A STUDY OF QUALITY IN THE PROCESS OF SOFTWARE PRODUCT DEVELOPMENT ACCORDING TO MAINTAINABILITY AND REUSABILITY

Our research indicates the modality by which algorithm modifications imply intervention in modules where expressions are evaluated or selections of elements are performed, the conclusion being the fact that in order to design robust software, a clear definition of the modules is necessary. Thus, the weak module which can be easily modified must be defined and placed so as not to affect other modules through modifications applied to them. The reusability issue is even more important as the main software producing companies have developed class libraries which reduce programming efforts. It is thus possible to start the realisation of software with personnel no larger than 15 people, but with high qualification and logistical resources. The problem of reusability occurs especially in the interference area. In designing interfaces, graphic elements are dominant, as well as those of information search-find. All this implies the definition of text placement and designing parts of the text which determine actions or operation selection. The problem of reusability occurs when in new software products conversions, compressions, sorting and optimisations as operations with extremely low proportion in the computing volume must be introduced, but which represent significant consume from the point of view of the programming effort.endogenous growth, horizontal differentiation, technological change, imperfect competition, human capital

Network community detection via neural embeddings

Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work -- particularly how network structure gets encoded in the embedding -- remain largely unexplained. Here, we show that shallow neural graph embedding methods encode community structure as well as, or even better than, spectral embedding methods for both dense and sparse networks, with and without degree and community size heterogeneity. Our results provide the foundations for the design of novel effective community detection methods as well as theoretical studies that bridge network science and machine learning.Comment: 38 pages, 7 figure

Complexity vs. performance in granular embedding spaces for graph classification

The most distinctive trait in structural pattern recognition in graph domain is the ability to deal with the organization and relations between the constituent entities of the pattern. Even if this can be convenient and/or necessary in many contexts, most of the state-of the art classi\ufb01cation techniques can not be deployed directly in the graph domain without \ufb01rst embedding graph patterns towards a metric space. Granular Computing is a powerful information processing paradigm that can be employed in order to drive the synthesis of automatic embedding spaces from structured domains. In this paper we investigate several classi\ufb01cation techniques starting from Granular Computing-based embedding procedures and provide a thorough overview in terms of model complexity, embedding space complexity and performances on several open-access datasets for graph classi\ufb01cation. We witness that certain classi\ufb01cation techniques perform poorly both from the point of view of complexity and learning performances as the case of non-linear SVM, suggesting that high dimensionality of the synthesized embedding space can negatively affect the effectiveness of these approaches. On the other hand, linear support vector machines, neuro-fuzzy networks and nearest neighbour classi\ufb01ers have comparable performances in terms of accuracy, with second being the most competitive in terms of structural complexity and the latter being the most competitive in terms of embedding space dimensionality

From social machines to social protocols:Software engineering foundations for sociotechnical systems

The overarching vision of social machines is to facilitate social processes by having computers provide administrative support. We conceive of a social machine as a sociotechnical system (STS): a software-supported system in which autonomous principals such as humans and organizations interact to exchange information and services. Existing approaches for social machines emphasize the technical aspects and inadequately support the meanings of social processes, leaving them informally realized in human interactions. We posit that a fundamental rethinking is needed to incorporate accountability, essential for addressing the openness of the Web and the autonomy of its principals. We introduce Interaction-Oriented Software Engineering (IOSE) as a paradigm expressly suited to capturing the social basis of STSs. Motivated by promoting openness and autonomy, IOSE focuses not on implementation but on social protocols, specifying how social relationships, characterizing the accountability of the concerned parties, progress as they interact. Motivated by providing computational support, IOSE adopts the accountability representation to capture the meaning of a social machine’s states and transitions. We demonstrate IOSE via examples drawn from healthcare. We reinterpret the classical software engineering (SE) principles for the STS setting and show how IOSE is better suited than traditional software engineering for supporting social processes. The contribution of this paper is a new paradigm for STSs, evaluated via conceptual analysis

A Constrained Power Method for Community Detection in Complex Networks

For an undirected complex network made up with vertices and edges, we developed a fast computing algorithm that divides vertices into different groups by maximizing the standard “modularity” measure of the resulting partitions. The algorithm is based on a simple constrained power method which maximizes a quadratic objective function while satisfying given linear constraints. We evaluated the performance of the algorithm and compared it with a number of state-of-the-art solutions. The new algorithm reported both high optimization quality and fast running speed, and thus it provided a practical tool for community detection and network structure analysis

ANALYZING AND MANAGING FLEXIBILITY IN ENGINEERING SYSTEMS DESIGN BASED ON DECISION RULES AND STOCHASTIC PROGRAMMING: APPLICATIONS IN WASTE-TO-ENERGY SYSTEMS

Ph.DDOCTOR OF PHILOSOPH