Catálogo de publicaciones - libros

Video broadcast series like news or magazine broadcasts usually expose a strong temporal structure, along with a characteristic audio-visual appearance. This results in frequent patterns occurring in the video signal. We propose an algorithm for the automatic detection of such patterns that exploits the video’s self-similarity induced by the patterns. The approach is applied to the problem of anchor shot detection, but can also be used for other related purposes. Tests on real-world video data show that it is possible with our method to detect anchor shots fully automatically with high reliability.

This paper presents a content-based approach to spam detection based on low-level information. Instead of the traditional ’bag of words’ representation, we use a ’bag of character -grams’ representation which avoids the sparse data problem that arises in -grams on the word-level. Moreover, it is language-independent and does not require any lemmatizer or ’deep’ text preprocessing. Based on experiments on Ling-Spam corpus we evaluate the proposed representation in combination with support vector machines. Both binary and term-frequency representations achieve high precision rates while maintaining recall on equally high level, which is a crucial factor for anti-spam filters, a cost sensitive application.

The intermittent nature of the wind creates significant uncertainty in the operation of power systems with increased wind power penetration. Con- siderable efforts have been made for the accurate prediction of the wind power using either statistical or physical models. In this paper, a method based on Artificial Neural Network (ANN) is proposed in order to improve the predictions of an existing neuro-fuzzy wind power forecasting model taking into account the evaluation results from the use of this wind power forecasting tool. Thus, an improved wind power forecasting is achieved and a better estimation of the confidence interval of the proposed model is provided.

This paper presents a new bidding strategy for continuous double auctions (CDA) designed for Mertacor, a successful trading agent, which won the first price in the “travel game” of Trading Agent Competition (TAC) for 2005. TAC provides a realistic benchmarking environment in which various travel commodities are offered in simultaneous online auctions. Among these, entertainment tickets are traded in CDA. The latter, represent the most dynamic part of the TAC game, in which agents are both sellers and buyers. In a CDA many uncertainty factors are introduced, because prices are constantly changing during the game and price fluctuations are hard to be predicted. In order to deal with these factors of uncertainty we have designed a strategy based on achieving a pre-defined long-term profit. This preserves the bidding attitude of our agent and shows flexibility in changes of the environment. We finally present and discuss the results of TAC-05, as well as an analysis of agents performance in the entertainment auctions.

The leap from decision support to autonomous systems has often raised a number of issues, namely system safety, soundness and security. Depending on the field of application, these issues can either be easily overcome or even hinder progress. In the case of Supply Chain Management (SCM), where system performance implies loss or profit, these issues are of high importance. SCM environments are often dynamic markets providing incomplete information, therefore demanding intelligent solutions which can adhere to environment rules, perceive variations, and act in order to achieve maximum revenue. Advancing on the way such autonomous solutions deal with the SCM process, we have built a robust, highly-adaptable and easily-configurable mechanism for efficiently dealing with all SCM facets, from material procurement and inventory management to goods production and shipment. Our agent has been crash-tested in one of the most challenging SCM environments, the trading agent competition SCM game and has proven capable of providing advanced SCM solutions on behalf of its owner. This paper introduces and its main architectural primitives, provides an overview of the TAC SCM environment, and discusses ’s performance.

Probabilistic Latent Semantic Indexing (PLSI) is a statistical technique for automatic document indexing. A novel method is proposed for updating PLSI when new documents arrive. The proposed method adds incrementally the words of any new document in the term-document matrix and derives the updating equations for the probability of terms given the class (i.e. latent) variables and the probability of documents given the latent variables. The performance of the proposed method is compared to that of the folding-in algorithm, which is an inexpensive, but potentially inaccurate updating method. It is demonstrated that the proposed updating algorithm outperforms the folding-in method with respect to the mean squared error between the aforementioned probabilities as they are estimated by the two updating methods and the original non-adaptive PLSI algorithm.

Parametric models such as linear regression can provide useful, interpretable descriptions of simple structure in data. However, sometimes such simple structure does not extend across an entire data set and may instead be confined more locally within subsets of the data. Nonparametric regression typically involves local averaging. In this study, local averaging estimator is coupled with a machine learning technique – boosting. In more detail, we propose a technique of local boosting of decision stumps. We performed a comparison with other well known methods and ensembles, on standard benchmark datasets and the performance of the proposed technique was greater in most cases.

We present, , a tool that supports discovery over time series data. is realized by the introduction of novel algorithmic processes, which support assessment of coherence and similarity across timeseries data. The innovation comes from the inclusion of specific ‘control’ operations in the elaborated time-series matching metric. The final outcome is the clustering of time-series into similar-groups. Clustering is performed via the appropriate customization of a phylogeny-based clustering algorithm and tool. We demonstrate via two experiments.

Several agent frameworks have been proposed for developing intelligent software agents and multi-agent systems that are able to perform in dynamic environments. These frameworks and architectures exploit specific reasoning tasks (such as option selection, desire filtering, plan elaboration and means-end reasoning) that support agents to react, deliberate and/or interact/cooperate with other agents. Such reasoning tasks are realized by means of specific modules that agents may trigger according to circumstances, switching their behaviour between predefined discrete behavioural modes. This paper presents the facilities provided by the non-layered BDI-architecture of IC for supporting performance in dynamic and unpredictable multi-agent environments through efficient balancing between behavioural modes in a continuous space. This space is circumscribed by the purely (individual) reactive, the purely (individual) deliberative and the social deliberative behavioural modes.  In a greater extend than existing frameworks; IC relates agent’s flexible behaviour to cognition and sociability, supporting the management of plans constructed by the agent’s mental and domain actions in a coordinated manner.

This paper describes the application of Decision Tress (DTs) in order to specify the most critical location and the rate of series compensation in order to increase power system loading margin. The proposed methodology is applied to a projected model of the Hellenic interconnected system in several system configurations. Investigation of the best system operating point to create the DTs, the effect of attributes number and type on the DTs size and quality are discussed in order to reach the final DTs parameters that lead to the construction of the best DTs for the determination of optimal series compensation location and rate. Finally, the results obtained for several (N-1) contingencies examined are presented.