ACM Symposium on Applied Computing 

About: ACM Symposium on Applied Computing is an academic conference. The conference publishes majorly in the area(s): Computer science & Software development. Over the lifetime, 7740 publications have been published by the conference receiving 102702 citations.

Particle swarm optimization method in multiobjective problems

Abstract: This paper constitutes a first study of the Particle Swarm Optimization (PSO) method in Multiobjective Optimization (MO) problems. The ability of PSO to detect Pareto Optimal points and capture the shape of the Pareto Front is studied through experiments on well-known non-trivial test functions. The Weighted Aggregation technique with fixed or adaptive weights is considered. Furthermore, critical aspects of the VEGA approach for Multiobjective Optimization using Genetic Algorithms are adapted to the PSO framework in order to develop a multi-swarm PSO that can cope effectively with MO problems. Conclusions are derived and ideas for further research are proposed.

Learning to identify emotions in text

Abstract: This paper describes experiments concerned with the automatic analysis of emotions in text. We describe the construction of a large data set annotated for six basic emotions: ANGER, DISGUST, FEAR, JOY, SADNESS and SURPRISE, and we propose and evaluate several knowledge-based and corpusbased methods for the automatic identification of these emotions in text.

Naive Bayes vs decision trees in intrusion detection systems

Abstract: Bayes networks are powerful tools for decision and reasoning under uncertainty. A very simple form of Bayes networks is called naive Bayes, which are particularly efficient for inference tasks. However, naive Bayes are based on a very strong independence assumption. This paper offers an experimental study of the use of naive Bayes in intrusion detection. We show that even if having a simple structure, naive Bayes provide very competitive results. The experimental study is done on KDD'99 intrusion data sets. We consider three levels of attack granularities depending on whether dealing with whole attacks, or grouping them in four main categories or just focusing on normal and abnormal behaviours. In the whole experimentations, we compare the performance of naive Bayes networks with one of well known machine learning techniques which is decision tree. Moreover, we compare the good performance of Bayes nets with respect to existing best results performed on KDD'99.

A clustering-based approach for discovering interesting places in trajectories

Abstract: Because of the large amount of trajectory data produced by mobile devices, there is an increasing need for mechanisms to extract knowledge from this data. Most existing works have focused on the geometric properties of trajectories, but recently emerged the concept of semantic trajectories, in which the background geographic information is integrated to trajectory sample points. In this new concept, trajectories are observed as a set of stops and moves, where stops are the most important parts of the trajectory. Stops and moves have been computed by testing the intersections of trajectories with a set of geographic objects given by the user. In this paper we present an alternative solution with the capability of finding interesting places that are not expected by the user. The proposed solution is a spatio-temporal clustering method, based on speed, to work with single trajectories. We compare the two different approaches with experiments on real data and show that the computation of stops using the concept of speed can be interesting for several applications.

The 'Dresden Image Database' for benchmarking digital image forensics

Abstract: This paper introduces and documents a novel image database specifically built for the purpose of development and bench-marking of camera-based digital forensic techniques. More than 14,000 images of various indoor and outdoor scenes have been acquired under controlled and thus widely comparable conditions from altogether 73 digital cameras. The cameras were drawn from only 25 different models to ensure that device-specific and model-specific characteristics can be disentangled and studied separately, as validated with results in this paper. In addition, auxiliary images for the estimation of device-specific sensor noise pattern were collected for each camera. Another subset of images to study model-specific JPEG compression algorithms has been compiled for each model. The 'Dresden Image Database' will be made freely available for scientific purposes when this accompanying paper is presented. The database is intended to become a useful resource for researchers and forensic investigators. Using a standard database as a benchmark not only makes results more comparable and reproducible, but it is also more economical and avoids potential copyright and privacy issues that go along with self-sampled benchmark sets from public photo communities on the Internet.