We first define the restricted Cheeger constant in the link prediction task. Then, according to Proposition 2.1, we have: Then, we can draw the same conclusion with Eq.12, and the Thus, Eq.16 can be simplified to: "sites" Based on the Eq.15 and Eq.17, we can rewrite L The inequality holds due to the assumption. Knowledge discovery: In the 5 random experiments, we add 500 pseudo links in each iteration. The metadata information of the nodes are all strongly relevant to "Linux" Both papers focus on the "malware"/"phishing" under the topic "Computer security". The detailed result of the case study is shown in Table 6.

Plus: Asus exits the smartphone market, and Sony partners with TCL on TVs. After delaying its Siri improvements to 2026, Apple's artificial intelligence plans are starting to take shape, at least according to the rumor mill. Bloomberg reports that Apple is turning Siri into a chatbot that will replace the voice assistant's existing interface, akin to OpenAI's ChatGPT. Codenamed Campos, the chatbot will be powered by Google's Gemini models and will be integrated into the iPhone, Mac, and iPad in their respective operating system updates later this fall. We'll likely learn more about Campos at Apple's developer event, WWDC, which usually takes place in June.

When you purchase through links in our articles, we may earn a small commission. Valve has spent the last decade tunneling into Microsoft's vault. Now, the heist is on. Microsoft's big focus for Windows is AI integration . Meanwhile, Valve has been not-so-quietly pilfering the entire PC gaming ecosystem from Microsoft, turning the Linux-based SteamOS into a real competitor to Windows.

Deep learning research for binary analysis faces a critical infrastructure gap. Today, existing datasets target single platforms, require specialized tooling, or provide only hand-engineered features incompatible with modern neural architectures; no single dataset supports accessible research and pedagogy on realistic use cases. To solve this, we introduce Binary-30K, the first heterogeneous binary dataset designed for sequence-based models like transformers. Critically, Binary-30K covers Windows, Linux, macOS, and Android across 15+ CPU architectures. With 29,793 binaries and approximately 26.93% malware representation, Binary-30K enables research on platform-invariant detection, cross-target transfer learning, and long-context binary understanding. The dataset provides pre-computed byte-level BPE tokenization alongside comprehensive structural metadata, supporting both sequence modeling and structure-aware approaches. Platform-first stratified sampling ensures representative coverage across operating systems and architectures, while distribution via Hugging Face with official train/validation/test splits enables reproducible benchmarking. The dataset is publicly available at https://huggingface.co/datasets/mjbommar/binary-30k, providing an accessible resource for researchers, practitioners, and students alike.

Different bitstring representations can yield varying computational performance. This work compares three bitstring implementations in C++: std::bitset, boost::dynamic_bitset, and a custom direct implementation. Their performance is benchmarked in the context of concatenation within a Linear Genetic Programming system. Benchmarks were conducted on three platforms (macOS, Linux, and Windows MSYS2) to assess platform specific performance variations. The results show that the custom direct implementation delivers the fastest performance on Linux and Windows, while std::bitset performs best on macOS. Although consistently slower, boost::dynamic_bitset remains a viable and flexible option. These findings highlight the influence of compiler optimisations and system architecture on performance, providing practical guidance for selecting the optimal method based on platform and application requirements.

We first define the restricted Cheeger constant in the link prediction task. Then, according to Proposition 2.1, we have: Then, we can draw the same conclusion with Eq.12, and the Thus, Eq.16 can be simplified to: "sites" Based on the Eq.15 and Eq.17, we can rewrite L The inequality holds due to the assumption. Knowledge discovery: In the 5 random experiments, we add 500 pseudo links in each iteration. The metadata information of the nodes are all strongly relevant to "Linux" Both papers focus on the "malware"/"phishing" under the topic "Computer security". The detailed result of the case study is shown in Table 6.

It's no secret that Russia has been slowly working towards eschewing as much Western technology as it can and developing its own, and its latest effort seems to be related to video games. On December 25, Anton Gorelkin, Deputy Chairman of the State Duma Committee on Information Policy, revealed some information on a domestic video game console being developed by the Ministry of Industry and Trade, as reported by TechSpot. The theoretical console will have an Elbrus processor and be powered by either Aurora or Alt Linux, both Russian forks of the popular Linux operating system. According to TechSpot, the Elbrus processor was developed by the Moscow Center of SPARC Technologies and primarily designed for defense, critical infrastructure and other applications. Despite the weaker chipset, Gorelkin stressed that the console isn't designed to play ports of older games, but will play "domestic video game products."

Most tech moguls hope to sell artificial intelligence to the masses. But Mark Zuckerberg is giving away what Meta considers to be one of the world's best AI models for free. Meta released the biggest, most capable version of a large language model called Llama on Monday, free of charge. Meta has not disclosed the cost of developing Llama 3.1 but Zuckerberg recently told investors that his company is spending billions on AI development. Through this latest release, Meta is showing that the closed approach favored by most AI companies is not the only way to develop AI.

Advanced Persistent Threats (APTs) are sophisticated, targeted cyberattacks designed to gain unauthorized access to systems and remain undetected for extended periods. To evade detection, APT cyberattacks deceive defense layers with breaches and exploits, thereby complicating exposure by traditional anomaly detection-based security methods. The challenge of detecting APTs with machine learning is compounded by the rarity of relevant datasets and the significant imbalance in the data, which makes the detection process highly burdensome. We present AE-APT, a deep learning-based tool for APT detection that features a family of AutoEncoder methods ranging from a basic one to a Transformer-based one. We evaluated our tool on a suite of provenance trace databases produced by the DARPA Transparent Computing program, where APT-like attacks constitute as little as 0.004% of the data. The datasets span multiple operating systems, including Android, Linux, BSD, and Windows, and cover two attack scenarios. The outcomes showed that AE-APT has significantly higher detection rates compared to its competitors, indicating superior performance in detecting and ranking anomalies.

Cloud computing and the evolution of management methodologies such as Lean Management or Agile entail a profound transformation in both system construction and maintenance approaches. These practices are encompassed within the term "DevOps." This descriptive approach to an information system or application, alongside the configuration of its constituent components, has necessitated the development of descriptive languages paired with specialized engines for automating systems administration tasks. Among these, the tandem of Ansible (engine) and YAML (descriptive language) stands out as the two most prevalent tools in the market, facing notable competition mainly from Terraform. The current document presents an inquiry into a solution for generating and managing Ansible YAML roles and playbooks, utilizing Generative LLMs (Language Models) to translate human descriptions into code. Our efforts are focused on identifying plausible directions and outlining the potential industrial applications. Note: For the purpose of this experiment, we have opted against the use of Ansible Lightspeed. This is due to its reliance on an IBM Watson model, for which we have not found any publicly available references. Comprehensive information regarding this remarkable technology can be found [1] directly on our partner's website, RedHat.