Alberto Faro

Combining literature text mining with microarray data: advances for system biology modeling

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.

Adaptive Background Modeling Integrated With Luminosity Sensors and Occlusion Processing for Reliable Vehicle Detection

This paper presents a novel vehicle detection and tracking system with stationary camera that relies on a recursive background-modeling approach, i.e., the adaptive Poisson mixture model, which is integrated with a hardware module consisting of luminosity sensors. The luminosity information side channel allows the system to effectively handle rapid changes in illumination, which is typical of outdoor applications and bottleneck of the existing background pixel classification methods. A novel algorithm for detecting and removing partial and full occlusions among blobs is also proposed. Partial occlusions are detected by evaluating the ratio between the area of the vehicle and the area of the vehicles convex hull and are suppressed by identifying a cutting line using curvature analysis. A predictive model of the shape and motion features of the vehicles over consecutive frames instead corrects the error of the previous levels when full occlusions or background-vehicle occlusions occur in the scene. Quantitative evaluation and comparisons on some real-world scenarios demonstrate that the proposed approach outperforms state-of-the-art methods in terms of both vehicle detection and processing time, particularly due to the robustness and the efficiency of the background-modeling algorithm.

Evaluation of the Traffic Parameters in a Metropolitan Area by Fusing Visual Perceptions and CNN Processing of Webcam Images

This paper proposes a traffic monitoring architecture based on a high-speed communication network whose nodes are equipped with fuzzy processors and cellular neural network (CNN) embedded systems. It implements a real-time mobility information system where visual human perceptions sent by people working on the territory and video-sequences of traffic taken from Webcams are jointly processed to evaluate the fundamental traffic parameters for every street of a metropolitan area. This paper presents the whole methodology for data collection and analysis and compares the accuracy and the processing time of the proposed soft computing techniques with other existing algorithms. Moreover, this paper discusses when and why it is recommended to fuse the visual perceptions of the traffic with the automated measurements taken from the Webcams to compute the maximum traveling time that is likely needed to reach any destination in the traffic network.

Mining massive datasets by an unsupervised parallel clustering on a GRID: Novel algorithms and case study

This paper proposes three novel parallel clustering algorithms based on the Kohonens SOM aiming at preserving the topology of the original dataset for a meaningful visualization of the results and for discovering associations between features of the dataset by topological operations over the clusters. In all these algorithms the data to be clustered are subdivided among the nodes of a GRID. In the first two algorithms each node executes an on-line SOM, whereas in the third algorithm the nodes execute a quasi-batch SOM called MANTRA. The algorithms differ on how the weights computed by the slave nodes are recombined by a master to launch the next epoch of the SOM in the nodes. A proof outline demonstrates the convergence of the proposed parallel SOMs and provides indications on how to select the learning rate to outperform both the sequential SOM and the parallel SOMs available in the literature. A case study dealing with bioinformatics is presented to illustrate that by our parallel SOM we may obtain meaningful clusters in massive data mining applications at a fraction of the time needed by the sequential SOM, and that the obtained classification supports a fruitful knowledge extraction from massive datasets.

Discovering Genes-Diseases Associations From Specialized Literature Using the Grid

This paper proposes a novel method for text mining on the Grid, aimed at pointing out hidden relationships for hypothesis generation and suitable for semi-interactive querying. The method is based on unsupervised clustering and the outputs are visualized with contextual information. Grid implementation is crucial for feasibility. We demonstrate it with a mining run for discovering genes-diseases associations from bibliographic sources and annotated databases. The proposed methodology is in view of a Grid architecture specialized in bioinformatics mining tasks. Some performance considerations are provided.

Design memories as evolutionary systems: socio-technical architecture and genetics

A knowledge base consisting of design cases has been structured into an organizational memory, which has been supporting a community of novices to learn information system (IS) for six years. The paper discusses why the organizational learning process, which is taking place, can be described as a genetic system that evolves towards the best quality attainable, and by means of a simulation examines the role of some components of this socio-technical system (i.e. features of the organizational memory, teacher ability and memetic processes in the community) in steering this evolution. Some indications for the design of socio-technical systems supported by design memories are pointed out.

An Automated Tool for Face Recognition using Visual Attention and Active Shape Models Analysis

An entirely automated approach for the recognition of the face of a people starting from her/his images is presented. The approach uses a computational attention module to find automatically the most relevant facial features using the Focus Of Attentions (FOA) These features are used to build the model of a face during the learning phase and for recognition during the testing phase. The landmarking of the features is performed by applying the active contour model (ACM) technique, whereas the active shape model (ASM) is adopted for constructing a flexible model of the selected facial features. The advantages of this approach and opportunities for further improvements are discussed

Ontology based intelligent mobility systems

Aim of the paper is to illustrate an ontology based approach to the design of mobility information systems which, through multiple distribution channels, provide companies and individual consumers with the information that facilitate their mobility in a metropolitan area. Main advantages of the proposed approach are user queries whose semantics is independent of the system implementation, and the possibility of delegating user tasks to mobile software agents by means of either PCs or PDAs. The ontology framework also facilitates data collection and control of remote devices such as semaphores and signs. The outlined architecture is under experimentation for managing mobility in the Catania metropolitan area.

Feeding back learning resources repurposing patterns into the “information loop”: opportunities and challenges

The paper outlines a model for framing the representation and treatment of information gathered from the reuse and repurposing of learning resources from distributed repositories. The model takes into account as sources of information both static user-edited or automatically generated metadata fields and the emerging, dynamic information clouds that surrounds a learning resource when users comment on it, tags it, or explicitly links it to other learning resources. By coordinating these separate information layers, the advantages that can be achieved are reducing the semantic gap occurring when unanticipated contexts of use are to be described by resorting only to predefined vocabularies; and improvements in the relevance of the retrieved resources after a query. To achieve this “coordination” it is proposed that the textual descriptions of the repurposing activity with respect to the intended learning outcomes and pedagogical strategies are fed to a dynamic unsupervised classification method that operates on the above mentioned information spaces, and that supports exploratory search by suggesting associations. It is argued that the proposed analogical retrieval, as opposed to standard query matching, is more fit to tracking the loci of innovation and sustaining the formation of best practices in the community.

SOFT-COMPUTING AGENTS PROCESSING WEBCAM IMAGES TO OPTIMIZE METROPOLITAN TRAFFIC SYSTEMS

The paper proposes a solution for the optimization of traveling times in a metropolitan area that exploits the traffic images collected from webcams located at the crossroads of the traffic network. This is achieved by optimizing the traffic light cycles according to a distributed mathematical model whose solution is obtained by using suitable soft-computing agents resident on the nodes of the information network where they are responsible of processing the webcam images and of managing the traffic light cycles.