A Pragmatic Approach to DHT Adoption

Despite the peer-to-peer community's obvious wish to have its systems adopted, specific mechanisms to facilitate incremental adoption have not yet received the same level of attention as the many other practical concerns associated with these systems. This paper argues that ease of adoption should be elevated to a first-class concern and accordingly presents HOLD, a front-end to existing DHTs that is optimized for incremental adoption. Specifically, HOLD is backwards-compatible: it leverages DNS to provide a key-based routing service to existing Internet hosts without requiring them to install any software. This paper also presents applications that could benefit from HOLD as well as the trade-offs that accompany HOLD. Early implementation experience suggests that HOLD is practical.

Neural Pattern Recognition With Multi-Scale Pyramidal Coding and Selective Attention

A new neural network architecture for spatial patttern recognition using multi-scale pyramida1 coding is here described. The network has an ARTMAP structure with a new class of ART-module, called Hybrid ART-module, as its front-end processor. Hybrid ART-module, which has processing modules corresponding to each scale channel of multi-scale pyramid, employs channels of finer scales only if it is necesssary to discriminate a pattern from others. This process is effected by serial match tracking. Also the parallel match tracking is used to select the spatial location having most salient feature and limit its attention to that part.