The story of ERNIE, a computer built by Thomas Flowers to generate random numbers for the UK's Premium Bonds lottery, evolving from neon-lamp based randomness to quantum technology.
A new study by MIT CSAIL researchers maps the challenges of AI in software development, identifying bottlenecks and highlighting research directions to move the field forward, aiming to allow humans to focus on high-level design while automating routine tasks.
This article discusses Fibonacci hashing, a method using the golden ratio to map data locations, potentially offering faster lookup speeds and more even distribution compared to integer modulo hashing. However, it may be problematic with data containing many Fibonacci numbers and is not a cryptographic hash.
A young computer scientist and two colleagues show that searches within data structures called hash tables can be much faster than previously deemed possible.
NIST has chosen HQC as a backup algorithm for post-quantum encryption, providing an additional layer of defense alongside ML-KEM. HQC uses different mathematical principles and is expected to be finalized in 2027.
This article combines theory with hands-on implementation in Flutter to make learning data structures engaging and practical.
Researchers have found that computations may require less memory than previously thought, with a new study suggesting that a calculation of X steps requires only the square root of X log X memory slots.
An interactive tool to visualize maze generation using Depth-First Search (DFS) and maze solving using Breadth-First Search (BFS).
This article by Zelda B. Zabinsky provides an overview of random search algorithms, which are particularly useful for tackling complex global optimization problems with either continuous or discrete variables. These algorithms, including simulated annealing, genetic algorithms, and particle swarm optimization, leverage randomness or probability in their iterative processes, often falling under the category of metaheuristics. Such methods are valuable for problems characterized by nonconvex, nondifferentiable, or discontinuous objective functions, as they offer a trade-off between optimality and computational speed. Random search algorithms can be categorized by their approach to exploration versus exploitation, and their application spans various fields, including engineering, scheduling, and biological systems. They address challenges where traditional deterministic methods struggle, particularly in the absence of clear structures distinguishing local from global optima.
The areas of research associated with Yinglian Xie, based on the dblp dataset, primarily focus on computer science domains such as cybersecurity, network analysis, and systems security. Key research topics include the detection and analysis of spamming botnets, anonymization techniques on the internet, and privacy protection in search systems. There is also significant work on network-level spam detection, botnet signatures, and web security. Yinglian Xie's publications span various conferences like IEEE Symposium on Security and Privacy, ACM SIGCOMM, and NDSS, highlighting a strong emphasis on both theoretical and practical aspects of security and privacy in distributed systems. Additionally, Xie has explored topics related to graph mining and anomaly detection in large networks.
