Software assessment using metrics: A comparison across large C++ and Java systems

This paper presents an assessment method to evaluate the quality of object oriented software systems. The assessment method is based on source code abstraction, object–oriented metrics and graphical representation. The metrics used and the underlying model representing the software are presented. The assessment method experiment is part of an industrial research effort with the Bell Canada Quality Engineering and Research Group. It helps evaluators assess the quality and risks associated with software by identifying code fragments presenting unusual characteristics. The assessment method evaluates object–oriented software systems at three levels of granularity: system level, class level and method level. One large C++ and eight Java software systems, for a total of over one million lines of code, are presented as case studies. A critical analysis of the results is presented comparing the systems and the two languages.     

Probabilistic topic models for sequence data

Probabilistic topic models are widely used in different contexts to uncover the hidden structure in large text corpora. One of the main (and perhaps strong) assumption of these models is that generative process follows a bag-of-words assumption, i.e. each token is independent from the previous one. We extend the popular Latent Dirichlet Allocation model by exploiting three different conditional Markovian assumptions: (i) the token generation depends on the current topic and on the previous token; (ii) the topic associated with each observation depends on topic associated with the previous one; (iii) the token generation depends on the current and previous topic. For each of these modeling assumptions we present a Gibbs Sampling procedure for parameter estimation. Experimental evaluation over real-word data shows the performance advantages, in terms of recall and precision, of the sequence-modeling approaches.     

Comparison between low-cost marker-less and high-end marker-based motion capture systems for the computer-aided assessment of working ergonomics

The paper deals with the comparison between a high-end marker-based acquisition system and a low-cost marker-less methodology for the assessment of the human posture during working tasks. The low-cost methodology is based on the use of a single Microsoft Kinect V1 device. The high-end acquisition system is the BTS SMART that requires the use of reflective markers to be placed on the subject's body. Three practical working activities involving object lifting and displacement have been investigated. The operational risk has been evaluated according to the lifting equation proposed by the American National Institute for Occupational Safety and Health. The results of the study show that the risk multipliers computed from the two acquisition methodologies are very close for all the analysed activities. In agreement to this outcome, the marker-less methodology based on the Microsoft Kinect V1 device seems very promising to promote the dissemination of computer-aided assessment of ergonomics while maintaining good accuracy and affordable costs.     

Three-dimensional crack propagation with distance-based discontinuous kernels in meshfree methods

Distance fields are scalar functions defining the minimum distance of a given point in the space from the boundary of an object. Crack surfaces are geometric entities whose shapes can be arbitrary, often described with ruled surfaces or polygonal subdivisions. The derivatives of distance functions for such surfaces are discontinuous across the surface, and continuous all around the edges. These properties of the distance function were employed to build intrinsic enrichments of the underlying mesh-free discretisation for efficient simulation of three-dimensional crack propagation, removing the limitations of existing criteria (reviewed in this paper). Examples show that the proposed approach is able to introduce quite convoluted crack paths. The incremental nature of the developed approach does not require re-computation of the enrichment for the entire crack surface as advancing crack front extends incrementally as a set of connected surface facets. The concept is based on purely geometric representation of discontinuities thus addressing only the kinematic aspects of the problem, such to allow for any constitutive and cohesive interface models to be used.     

A Meshless Cohesive Segments Method for Crack Initiation and Propagation in Composites

A modeling method aimed at eliminating the need of explicit crack representation in bi-dimensional structures is presented for the simulation of the initiation and subsequent propagation within composite materials. This is achieved by combining a meshless method with a physical stress–displacement based criterion known as Cohesive Model. This model consents to apply a penalty-based approach to delamination modeling where a variable penalty factor along the crack segment allows to loosen or tight the two parts according to their relative displacements. Results are showed for classical single mode loading benchmark cases and compared to experimental results taken from the literature.     

On the intersubject generalization ability in extracting kinematic information from afferent nervous signals

In the recent past, many efforts have been carried out in order to evaluate the feasibility of implementing closed-loop controlled neuroprostheses based on the processing of sensory electroneurographic (ENG) signals. The success of these techniques mostly relies on the development of processing algorithms capable of extracting the necessary kinematic information from these signals. Soft-computing algorithms can be very useful when dealing with the complexity of the neuromuscular system because of their generalization ability and model-free structure. In this paper, these techniques were used to extract angular position information from the ENG signals recorded from muscle afferents in animal model using cuff electrodes. Specifically, a genetic algorithm-based dynamic nonsingleton fuzzy logic system (named GA-DNSFLS) was developed and tested on different types of angular trajectories (characterized by small or large angular excursions). In particular, two different Takagi-Sugeno-Kang (TSK)-like structures were used in the consequent part of the neuro-fuzzy model in order to verify which one could improve the generalization abilities (intrasubject and intersubject). The results showed that the GA-DNSFLS was able to reconstruct the trajectories giving interesting results in terms of correlation between the actual and the predicted trajectories for small excursion movements during intrasubject and intersubject tests. Particularly, one of the TSK models showed better results in terms of intersubject generalization. The simulations conducted with the large excursion movements led in some cases to interesting results but further experiments are necessary in order to analyze this point more in deep.     

From global to local and viceversa: uses of associative rule learning for classification in imprecise environments

We propose two models for improving the performance of rule-based classification under unbalanced and highly imprecise domains. Both models are probabilistic frameworks aimed to boost the performance of basic rule-based classifiers. The first model implements a global-to-local scheme, where the response of a global rule-based classifier is refined by performing a probabilistic analysis of the coverage of its rules. In particular, the coverage of the individual rules is used to learn local probabilistic models, which ultimately refine the predictions from the corresponding rules of the global classifier. The second model implements a dual local-to-global strategy, in which single classification rules are combined within an exponential probabilistic model in order to boost the overall performance as a side effect of mutual influence. Several variants of the basic ideas are studied, and their performances are thoroughly evaluated and compared with state-of-the-art algorithms on standard benchmark datasets.