Ivan Bruno

Comparing fault-proneness estimation models

Over the last, years, software quality has become one of the most important requirements in the development of systems. Fault-proneness estimation could play a key role in quality control of software products. In this area, much effort has been spent in defining metrics and identifying models for system assessment. Using this metrics to assess which parts of the system are more fault-proneness is of primary importance. This paper reports a research study begun with the analysis of more than 100 metrics and aimed at producing suitable models for fault-proneness estimation and prediction of software modules/files. The objective has been to find a compromise between the fault-proneness estimation rate and the size of the estimation model in terms of number of metrics used in the model itself. To this end, two different methodologies have been used, compared, and some synergies exploited. The methodologies were the logistic regression and the discriminant analyses. The corresponding models produced for fault-proneness estimation and prediction have been based on metrics addressing different aspects of computer programming. The comparison has produced satisfactory results in terms of fault-proneness prediction. The produced models have been cross validated by using data sets derived from source codes provided by two application scenarios.

Optical music sheet segmentation

The optical music recognition problem has been addressed in several ways, obtaining suitable results only when simple music constructs are processed. The most critical phase of the optical music recognition process is the first analysis of the image sheet. The first analysis consists of segmenting the acquired sheet into smaller parts which may be processed to recognize the basic symbols. The segmentation module of the O/sup 3/ MR system (Object Oriented Optical Music Recognition) system is presented. The proposed approach is based on the adoption of projections for the extraction of basic symbols that constitute a graphic element of the music notation. A set of examples is also included.

Assessing Optical Music Recognition Tools

68 Computer Music Journal As digitization and information technologies advance, document analysis and optical-characterrecognition technologies have become more widely used. Optical Music Recognition (OMR), also commonly known as OCR (Optical Character Recognition) for Music, was first attempted in the 1960s (Pruslin 1966). Standard OCR techniques cannot be used in music-score recognition, because music notation has a two-dimensional structure. In a staff, the horizontal position denotes different durations of notes, and the vertical position defines the height of the note (Roth 1994). Models for nonmusical OCR assessment have been proposed and largely used (Kanai et al. 1995; Ventzislav 2003). An ideal system that could reliably read and “understand” music notation could be used in music production for educational and entertainment applications. OMR is typically used today to accelerate the conversion from image music sheets into a symbolic music representation that can be manipulated, thus creating new and revised music editions. Other applications use OMR systems for educational purposes (e.g., IMUTUS; see www.exodus.gr/imutus), generating customized versions of music exercises. A different use involves the extraction of symbolic music representations to be used as incipits or as descriptors in music databases and related retrieval systems (Byrd 2001). OMR systems can be classified on the basis of the granularity chosen to recognize the music score’s symbols. The architecture of an OMR system is tightly related to the methods used for symbol extraction, segmentation, and recognition. Generally, the music-notation recognition process can be divided into four main phases: (1) the segmentation of the score image to detect and extract symbols; (2) the recognition of symbols; (3) the reconstruction of music information; and (4) the construction of the symbolic music notation model to represent the information (Bellini, Bruno, and Nesi 2004). Music notation may present very complex constructs and several styles. This problem has been recently addressed by the MUSICNETWORK and Motion Picture Experts Group (MPEG) in their work on Symbolic Music Representation (www .interactivemusicnetwork.org/mpeg-ahg). Many music-notation symbols exist, and they can be combined in different ways to realize several complex configurations, often without using well-defined formatting rules (Ross 1970; Heussenstamm 1987). Despite various research systems for OMR (e.g., Prerau 1970; Tojo and Aoyama 1982; Rumelhart, Hinton, and McClelland 1986; Fujinaga 1988, 1996; Carter 1989, 1994; Kato and Inokuchi 1990; Kobayakawa 1993; Selfridge-Field 1993; Ng and Boyle 1994, 1996; Couasnon and Camillerapp 1995; Bainbridge and Bell 1996, 2003; Modayur 1996; Cooper, Ng, and Boyle 1997; Bellini and Nesi 2001; McPherson 2002; Bruno 2003; Byrd 2006) as well as commercially available products, optical music recognition—and more generally speaking, music recognition—is a research field affected by many open problems. The meaning of “music recognition” changes depending on the kind of applications and goals (Blostein and Carter 1992): audio generation from a musical score, music indexing and searching in a library database, music analysis, automatic transcription of a music score into parts, transcoding a score into interchange data formats, etc. For such applications, we must employ common tools to provide answers to questions such as “What does a particular percentagerecognition rate that is claimed by this particular algorithm really mean?” and “May I invoke a common methodology to compare different OMR tools on the basis of my music?” As mentioned in Blostein and Carter (1992) and Miyao and Haralick (2000), there is no standard for expressing the results of the OMR process. Assessing Optical Music Recognition Tools

A distributed environment for automatic multimedia content production based on grid

The containment of sale prices is a vital key when setting up a viable and sustainable business venture in the digital multimedia content. Possible solutions to this challenge could be found by automating, accelerating and restructuring the production process. With this aim, the AXMEDIS solution provides innovative methods and tools to speed up and optimize content production and distribution. The solution described in the paper was designed as a distributed environment based on the grid technology and promises an efficient management of multimedia services and production as well as the dynamic service discovery and composition, distributed resource management and adaptive media delivery.

Mobile Medicine: semantic computing management for health care applications on desktop and mobile devices

In many health care situations, powerful mobile tools may help to make decisions and provide support for continuous education and training. They can be useful in emergency conditions and for the supervised application of protocols and procedures. To this end, content models and formats with semantic and intelligence have more flexibility to provide medical personnel (both in off-line and on-line conditions) with more powerful tools than those currently on the market. In this paper, we are presenting Mobile Medicine solution, which exploits a collection of semantic computing technologies together with intelligent content model and tools to provide innovative services for medical personnel. Most of the activities of semantic computing are performed on the back office on a cloud computing architecture for: clustering, recommendations, intelligent content production and adaptation. The mobile devices have been endowed with a content organizer to collect local data, provide local suggestions, while supporting taxonomical searches and local queries on PDA and iPhone. The proposed solution is under usage at the main hospital in Florence. The smart content has been produced by medical personnel, with the adoption of the new ADF-Design authoring tool, which produces content in MPEG-21 format. The mobile content distribution service is integrated with a collaborative networking portal, for discussion on procedures and content, thus suggestions are provided on both PC and Mobiles (PDA and iPhone).

Micro Grids for Scalable Media Computing and Intelligence in Distributed Scenarios

Micro grid technology is playing a central role in the semantic evolution of small- and medium-sized services. This article presents the main requirements and architecture of micro grids for media and semantic computing.

A Language and Architecture for Automating Multimedia Content Production on Grid

Possible solutions to the management of multichannel delivering, production on demand, and containment of sale prices in the digital multimedia content production could be the automating, accelerating and restructuring the production process. The proposed solution provides innovative methods and tools to speed up and optimize content production and distribution based on the GRID technology supported by a specific programming language that allow defining the automatic procedure for content processing, production, adaptation, protection, DRM managing, distribution, etc. This paper describes the GRID architecture of the AXMEDIS Content Processing Area and the language adopted to define algorithms executed into the GRID environment

EXPLOITING INTELLIGENT CONTENT VIA AXMEDIS/MPEG-21 FOR MODELLING AND DISTRIBUTING NEWS

The content technology needs to attain forms with more intelligence, flexibility and complete features than those being currently on the market or proposed by standards. In this paper, an analysis of the state of the art about intelligent and complex content models is presented. The analysis allowed identifying a number of topics and features which models and formats should evolve according to. The work has been used to extend AXMEDIS content model and format which in turn is grounded on MPEG-21, SMIL, HTML, and other standards. The Extended AXMEDIS format presents a set of new features among them: semantic descriptors, extended annotations, intelligent behavioral and semantic computing capabilities. The newly obtained format has been compared against NewsML which is one of the most widespread formats for news production and distribution. The management of news has some peculiarities such as container, production tools and players, that may take advantage of the intelligent content features and applications. Moreover, news have to be massively processed for ingestion and repurposing, and present relevant requirements on right control. Also these features may be satisfied by AXMEDIS tools. To this end, a comparative analysis of processing and modeling NewsML with AXMEDIS tools and format has been performed and reported to verify the usage. In addition, AXMEDIS format can be profitably used for a range of innovative applications of intelligent content.

Automatic Music Transcription Supporting Different Instruments

Automatic music recognition from an audio performance is a key challenge for coding music in Western music notation in the digital world. This problem has been addressed in several ways, obtaining suitable results when a single specific instrument and monophonic music are processed. The development of a system for automatic music transcription that is able to cope with different music instruments is the aim of this article. Experimental results have shown that for monophonic pieces, recognition is quite viable and the process can be parameterized to realize a music-independent recognition tool and process.

Multimedia music distribution and sharing among mediateques, archives and their attendees

Archives of mediateques, theatres, music schools, conservatories, universities, etc., are the most important sources of cultural heritage. Such institutions are interested in digitizing content to (i) improve the service towards their attendees, thus increasing the number of collections provided, (ii) add new functionalities to the service already provided, (iii) save fragile materials otherwise settled to time deterioration, such as tapes, disks, documents, etc. Publishers are interested in digitizing only content that guarantees a certain return of investment and at the same time they are inclined to limit the distribution of the content located in archives, so as to control its exploitation and to preserve the content ownership. WEDELMUSIC has been defined to allow content providers to share and distribute interactive music in the respect of the owners rights. This means that networks of content sharing Mediateques can arise while respecting the owners rights. Consequently, solutions, models and tools were defined and developed, including adequate protection and monitoring solutions.