Using the dot task (see Jones, Farrand, Stuart, & Morris, 1995)-regarded as a good visuospatial analogue of the verbal serial recall task-we examined whether the Hebb repetition effect and its characteristics can be extended to visuospatial material. Classically, the Hebb effect has been associated with serial verbal memory: Repetition of a to-be-remembered sequence of verbal items every third trial markedly improves serial recall of that sequence. In the present study, Hebb effects were observed with visuospatial information, and a direct comparison between verbal and spatial sequence learning revealed that the Hebb repetition effect for visuospatial information shares similar characteristics with its verbal analogue. Our results cast some doubt regarding the parsimony of the view that the classical verbal Hebb effect is driven by a store specialized for phonological information and impose some further constraints on modeling serial memory and implicit sequence learning.

In a serial recall task, the Hebb repetition effect occurs when recall performance improves for a sequence repeated throughout the experimental session. This phenomenon has been replicated many times. Nevertheless, such cumulative learning seldom leads to perfect recall of the whole sequence, and errors persist. Here the authors report evidence that there is another side to the Hebb repetition effect that involves learning errors produced in a repeated sequence. A learning measure based on past recalls (correct or incorrect) shows that the probability of a given response increases with the number of prior occurrences of that response. The pattern of results reveals another manifestation of the Hebb repetition effect and speaks to the nature of implicit learning.

We consider the problem of removing c points from a set S of n points so that the remaining point set is optimal in some sense. Definitions of optimality we consider include having minimum diameter, having minimum area (perimeter) bounding box, having minimum area (perimeter) convex hull. For constant values of c, all our algorithms run in O (n log n) time.

Given a graph G, a k-dominating set of G is a subset S of its vertices with the property that every vertex of G is either in S or has at least k neighbors in S. We present a new incremental local algorithm to construct a k-dominating set. The algorithm constructs a monotone family of dominating setsis an i-dominating set. For unit disk graphs, the size of each of the resulting i-dominating sets is at most six times the optimal.

Over the last years, dynamic and static malware analysis techniques have made significant progress. Majority of the existing analysis systems primarily focus on internal host activity. In spite of the importance of network activity, only a limited set of analysis tools have recently started taking it into account.In this work, we study the value of network activity for malware classification by various antivirus products. Specifically, we ask the following question: How well can we classify malware according to network activity? We monitor the execution of a malware sample in a controlled environment and summarize the obtained high-level network information in a graph. We then analyze graphs similarity to determine whether such high-level behavioral profile is sufficient to provide accurate classification of malware samples. The experimental study on a real-world malware collection demonstrates that our approach is able to group malware samples that behave similarly.