1,720,979 research outputs found
Integrated Virtual Environments for Collaborative Real-Time Activities: the Co.S.M.O.S. prototype
No full textThis paper presents Co.S.M.O.S. (Social and Collaborative Multimedia
Operating System), a prototype of a virtual environment to support real-time
collaborative activities developed with the aim to meet the growing needs of users in terms of Knowledge Management, Collaboration, and Socialization.
More precisely Co.S.M.O.S. is an integrated environment consisting of a
WebOS, an environment for the realization of collaborative activities and a
social network which can efficiently support real-time collaboration and thus
facilitate the development of social communities through advanced sharing
and cooperation in the process of Knowledge Management. Co.S.M.O.S. is
easily accessible via a Java applet, embedded in a web page, offering users
all its features
Una strategia per la valutazione continua di attività di e-learning
No full textLo sviluppo e la diffusione dell’e-learning sta imponendo una sempre maggiore attenzione verso la definizione di strategie di valutazione in grado di verificare la rispondenza delle azioni formative sia alle necessità educative che alle esigenze organizzative, normative ed istituzionali. Questo lavoro presenta una semplice ed efficace strategia di tipo “participant-oriented” per la valutazione delle azioni di e-learning. L’applicazione di tale strategia ad attività in e-learning svolte nell’Università degli Studi di Bari ha permesso di evidenziarne la potenzialità e l’utilità
Artificial Classifier Generation for Multi-expert System Evaluation
No full textThe evaluation of combination methods for multi-classifier systems is a difficult problem. In many cases multi-classifier combination methods are too complex to be formally studied and the experimental approach is the unique possible strategy. Of course, in order to simulate a multitude of real working conditions, sets of artificial classifiers with diverse characteristics must be generated.
This paper presents an effective technique for generating sets of artificial classifiers with different characteristics both at the individual-level (i.e. recognition performance) and at the collective-level (i.e. degree of similarity).
In the experimental tests, sets of artificial classifiers simulating different working conditions are generated and the performances of abstract-level combination methods are estimated. The results points out the effectiveness of the new technique for generating sets of artificial classifiers with different characteristics and their usefulness in estimating the performances of combination methods
HANDWRITTEN SIGNATURE VERIFICATION BY MULTIPLE REFERENCE SETS
No full textThis paper presents a new approach for on-line handwritten signature verification, which exploits the potential of multiple reference sets.
Preliminarily, system performance is estimated using different sets of reference signatures for each writer. Successively, reference sets leading to diverse system behaviors are enrolled into the personal knowledge-base and used in a multi-stage verification process.
The experimental results show the effectiveness of the proposed approach, compared to traditional techniques
Combination of Measurement-Level Classifiers: Output Normalization by Dynamic Time Warping
No full textClassifier combination is a powerful strategy to support useful solutions in difficult classification problems. Notwithstanding, the effectiveness of a multi-classifier system strongly depends on the decision fusion strategies. In this field, one of the most significant aspects concerns output normalization, when classifiers decisions are provided at measurement level.
This paper presents a new approach for output normalization that uses Dynamic Time Warping (DTW). Some experimental tests have been carried out in the field of handwritten digit recognition. The proposed approach is superior to other output normalization algorithms in the literature
Learning Local Corrispondences for Static Signature Verification
No full textThis paper presents a new approach for off-line signature verification. Signature verification is performed by matching only well-selected regions of the signature images. More precisely, from the analysis of lower and upper contours of a signature image, region stability is estimated and the most stable regions are selected for verification, during the enrollment phase. In the verification phase, an unknown specimen is verified through the analysis of the selected regions, on the basis of a well-defined similarity measure. The experimental results, carried out on signatures from the GPDS database, demonstrate the potential of the proposed approach
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