Computer Science

ABSTRACT This paper reviews the achievement of benefits through Enterprise Content Management (ECM) systems in public sector organisations. From the literature there are few benefits which deal with ECM benefits in organisations, especially in public sector. The main benefits have been anecdotal and not linked to a theoretical basis. In this paper a framework to classify these benefits is proposed and benefits identified in the literature are discussed and classified. The paper also proposes a more substantial framework through a comparison with benefits from enterprise resource planning systems (ERPs) and Knowledge Management systems (KMs) which are applicable to the ECM environment. From a practical perspective this research will be of value to public sector organisations as these organisations are making substantial investments in the implementation of ECM systems often without achieving the expected benefits. Thus, this study will enhance the understanding of achievements of benefits through ECM use in public sector organisations.

... Department of Electrical and Computer Engineering University of Miami Coral Gables, Florida 33124-0640 E-mail: {emad , christos}@ece.miami.edu and Computer Science Department Southern Connecticut State University 501 Crescent Street New Haven, Connecticut 06515 ...

Reverse transcriptase (RT) is a viral enzyme crucial for HIV-1 replication. Currently, 12 drugs are targeted against the RT. The low fidelity of the RT-mediated transcription leads to the quick accumulation of drug-resistance mutations. The sequence-resistance relationship remains only partially understood. Using publicly available data collected from over 15 years of HIV proteome research, we have created a general and predictive rule-based model of HIV-1 resistance to eight RT inhibitors. Our rough set-based model considers changes in the physicochemical properties of a mutated sequence as compared to the wild-type strain. Thanks to the application of the Monte Carlo feature selection method, the model takes into account only the properties that significantly contribute to the resistance phenomenon. The obtained results show that drug-resistance is determined in more complex way than believed. We confirmed the importance of many resistance-associated sites, found some sites to be ...

A mechanism for the self-organized integration of different adaptive cues is proposed. In democratic integration the cues agree on a result and each cue adapts towards the result agreed upon. A technical formulation of this scheme is employed in a face tracking system. The self-organized adaptivity lends itself to suppression and recalibration of discordant cues. Experiments show that the system is robust to sudden changes in the environment as long as the changes disrupt only a minority of cues at the same time, ...

This paper investigates a novel graph embedding procedure based on simplicial complexes. Inherited from algebraic topology, simplicial complexes are collections of increasing-order simplices (e.g., points, lines, triangles, tetrahedrons) which can be interpreted as possibly meaningful substructures (i.e., information granules) on the top of which an embedding space can be built by means of symbolic histograms. In the embedding space, any Euclidean pattern recognition system can be used, possibly equipped with feature selection capabilities in order to select the most informative symbols. The selected symbols can be analysed by field-experts in order to extract further knowledge about the process to be modelled by the learning system, hence the proposed modelling strategy can be considered as a grey-box. The proposed embedding has been tested on thirty benchmark datasets for graph classification and, further, we propose two real-world applications, namely predicting proteins’ enzymat...

Summary We present a novel method for the development of hierarchical multi-attribute decision models from a given unstructured set of decision examples. The method develops a hierarchical structure by discovering new aggregate attributes and their descriptions. Each new aggregate attribute is described by an example set whose complexity is lower than the complexity of the initial set. The method is based on function decomposition. Models can be developed either with or without human interaction. The method is experimentally evaluated on a real-world housing loans allocation problem. This case study shows that the decomposition can discover a meaningful and transparent decision model of high classification accuracy. We show that human assistance has a positive effect on both the comprehensibility and classification accuracy.