Researchers built an inexpensive device that circumvents chipmakers’ confidential computing protections and reveals weaknesses in scalable memory encryption. Read More
Year-end budgeting is the perfect time to close real security gaps by strengthening identity controls, reducing redundant tools, and investing in outcome-driven engagements. The article highlights how targeting credential risks and documenting results helps teams maximize spend and justify next year’s budget. […] Read More
Thousands of credentials, authentication keys, and configuration data impacting organizations in sensitive sectors have been sitting in publicly accessible JSON snippets submitted to the JSONFormatter and CodeBeautify online tools that format and structure code. […] Read More
A new ClickFix variant ratchets up the psychological pressure to 100 and addresses some technical mitigations to classic ClickFix attacks. Read More
e-Conomy SEA 2025: Malaysia takes 32% of regional AI fundingAI News Malaysia has captured 32% of Southeast Asia’s total AI funding – equivalent to US$759 million – between H2 2024 and H1 2025, establishing itself as the region’s dominant destination for artificial intelligence investment as massive infrastructure expansion and high consumer adoption converge to reshape the country’s technology landscape, according to the e-Conomy SEA 2025 report
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Malaysia has captured 32% of Southeast Asia’s total AI funding – equivalent to US$759 million – between H2 2024 and H1 2025, establishing itself as the region’s dominant destination for artificial intelligence investment as massive infrastructure expansion and high consumer adoption converge to reshape the country’s technology landscape, according to the e-Conomy SEA 2025 report
The post e-Conomy SEA 2025: Malaysia takes 32% of regional AI funding appeared first on AI News. Read More
The U.S. Cybersecurity and Infrastructure Security Agency (CISA) on Monday issued an alert warning of bad actors actively leveraging commercial spyware and remote access trojans (RATs) to target users of mobile messaging applications. “These cyber actors use sophisticated targeting and social engineering techniques to deliver spyware and gain unauthorized access to a victim’s messaging app, Read […]
Cybersecurity researchers have disclosed details of a new campaign that has leveraged Blender Foundation files to deliver an information stealer known as StealC V2. “This ongoing operation, active for at least six months, involves implanting malicious .blend files on platforms like CGTrader,” Morphisec researcher Shmuel Uzan said in a report shared with The Hacker News. […]
Dartmouth College has disclosed a data breach after the Clop extortion gang leaked data allegedly stolen from the school’s Oracle E-Business Suite servers on its dark web leak site. […] Read More
Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNNcs.AI updates on arXiv.org arXiv:2412.17629v5 Announce Type: replace-cross
Abstract: Evolutionary algorithms (EAs) simulate natural selection but have two main limitations: (1) they rarely update individuals based on global correlations, limiting comprehensive learning; (2) they struggle with balancing exploration and exploitation, where excessive exploitation causes premature convergence, and excessive exploration slows down the search. Moreover, EAs often depend on manual parameter settings, which can disrupt the exploration-exploitation balance. To address these issues, we propose Graph Neural Evolution (GNE), a novel EA framework. GNE represents the population as a graph, where nodes represent individuals, and edges capture their relationships, enabling global information usage. GNE utilizes spectral graph neural networks (GNNs) to decompose evolutionary signals into frequency components, applying a filtering function to fuse these components. High-frequency components capture diverse global information, while low-frequency ones capture more consistent information. This explicit frequency filtering strategy directly controls global-scale features through frequency components, overcoming the limitations of manual parameter settings and making the exploration-exploitation control more interpretable and manageable. Tests on nine benchmark functions (e.g., Sphere, Rastrigin, Rosenbrock) show that GNE outperforms classical (GA, DE, CMA-ES) and advanced algorithms (SDAES, RL-SHADE) under various conditions, including noise-corrupted and optimal solution deviation scenarios. GNE achieves solutions several orders of magnitude better (e.g., 3.07e-20 mean on Sphere vs. 1.51e-07).
arXiv:2412.17629v5 Announce Type: replace-cross
Abstract: Evolutionary algorithms (EAs) simulate natural selection but have two main limitations: (1) they rarely update individuals based on global correlations, limiting comprehensive learning; (2) they struggle with balancing exploration and exploitation, where excessive exploitation causes premature convergence, and excessive exploration slows down the search. Moreover, EAs often depend on manual parameter settings, which can disrupt the exploration-exploitation balance. To address these issues, we propose Graph Neural Evolution (GNE), a novel EA framework. GNE represents the population as a graph, where nodes represent individuals, and edges capture their relationships, enabling global information usage. GNE utilizes spectral graph neural networks (GNNs) to decompose evolutionary signals into frequency components, applying a filtering function to fuse these components. High-frequency components capture diverse global information, while low-frequency ones capture more consistent information. This explicit frequency filtering strategy directly controls global-scale features through frequency components, overcoming the limitations of manual parameter settings and making the exploration-exploitation control more interpretable and manageable. Tests on nine benchmark functions (e.g., Sphere, Rastrigin, Rosenbrock) show that GNE outperforms classical (GA, DE, CMA-ES) and advanced algorithms (SDAES, RL-SHADE) under various conditions, including noise-corrupted and optimal solution deviation scenarios. GNE achieves solutions several orders of magnitude better (e.g., 3.07e-20 mean on Sphere vs. 1.51e-07). Read More
The Core in Max-Loss Non-Centroid Clustering Can Be Emptycs.AI updates on arXiv.org arXiv:2511.19107v1 Announce Type: cross
Abstract: We study core stability in non-centroid clustering under the max-loss objective, where each agent’s loss is the maximum distance to other members of their cluster. We prove that for all $kgeq 3$ there exist metric instances with $nge 9$ agents, with $n$ divisible by $k$, for which no clustering lies in the $alpha$-core for any $alpha<2^{frac{1}{5}}sim 1.148$. The bound is tight for our construction. Using a computer-aided proof, we also identify a two-dimensional Euclidean point set whose associated lower bound is slightly smaller than that of our general construction. This is, to our knowledge, the first impossibility result showing that the core can be empty in non-centroid clustering under the max-loss objective.
arXiv:2511.19107v1 Announce Type: cross
Abstract: We study core stability in non-centroid clustering under the max-loss objective, where each agent’s loss is the maximum distance to other members of their cluster. We prove that for all $kgeq 3$ there exist metric instances with $nge 9$ agents, with $n$ divisible by $k$, for which no clustering lies in the $alpha$-core for any $alpha<2^{frac{1}{5}}sim 1.148$. The bound is tight for our construction. Using a computer-aided proof, we also identify a two-dimensional Euclidean point set whose associated lower bound is slightly smaller than that of our general construction. This is, to our knowledge, the first impossibility result showing that the core can be empty in non-centroid clustering under the max-loss objective. Read More
