Tootfinder

NIST and Web3 Security – A Developing Perspective
 
The National Institute for Standards and Technology just released an initial draft of “A Security Perspective on the Web3 Paradigm” as document IR 8475. It is not long and it is a great take on how NIST is thinking about Web3 security. Here is the link:
 
#Web3 #NIST #Security #InfoSec #BlockChain #IPFS
 

Instance-Conditioned Adaptation for Large-scale Generalization of Neural Combinatorial Optimization
Changliang Zhou, Xi Lin, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang
https://arxiv.org/abs/2405.01906

Instance-Conditioned Adaptation for Large-scale Generalization of Neural Combinatorial Optimization
The neural combinatorial optimization (NCO) approach has shown great potential for solving routing problems without the requirement of expert knowledge. However, existing constructive NCO methods cannot directly solve large-scale instances, which significantly limits their application prospects. To address these crucial shortcomings, this work proposes a novel Instance-Conditioned Adaptation Model (ICAM) for better large-scale generalization of neural combinatorial optimization. In particular, …

NIST and Web3 Security – A Developing Perspective
 
The National Institute for Standards and Technology just released an initial draft of “A Security Perspective on the Web3 Paradigm” as document IR 8475. It is not long and it is a great take on how NIST is thinking about Web3 security. Here is the link:
 
#Web3 #NIST #Security #InfoSec #BlockChain #IPFS
 

Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommendation Systems
Dayu Yang, Fumian Chen, Hui Fang
https://arxiv.org/abs/2404.11773

Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommendation Systems
Large Language Models (LLMs) have demonstrated great potential in Conversational Recommender Systems (CRS). However, the application of LLMs to CRS has exposed a notable discrepancy in behavior between LLM-based CRS and human recommenders: LLMs often appear inflexible and passive, frequently rushing to complete the recommendation task without sufficient inquiry.This behavior discrepancy can lead to decreased accuracy in recommendations and lower user satisfaction. Despite its importance, existi…