Function Call Graphs
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
AMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification
new Abstract: As malware illustrates a complex structure and behavior, detection of these has been a significant challenge in the domain of cybersecurity along with related services in daily life. So, it becomes crucial to have a reliable and adaptive solution to address the issue. Among the several detection methods developed over the years, one of the most reliable ones is studying and analyzing the structural and behavioral patterns of malware.
Evidence for a Functional Proximity Law in Multilayer Networks
arXiv:2604.23639v3 Announce Type: replace Abstract: Hub importance scores in multilayer networks persist more strongly between functionally similar layers than dissimilar ones. We call this the Functional Proximity Law and test it across 31 pre-registered experiments: 13 canonical domains (10 confirmed, 3 denied; molecular biology, neuroscience, computer systems, ecology, linguistics, AI architecture) plus 18 pre-registered external and replication validations (15 confirmed, 1 denied, 2...
Pyro Caml Continuous Profiler for OCaml
The core SAST engine of Semgrep is written in OCaml. There are a lot of good technical and historical reasons for this that I’ll leave for another time. An important consequence of using a language with a (relatively) small ecosystem like OCaml is that there aren’t a lot of libraries for things like observability, which are critical for running industrial software like Semgrep on hundreds of thousands of code repositories, and keeping it both reliable and performant.
Availability-Aware and Efficiency-Driven AI Service Chain Provisioning in Multi-Domain Edge Intelligence Cloud
arXiv:2606.05637v1 Announce Type: new Abstract: In a multi-domain edge intelligence cloud (MDEIC) managed by multiple network operators, AI services are delivered by chains of virtual network functions (VNFs) executed in sequence, called AI service chains (AISCs). Therefore, achieving an efficient and economical AISC provisioning approach is essential. However, the interaction between the environmental characteristics (heterogeneity, resource constraints and limited information visibility)...
Zig ELF Linker Improvements Devlog
Devlog This page contains a curated list of recent changes to main branch Zig. Also available as an RSS feed. This page contains entries for the year 2026.
Zig: Build System Reworked
Devlog This page contains a curated list of recent changes to main branch Zig. Also available as an RSS feed. This page contains entries for the year 2026.
Port React Compiler to Rust
[compiler] Port React Compiler to Rust#36173 This is an experimental, work-in-progress port of React Compiler to Rust. Key points: - Work-in-progress - we are sharing early, prior to testing internally at Meta, to get feedback from partners in parallel with continued development.
pg_durable: Microsoft open sources in-database durable execution
Long-running, fault-tolerant SQL functions for teams that already keep their state in Postgres and want to stop stitching together cron jobs, workers, queues, and status tables to make background work reliable. Define the workflow in SQL, let pg_durable checkpoint each step, and resume after crashes, restarts, or failed steps. Durable execution is now a standard industry pattern, and pg_durable brings it inside Postgres with no extra service infrastructure required.
Gooey: A GPU-accelerated UI framework for Zig
A GPU-accelerated UI framework for Zig, targeting macOS (Metal), Linux (Vulkan/Wayland), and Browser (WASM/WebGPU). Join the Gooey discord Early Development: API is evolving. Example app built with Gooey — chat-zig, an Anthropic Claude client using the Zig 0.16 std.
Needles at Scale: LLM-Assisted Target Selection for Windows Vulnerability Research
Announce Type: new Abstract: The attack surface of a modern operating system is a haystack: thousands of signed binaries and millions of functions, almost none relevant to any given vulnerability. A human analyst or an LLM agent must pick the function worth reading before analyzing it. At whole-OS scope, this target selection, not the analysis, is the binding constraint.