We present a simple and very efficient algorithm for string matching based on the combination of weak factor recognition and hashing. Despite its quadratic worst-case running time, our algorithm exhibits a sublinear behaviour. We also propose some practical improvements of our algorithm and a variant with a linear worst-case time complexity. Experimental results show that, in most cases, some of the variants of our algorithm obtain the best running times when compared, under various conditions, against the most effective algorithms present in the literature. For instance, in the case of small alphabets and long patterns, the gain in running time is up to 18%. This makes our proposed algorithm one of the most flexible solutions in practical cases.

No abstract

In dealing with the algorithmic aspects of intelligent systems, the analogy with the biological brain has always been attractive, and has often had a dual function. On the one hand, it has been an effective source of inspiration for their design, while, on the other hand, it has been used as the justification for their success, especially in the case of Deep Learning (DL) models. However, in recent years, inspiration from the brain has lost its grip on its first role, yet it continues to be proposed in its second role, although we believe it is also becoming less and less defensible. Outside the chorus, there are theoretical proposals that instead identify important demarcation lines between DL and human cognition, to the point of being even incommensurable. In this article we argue that, paradoxically, the partial indifference of the developers of deep neural models to the functioning of biological neurons is one of the reasons for their success, having promoted a pragmatically opportunistic attitude. We believe that it is even possible to glimpse a biological analogy of a different kind, in that the essentially heuristic way of proceeding in modern DL development bears intriguing similarities to natural evolution.

No abstract

No abstract