Object detection by global contour shape

We present a method for object class detection in images based on global shape. A distance measure for elastic shape matching is derived, which is invariant to scale and rotation, and robust against non-parametric deformations. Starting from an over-segmentation of the image, the space of potential object boundaries is explored to find boundaries, which have high similarity with the shape template of the object class to be detected. An extensive experimental evaluation is presented. The approach achieves a remarkable detection rate of 83-91% at 0.2 false positives per image on three challenging data sets.     

Method of classifier selection using the genetic approach

Abstract: The paper presents a novel machine learning algorithm used for training a compound classifier system that consists of a set of area classifiers. Area classifiers recognize objects derived from the respective competence area. Splitting feature space into areas and selecting area classifiers are two key processes of the algorithm; both take place simultaneously in the course of an optimization process aimed at maximizing the system performance. An evolutionary algorithm is used to find the optimal solution. A number of experiments have been carried out to evaluate system performance. The results prove that the proposed method outperforms each elementary classifier as well as simple voting.     

Atomic RMI: A Distributed Transactional Memory Framework

This paper presents Atomic RMI, a distributed transactional memory framework that supports the control flow model of execution. Atomic RMI extends Java RMI with distributed transactions that can run on many Java virtual machines located on different network nodes. Our system employs SVA, a fully-pessimistic concurrency control algorithm that provides exclusive access to shared objects and supports rollback and fault tolerance. SVA is capable of achieving a relatively high level of parallelism by interweaving transactions that access the same objects and by making transactions that do not share objects independent of one another. It also allows any operations within transactions, including irrevocable ones, like system calls, and provides an unobtrusive API. Our evaluation shows that in most cases Atomic RMI performs better than fine grained mutual-exclusion and read/write locking mechanisms. Atomic RMI also performs better than an optimistic transactional memory in environments with high contention and a high ratio of write operations, while being competitive otherwise.     

Anisotropic Filtering of Non-Linear Surface Features

A new method for noise removal of arbitrary surfaces meshes is presented which focuses on the preservation and sharpening of non‐linear geometric features such as curved surface regions and feature lines. Our method uses a prescribed mean curvature flow (PMC) for simplicial surfaces which is based on three new contributions: 1. the definition and efficient calculation of a discrete shape operator and principal curvature properties on simplicial surfaces that is fully consistent with the well‐known discrete mean curvature formula, 2. an anisotropic discrete mean curvature vector that combines the advantages of the mean curvature normal with the special anisotropic behaviour along feature lines of a surface, and 3. an anisotropic prescribed mean curvature flow which converges to surfaces with an estimated mean curvature distribution and with preserved non‐linear features. Additionally, the PMC flow prevents boundary shrinkage at constrained and free boundary segments.     

Helenos: A realistic benchmark for distributed transactional memory

Transactional memory (TM) is an approach to concurrency control that aims to make writing parallel programs both effective and simple. The approach has been initially proposed for nondistributed multiprocessor systems, but it is gaining popularity in distributed systems to synchronize tasks at large scales. Efficiency and scalability are often the key issues in TM research; thus, performance benchmarks are an important part of it. However, while standard TM benchmarks like the Stanford Transactional Applications for Multi‐Processing suite and STMBench7 are available and widely accepted, they do not translate well into distributed systems. Hence, the set of benchmarks usable with distributed TM systems is very limited, and must be padded with microbenchmarks, whose simplicity and artificial nature often makes them uninformative or misleading. Therefore, this paper introduces Helenos, a realistic, complex, and comprehensive distributed TM benchmark based on the problem of the Facebook inbox, an application of the Cassandra distributed store.     

Language models for detection of unknown attacks in network traffic

In this paper, we propose a method for network intrusion detection based on language models. Our method proceeds by extracting language features such as n-grams and words from connection payloads and applying unsupervised anomaly detection—without prior learning phase or presence of labeled data. The essential part of this procedure is linear-time computation of similarity measures between language models of connection payloads. Particular patterns in these models decisive for differentiation of attacks and normal data can be traced back to attack semantics and utilized for automatic generation of attack signatures. Results of experiments conducted on two datasets of network traffic demonstrate the importance of high-order n-grams and variable-length language models for detection of unknown network attacks. An implementation of our system achieved detection accuracy of over 80% with no false positives on instances of recent remote-to-local attacks in HTTP, FTP and SMTP traffic.     

Two-constraint domain decomposition with Space Filling Curves

In scientific computing, Space Filling Curves are a widely used tool for one-constraint domain decomposition. They provide a mechanism to sort multi-dimensional data in a locality preserving way, and, in this way, a (one dimensional) list of mesh elements is established which is subsequently split into 3 partitions under consideration of the constraint. This procedure has a runtime of O(NlogN) (N is the number of mesh elements) while nearly perfect load balancing can be established with reasonable partition surface sizes.In this work, we discuss the extensibility of this procedure to two-constraint settings which is desirable, since the methodology is extremely fast. Here, the splitting operation is subject to two constraints, and, unlike to the one-constraint case, obtaining near perfect balancing is often hard to establish, and is, even more as in the one-constraint case, in conflict with the induced surface sizes (or edge-cuts). We discuss multiple strategies to tackle the splitting, and we present a fast, O(NlogN) splitting heuristic algorithm which provides an integer σ that allows to trade off between balancing and surface sizes which results in a O(NlogN) two-constraint decomposition method. Results are compared to the multi-constraint domain decomposition abilities implemented in the Metis software package, and show that the method produces higher surface sizes, but is orders of magnitudes faster which makes the method superior for certain applications.     

Patch layout from feature graphs

The structuring of surface meshes is a labor intensive task in reverse engineering. For example, in CAD, scanned triangle meshes must be divided into characteristic/uniform patches to enable conversion into high-level spline surfaces. Typical industrial techniques, like rolling ball blends, are very labor intensive.We provide a novel, robust and quick algorithm for the automatic generation of a patch layout based on a topology consistent feature graph. The graph separates the surface along feature lines into functional and geometric building blocks. Our algorithm then thickens the edges of the feature graph and forms new regions with low varying curvature. Further, these new regions-so-called fillets and node patches-will have highly smooth boundary curves, making the algorithm an ideal preprocessor for a subsequent spline fitting algorithm.