detecting robots to save the planet. What Sham has developed is an automated early warning system that combines a high-precision, pan-tilt robot with thermal imaging sensors and advanced artificial intelligence (AI) vision technology. In its tests of the Computer Vision Wildfire Detection System, the Guangdong Academy of Forestry (Insight Robotics' research partner since 2010) has recorded a 100 per cent detection rate in multiple field trials and deployments.

There is no doubt that technology has made our lives better, convenient and easier. We are lucky to live in the era where the new emerging technologies like AR, VR and AI are offering an immersive digital experience through their products. From AI robot to unique VR headset, these creations are the next big thing in the tech world. Entrepreneur India spoke to few experts to know which sectors will be using AI in coming years. Distributing content to right people is of critical importance to attract high traffic.