Web Application Firewalls are crucial for protecting web applications against a wide range of cyber threats. Traditional Web Application Firewalls often struggle to effectively distinguish between malicious and legitimate traffic, leading to limited efficacy in threat detection. To overcome these limitations, this paper proposes an Adaptive Dual-Layer WAF employing a two-layered Machine Learning model designed to enhance the accuracy of anomaly and threat detection. The first layer employs a Decision Tree (DT) algorithm to detect anomalies by identifying traffic deviations from established normal patterns. The second layer employs Support Vector Machine to classify these anomalies as either threat anomalies or benign anomalies. Our Adaptive Dual Layer WAF incorporates comprehensive data pre-processing and feature engineering techniques and has been thoroughly evaluated using five large benchmark datasets. Evaluation using these datasets shows that ADL WAF achieves a detection accuracy of 99.88% and a precision of 100%, significantly enhancing anomaly detection and reducing false positives. These findings suggest that integrating machine learning techniques into WAFs can substantially improve web application security by providing more accurate and efficient threat detection.

DL based evidence prediction with uncertainty anchor sample identification.

Ideally, we would prefer an estimator that learns any distribution.

As large language models (LLMs) scale up, accuracy improves, but the autoregressive (AR) nature of decoding increases latency since each token requires a serial forward pass. Speculative decoding addresses this by employing a fast drafter to propose multi-token drafts, which are then verified in parallel by the target model. However, many deployments still rely on AR drafters, where sequential passes limit wall-clock gains. We revisit the drafting stage and present DiffuSpec, a training-free drop-in framework that uses a pretrained diffusion language model (DLM) to produce multi-token drafts in a single forward pass, while remaining compatible with standard AR verifiers. Because DLM drafts are generated under bidirectional conditioning, parallel per-position candidates form a token lattice in which the locally highest-probability token at each position need not form a causal left-to-right path. Moreover, DLM drafting requires pre-specifying a draft length, inducing a speed-quality trade-off. To address these challenges, we introduce two practical components: (i) a causal-consistency path search (CPS) over this lattice that extracts a left-to-right path aligned with AR verification; and (ii) an adaptive draft-length (ADL) controller that adjusts next proposal size based on recent acceptance feedback and realized generated length. Across benchmarks, DiffuSpec yields up to 3x wall-clock speedup, establishing diffusion-based drafting as a robust alternative to autoregressive drafters for speculative decoding.

Ideally, we would prefer an estimator that learns any distribution.

DL based evidence prediction with uncertainty anchor sample identification.

There are numerous frameworks capable of creating and orchestrating agents to address complex tasks. However, most of them highly coupled Python programming with agent declaration, making it hard for maintenance and runtime optimization. In this work, we introduce ADL, an agent declarative language for customer service chatbots. ADL abstracts away implementation details, offering a declarative way to define agents and their interactions, which could ease maintenance and debugging. It also incorporates natural language programming at its core to simplify the specification and communication of chatbot designs. ADL includes four basic types of agents and supports integration with custom functions, tool use, and third-party agents. MICA, a multi-agent system designed to interpret and execute ADL programs, has been developed and is now available as an open-source project at https://github.com/Mica-labs/MICA. Its documentation can be found at https://mica-labs.github.io/.

We study the problem of named entity recognition (NER) based on demonstration learning in low-resource scenarios. We identify two issues in demonstration construction and model training . Firstly, existing methods for selecting demonstration examples primarily rely on semantic similarity; We show that feature similarity can provide significant performance improvement. Secondly, we show that the NER tagger's ability to reference demonstration examples is generally inadequate. We propose a demonstration and training approach that effectively addresses these issues. For the first issue, we propose to select examples by dual similarity, which comprises both semantic similarity and feature similarity. For the second issue, we propose to train an NER model with adversarial demonstration such that the model is forced to refer to the demonstrations when performing the tagging task. We conduct comprehensive experiments in low-resource NER tasks, and the results demonstrate that our method outperforms a range of methods.

UPenn Wharton School Associate Professor Ethan Mollick weighs in on the Biden White House's new guidelines for artificial intelligence in the workplace on'Fox News Live.' Fox News' "Antisemitism Exposed" newsletter brings you stories on the rising anti-Jewish prejudice across the U.S. and the world. IN TODAY'S NEWSLETTER: - ADL issues'urgent call' alleging anti-Israel bias in 4 AI large language models - Georgetown grad student accused of spreading Hamas propaganda - Israeli hostages' families sue Mahmoud Khalil, Columbia organizers as alleged'Hamas' propaganda arm' The ADL's report found that virtually all artifical intelligence tools displayed a built-in bias against Israel and Jews. TOP STORY: A new report from the Anti-Defamation League (ADL) shows anti-Jewish and anti-Israel biases among AI large language models. The organization used thousands of AI queries to find "a concerning inability to accurately reject antisemitic tropes and conspiracy theories." Additionally, every LLM except GPT showed bias regarding Jewish conspiracy theories and even more bias against Israel than Jews, the ADL said.

Washington, DC – After Elon Musk made an apparent Nazi salute at an inauguration rally for United States President Donald Trump, the Anti-Defamation League (ADL) rushed to defend the SpaceX founder. The self-described anti-Semitism watchdog and "leading anti-hate organization in the world" dismissed Musk's raised arm as "an awkward gesture in a moment of enthusiasm" in a social media post on Monday. Months earlier, however, Jonathan Greenblatt, the head of the staunchly pro-Israel ADL, compared the Palestinian keffiyeh to the Nazi swastika. Activists say the contrast between the ADL's hurried defence of Musk and its efforts to demonise Palestinians and their supporters shows that the group is more focused on silencing voices critical of Israel than it is on fighting anti-Semitism. "The ADL is being crystal clear about where it stands," said Beth Miller, political director at Jewish Voice for Peace (JVP).