A Software Product Line (SPL) represents a family of products in one domain. It is often specified through a feature model representing the end-user system characteristics. Most proposed feature model construction methods examine a set of product variants code to identify common blocks of elements each of which represents a feature. In this identification process, they adopt different similarity measures and clustering techniques. Consequently, they differ in the granularity and cohesiveness of the identified features, which in turn affect the structure of the constructed feature model. Evidently, this difference also impacts the reuse of the SPL when deriving a new product. This paper compares widely adopted textual clustering techniques in terms of the recall and precision in their identification of the features. More specifically, it experimentally examines Latent Semantic Indexing (LSI), Principal Component Analysis (PCA), Support Vector Machine (SVM)and Term Frequency-Inverse Document Frequency (TF-IDF). The experimental results showed that LSI has better performance than the other textual similarity techniques with an average precision rate of 68.6

Software project scope defines functional and non-functional requirements. These requirements may change to satisfy the customers' needs. However, the control of scope creep represents one of the success keys in software project management. Changes in non-functional requirements affect the ISO/IEC 25010 quality characteristics such as security, portability, etc. Furthermore, some of these quality characteristics may evolve throughout the software life cycle into functional requirements. In this paper, we explore the use of COSMIC method - ISO/IEC 19761 to quantify and evaluate security change requests. Measuring the functional size of security change requests allows stakeholders to make appropriate decisions about whether to accept, defer, or deny the change.

Social business processes support enterprises tap into the opportunities of Web 2.0. To ensure an efficient support, this paper presents an approach for identifying an enterprise’s business processes that could be converted into social. Indeed, not all processes are eligible for this conversion. The approach is built upon the concept of enterprise architecture that sheds the light on the necessary contextual elements for guiding the socialization of business processes. Due to the fuzzy nature of socialization, two multi-criteria decision making techniques, fuzzy logic and fuzzy-decision tree, enhance this guidance. A set of experiments for evaluating the proposed approach are presented in the paper, as well.

Skin lesion segmentation is a key step in a diagnosis system based on dermoscopic images. This paper proposes a method to detect the skin lesion accurately. The images are first cleansed to remove noise. Then, pertinent features are extracted from RGB, HSV and XYZ color spaces. Cluster analysis is used for segmentation. We take advantage of the multiple density clustering algorithm MDCUT [1] to solve the problem of image segmentation using region growing. We demonstrate how MDCUT algorithm is used to automatically determine the needed parameters for region growing image segmentation. Experiments on medical skin lesion image and comparison with the ground truth segmentation results demonstrate the validity of our method.

Like any software product, the quality of Business Process (BP) models is an important issue that must be addressed. Indeed, a poor-quality BP model can disturb its implementation and execution as well as its performance. In the literature, different studies focused on the evaluation of BP model's quality using quality metrics. The common challenge highlighted by these studies is the lack of a consensus on the thresholds of the used quality metrics and the difficulty of interpreting the values of these metrics. In this paper, we propose an approach that uses existing quality metrics to evaluate the quality of BP models in terms of comprehensibility and modifiability. Besides, we determine the thresholds of the different quality metrics through decision tree technique. Decision tree also produces a set of decision rules that are useful to determine the quality level of BP model. The approach is validated through a tool named BP-Quality that calculates the values of the quality metrics and deduces the comprehensibility and modifiability levels based on calculated values and the aforementioned thresholds.

The necessity of aligning an enterprise's IS model to its business process model (BPM) is irrefutable.How to ensure the establishment and/or maintenance of this alignment remains, however, a pressing need for enterprises seeking to establish a new IS, better govern its enterprise architecture, and/or update its existing IS face to business-driven changes.The main difficulty of establishing/maintaining BP-IS models alignment stems from the divergent knowledge domains of the stakeholders (business process experts and software developers).To bridge the gap between these two stakeholders, this paper proposes an MDA compliant approach to automate the generation of UML class diagrams from BPMN models.The generated IS design can be used either to establish a new IS system, or analyze or maintain an existing one.The generation is defined in terms of transformations that ensure the alignment of the class diagram to the BPMN model by both accounting for the semantics and structure of the BPMN model, and providing for all business objects and activities.

Software product lines (SPLs) identify and manage the commonalities and variability, called features, among the variants of products in a given domain. This reuse technique improves productivity factors such as reducing costs and time to market while enabling the derivation of particular applications that meet customers' needs by reusing the domains' artifacts. In this paper, we tackle the problem of SPL extraction from language-independent functional descriptions of existing product variants. Our contribution consists in synthesizing the SPL feature model from possibly incomplete requirements (use case diagrams, scenarios and functional requirements) of the product variants. To validate our approach, we applied it on five case studies: ArgoUML-SPL (small-scale system), Mobile Media-SPL and Messaging-SPL (medium scale systems), Health complaint-SPL and Crisis management-SPL (large scale systems). For this purpose, we used several releases from these domains as the considered product variants. Then, we applied our approach and evaluated its efficiency through measurements.

Configurable processes are increasingly being adopted by enterprises that seek experience sharing and best practice adoption. A configurable process is a customizable model that specifies how different enterprises perform similar processes. At the modeling level, a configurable process model provides for flexible business process (BP) reuse by (de)selecting the (ir)relevant parts to derive a particular process variant. At the exploitation level, it offers flexibility and agility to an enterprise looking to outsource its BP to different providers cooperating in a cloud federation. More specifically, an enterprise can use a configurable process model to derive particular process variants that it outsources depending on its objectives. In particular, it may opt for outsourcing the variant that results in the optimal deployment, e.g., having the minimal cost of allocated cloud services that fulfill the user quality of service (QoS) requirements. However, identifying the optimal deployment variants is a complex problem because of the heterogeneity of services within a cloud federation and the number of possible variants that can be derived from a configurable process model. In addition, the complexity of this problem increases for variable user QoS requirements. In this paper, we propose an approach to derive, from a configurable process model, the variant that has the optimal deployment in a cloud federation. We propose a linear programming approach that accounts for the variability of both the BP model and the user QoS requirements, and that ensures an optimal time-aware cloud service allocation. We experimentally show the effectiveness and flexibility of our approach on a generated testbed.

A cloud service is usually classified as Infrastructure as a Service, Platform as a Service or Software as a Service. This classification is not sufficient, when Business Processes are considered. Therefore, the next level of abstraction above SaaS is Business Process as a Service (BPaaS) [1]. BPaaS runs in a dynamic and volatile environment such as cloud computing and has hence very changing QoS (Quality of Service) parameters. Therefore, the BPaaS monitoring and adaptation are of a great importance to guarantee the QoS parameters defined in the Service Level Agreement (SLA). To address these issues, in this paper we proposed two Fuzzy systems for self-adaptive BPaaS that aim to prevent the SLA violations. The proposed approach objective is to satisfy two important issues: (i) the selection of service adaptation strategies and (ii) the cost as well as the impact of changes. We also presented the results of the experiments achieved to evaluate the proposed approach. These experimental results prove the effectiveness of the fuzzy systems.

To help developing continuous improvement in Information and Communication Technology, new software are required with functionality and characteristics different from the traditional one. Thus, measurement procedures need to be tailored to this new computing context. In fact, the software functional size is one of the important cost drivers of software development projects. An accurate measurement needs a detailed definition of software requirements and may require an important measurement effort. However, this is not always available. Thus, well defined measurement procedures requiring little effort and providing approximate or rapid measurement results are needed. The COSMIC Functional Size Measurement (FSM) method has knew a great success compared to other methods since it addresses different types of software including "business application", "mobile apps", etc. Moreover, it has been successfully used with UML diagrams for different objectives. In this paper, a Use Case based measurement procedure is proposed in order to estimate the functional size of mobile and web applications using COSMIC FSM method. The proposed procedure helps both beginners and experienced measurer in applying COSMIC and avoiding measurement errors. It is based on a set of measurement formulas tested and validated through the case study "Restaurant Management System".

Because of their structural and semantic inter-dependency, UML diagrams describing a software product must be consistent to ensure the success of the development process. This inter-diagram consistency is often not easy to ensure considering the multi-views the diagrams cover, the different levels of abstraction they encode, and the different development phases during which they are produced. In this paper, we tackle the consistency problem between the functional and dynamic views in behavioral diagrams of software. We propose an approach to detect and localize inter-consistency errors among UML use cases and activity diagrams. This approach is composed of two phases: measurement phase (1) and controlling phase (2). This approach is illustrated and validated through a “Restaurant management system” case study. The proposed approach will help both software designers/developers and quality assurance/testing engineers identifying and localizing the causes of software errors at early phases (specification and design).

A biometric identification system is an automatic pattern recognition system that identifies a person through their specific physiological and/or behavioral characteristics. Unimodal biometric system often suffers from some limitations due to noise in sensed data, intra-class variation, inter-class similarities, spoof attacks, etc. Multi-biometric systems seek to overcome some of these limitations by providing multiple pieces of evidence about the person identity. The increased performance of multi-biometric systems motivated our investigation of a new approach for multi-biometric identification systems based on the combination of five finger surface instances of a person. The proposed approach has the particularity of using a new fusion scheme based on the rank level integration method to consolidate the results obtained from the different five biometric instances. To the best of our knowledge, this is the first time the rank level fusion approach is designed to incorporate the results of five finger surface instances to produce more reliable recognition results. To highlight the contributions of our approach, this paper presents a comparative experimental study of several rank level fusion approaches that can be useful in combining multi-biometric systems. The experimental evaluation was conducted on the real hand database ‘Sfax-Miracl hand database’. The experimental results suggest that our novel approach produces a significant performance improvement in the recognition accuracy over approaches based on individual finger surface. They also indicate that the rank level integration of the five finger surfaces is poised to provide a promising direction to finger surface-based multi-biometric systems.

Recommendation systems provide suggestions for items that are potentially interesting for a user in a given context. The provided recommendations are extracted generally from a huge amount of data collected from several sources of information. Thus a recommendation system requires firstly a pre-treatment step to prepare the data and secondly the application of some techniques such as data mining techniques to handle and extract the knowledge to be recommended to the user from the data. Our contribution consists on proposing a Recommendation System for Software Engineering (RSSE). This system recommends UML classes in the design phase of UML classes diagrams. Our RSSE is composed by two main phases: an off-line phase in which we use a clustering algorithm to partition UML classes collected from several UML classes diagrams based on the semantic relations existing between their characteristics. We have defined a metric that measures the similarity between UML classes. The second is an online phase in which we use the obtained clusters of UML classes to propose suggestions to the user based on elements added to his UML classes diagram under construction. The proposed system is then experimentally evaluated by using a UML classes corpus collected from several UML classes diagrams. The experimental evaluation shows very encouraging ratio of useful recommendations.

UML diagrams became a common part of software requirement documentation, implementation, etc. In particular, the Use Case Diagram (UCD) is considered as the de-facto standard for modelling the user requirements at an early phase of the Software Development Life Cycle (SDLC). Each use case can be detailed by an Activity Diagram (UAD). Because Functional Changes (FC) are inevitable during the SDLC, it is required to identify the FC status at different levels of granularity. In our previous work, we proposed an approach for measuring the functional size of a FC in the UAD using COSMIC method. In this paper, we propose to extend this approach by including the UCD. In addition, we predict the status of a FC in UAD from its use case. This approach is applied before the FC implementation. It allows a rapid and an approximate evaluation of how important the FC is and it helps designers/managers in decision-making to answer the FC request.

Similar to any software system, the success of the development of a decisional information system depends on the quality of its specification, often modeled as star schemes. In this paper, we present a new approach assisted by Prolog in order to verify the conformity of multidimensional star schemas with its source schema and to check summarizability constraint. In addition, we propose a tool to assist decision makers to design valid schemas. Our tool presents a new idea to check summarizability constraints and to generate a warning messages to inform the decision makers about quality of results of the future decisional system.

The paper deals with the Knapsack Problem with conflicts, also known as the Disjunctively Constrained Knapsack Problem (DCKP). The conflicts are represented by a graph whose vertices are the items such that adjacent items cannot be packed in the knapsack simultaneously. We consider a classical formulation for the DCKP, study the polytope associated with this formulation and investigate the facial aspect of its basic constraints. We then present some valid inequalities. Based on this study, we discuss separation routines of the valid inequalities and devise a Branch-and-Cut algorithm. Preliminary results on a set of problem instances are also given.

A Social Business Process (SBP) is the result of blending social computing (a.k.a. Web 2.0) with business process (BP). Despite the benefits of SBP to enterprises, several limitations continue to undermine them. In this paper, we address two specific limitations, namely the difficulty of capturing SBP's requirements and the lack of a definition for SBP. Thus, meta-modeling is used to capture requirements from organizational, technological, and management perspectives. In addition, we introduce a definition for SBP by enriching an existing BP meta-model with social concepts. To annotate the SBP model with its requirements, a BPMN extension is proposed. The proposed meta-models are evaluated in terms of completeness and clarity using the Bunge-Wand-Weber ontology.

Because of software requirements play a critical role in software development projects, measuring the non-functional requirements as well as functional requirements is therefore not to be trifled with. Software security as a non-functional requirement is one of the most important quality characteristic that is recently added in the ISO 25010 quality models (previously defined as sub characteristics in ISO 9126). This characteristic must be evaluated cautiously and precisely during all the software life-cycle and especially early in the design phase. The purpose of this paper is early evaluating security in web application. To achieve this purpose, we propose to measure the quality attributes of authenticity through a combination of functional and structural size of the authenticity sequence diagram at the design phase. This combination of measurement can be used to identify the risk of violation of authenticity in web application design. An example of GeoNetwork web application is used to illustrate our proposed measurement for evaluating security as defined by ISO/IEC 25010.

The reuse of design in the software product line (SPL) domain brings many benefits because it permits a clear understanding of the SPL. These SPL contain a set of common elements and a set of variable elements known as features. One of the main difficulties for representing SPL design is the variability. In order to express the variability in a SPL design and to reinforce its comprehension, it is necessary to define a design language that aims to model SPL and to distinguish the commonalities and variability. Accordingly, we present in this paper a new profile containing UML extension named SPL-UML that offers extended diagrams enriched by the information extracted from the feature models which lets us differentiate between the common points and variabilities of the SPL. The coherence between the proposed extensions is then ensured by OCL (Object Constraint Language) constraints. Finally, the UML extensions are illustrated using an example of TextEditing System.

When enterprises decide to outsource their business processes to the Cloud, various considerations should be tackled. Indeed, the enterprises aim to reduce the business processes investment cost, to enhance their performance, and to focus on the enterprise core competency while considering security constraints. Hence, it is essential to assist enterprises to take the suitable decision by providing an appropriate decision system that specifies the activities to be outsourced as well as the Cloud resource to support them while considering the above enterprise preferences. Obviously, the outsourcing decision, when taken in a specific business process context, may be influenced by some variants that make it not suitable in another one. For instance, the business process workloads vary according to its execution period and thus the decision may require to change the Cloud resources as well as the outsourced activities to fit the new context requirements. This consideration should be taken when tackling an outsourcing decision to alleviate enterprise experts from the burden of assessing by themselves the changing business processes context and react by consequence to this change. In this paper, we present an adaptive outsourcing decision system, which provides personalized and autonomic decision-making to support the dynamic business process context when outsourced to the Cloud. The system predicts the business process context and provides accordingly appropriate decisions using the penalty based genetic algorithm.

Feature models (FMs) are widely used to describe software product lines (SPL). They specify common and variation points in the domain of the SPL. Because of their wide domain coverage, FM development requires extensive domain analysis and important domain expertise that is often hard to acquire. These development requirements motivated us to propose, in this paper, a top-down method that extracts automatically an FM from functional requirements of product variants. Besides its automation, its novelty stems from the use of semantic information mined through natural language processing techniques to extract potential features from each product variant. To account for name variations, our method harmonizes the names of the features extracted from the product variants by using a classification technique to group similar features. In addition, to determine the feature type, it uses the formal concept analysis technique to distinguish mandatory from optional features. Furthermore, to structure the FM, it uses a set of semantic criteria to determine the constraints among the features. The paper reports on a quantitative and a comparative evaluation of the method on existing FMs, and it examines the conformity of the generated FMs to the input functional requirements, based on experts’ feedbacks.

The growing interest in cloud-based data warehousing is driven by the high return on investment. Nonetheless, the adoption of cloud computing for data warehousing faces security challenges given the proprietary nature of the enclosed data. This paper first, presents an adaptive security solution for sensitive data in cloud-based data warehouses. Second, it illustrates how the security solution can be implemented as a service through a model-driven approach.

Refactoring a business process model may enhance the understandability and/or ease the maintainability of the process. So far proposed refactoring approaches tackled only the structural aspect of a model. As we illustrate in this paper, combining the semantic aspect with the structural aspect further reduces the control flow complexity of a business process modeled in the Business Process Modelling Notation. Our refactoring method adapts a set of rules from the graph optimization domain. We illustrate the efficiency of a subset of the proposed rules through well-established complexity metrics.

Cloud computing offers generous computing resources needed to deploy data warehouses efficiently. However, security remains a key challenge for a widespread adoption of the Cloud for data warehousing. In this paper, we extend BPMN to provide for modelling secure Extract-Transform-Load processes deployed as web services in a Cloud environment. In addition, following a model-driven engineering approach, we outline how the ETL model can be transformed into a set of services to be deployed in the Cloud.

The efficient monitoring of Cloud infrastructure is a topic that is currently attracting significant interest. Understanding the behaviour of the variety of monitoring tools and how to manage them optimally are challenging tasks. Monitoring tools and techniques have an important role to play in this area by gathering the information required to make informed decisions. Surveying these monitoring tools can identify the fitness of these tools in serving certain objectives for both Cloud providers and consumers in different Cloud operational areas.

Software Product Lines (SPL) offer the ability to create, maintain and evolve a set of similar products with the simplicity of a single system rather than the complexity of deploying a multitude of products. It is well recognized by the research community interested in SPLs that designing SPLs with the standard UML language do not provide for a clear understanding of the SPL. This motivated us to propose SPL-UML, a profile that models the variability in UML designs for software product lines (SPL). The profile offers extended diagrams enriched by the information extracted from the feature models that lets you differentiate between the common points and variabilities of the SPL. Our profile is illustrated through its application in the design of a Mobile Media SPL.

Face to the increasingly stringent business competition, Small and Medium Sized Enterprises (SMEs) strive to excel in the marketplace by adopting different strategies and solutions. Outsourcing their business processes to the Cloud is considered as a widely adopted strategy. Among others, SMEs outsource their related business process to improve their performance. However, this strategy is not without inconvenience especially when the decision is taken without being aware of the business process requirements and the experts preferences. This paper's major contributions are: the proposition of a monitoring tool to identify the defaulting activities causing the degradation of the business process performance. These information are used later to favor the outsourcing of defaulting activities as the Cloud environment may be a better alternative environment instead of the enterprise one as it can provide a better IT infrastructure with lower cost compared to the in-house one. Furthermore, we propose a decision model using the Analytic Hierarchy Process (AHP) to assist experts in the fastidious task of selecting suitable activity to be outsourced according to the identified requirements and the experts preferences. It resorts essentially to commonly used standards which make it a powerful tool for both IT and business experts. An evaluation of the results is elaborated to insure the utility of the elaborated tool using the CloudSim toolkit.

This paper describes the design and delivery of two competition-based small offensive security exercises in an undergraduate Computer and Information Security course at the Faculty of Computing and Information Technology, King Abdulaziz University. We designed competition scenarios for two small exercises based on known attacks. The first exercise aimed to break the Windows Server 2008 password, and the second sought to break the Wired Equivalent Privacy (WEP) wireless network key (password). We present the competition scenarios and design, including the required hardware and software in each exercise. In addition, we give an overview about the attacks and possible defenses against them. We also present the results of a survey conducted to determine students' sentiments towards these types of exercises and to measure the effectiveness of these exercises in supporting the course's theoretical concepts from the student perspective. The results strongly suggest that the exercises were informative, motivating, stimulating, and enjoyable. This work was only the first step for us. We look forward to creating more challenging competition-based exercises and rewarding the teams that put forth superior efforts.

Several real-time embedded system must be dynamically reconfigured to account for hardware/software faults and/or maintain acceptable performances. Depending on the run-time environment, some reconfigurations might be unfeasible, i.e., they violate some real-time constraints of the system. In this paper, we deal with the development of dynamically reconfigurable embedded systems in terms of the production of execution schedules of system tasks (feasible configuration) under hard real-time constraints. More specifically, we propose an approach that starts from a set of reconfigurations to construct a Software Product Line that can be reused in a predictive and organized way to derive real-time embedded systems. To make sure that the SPL offers various feasible reconfigurations, we define an intelligent agent that automatically checks the system's feasibility after a reconfiguration scenario is applied on a multiprocessor embedded system. This agent dynamically determines precious technical solutions to define a new product whenever a reconfiguration is unfeasible. The set of products thus defined by the agent can then be unified into an SPL. The originality of our approach is its capacity to extract, from the unfeasible configurations of an embedded system, an SPL design enriched with real-time constraints and modeled with a UML Marte profile. The SPL design can assist in the comprehension, reconfiguration as well as evolution of the SPL in order to satisfy real-time requirements and to obtain a feasible system under normal and overload conditions.

An automated change impact analysis and management approach is vital to handle the complexity of adapting software during its evolution.Such an approach reduces the maintenance cost and provides for adequate decision making when confronted with the choice of accepting or ignoring changes.This paper presents a change impact management approach between UML models.It verifies the consistency and the quality of interdependent diagrams after a change is handled.In addition, it calculates the effort required in managing any change and displays a report indicating to the designer all necessary modifications to keep the design coherent.The approach is supported by a toolset, called CQV-UML tool.

A BP is a series of logically related tasks implemented by a set of applications/services performed together to produce a defined set of results. The cloud resources scheduling to BP tasks is a difficult problem. Due that, first, it considers the dependencies and communication between tasks within a BP. Second, it takes into account several objectives like minimizing the execution time, minimizing the execution cost, maximizing the resource utilization. Besides, BP execution can be affected by a set of contextual information such as the unavailability of resources, the overloading of network, etc. which make the scheduling problem more complex. In this paper, we propose a context-based scheduling approach for adaptive BP in the cloud.

Cloud computing is increasingly being used to deliver infrastructure, platform and/or software as services over the Internet. The resulting Cloud-based services with various types create complex management situations at the Cloud provider side. In particular, face to a large and dynamic number of service loads, a Cloud provider needs a means to maintain QoS (Quality of Service) levels it has agreed-upon with its customers. To properly operate and manage such complex situations, an effective and efficient monitoring is necessary. Most of the monitoring propositions are for the infrastructure and platform layers. This paper presents a multi tenant framework for QoS Monitoring and Detection of SLA Violations (QMoDeSV) for composite services implemented as BPEL (Business Process Execution Language) processes and deployed in a Cloud environment. It presents the complete design of QMoDeSV and a preliminary experimental evaluation of its performance.

Software Product Lines (SPL) ensure predictive and organized software reuse. In practice, SPL are often set up after several similar product variants have been in use. This practical fact prompted a quest for bottom-up processes that start from existing the source of product variants to identify a product line. This later is then described with a feature model that essentially specifies the components of the SPL and their variability within the product family. However, so far proposed notations for feature models do not provide for a clear understanding of the SPL nor do they guide in their maintenance and future evolution. These shortages motivated us to propose a bottom-up approach that extracts from the source code of product variants, the SPL design enriched with information extracted from the feature model. The enriched SPL is modeled with a UML profile that assists in the comprehension, reuse as well as evolution of the SPL.

Hand detection is the first step of any hand biometric recognition process, which determines the outcome of the following treatments. In this paper, we propose a robust method for hand detection without contact and without constraints on the capture environment for hand biometric applications. This method is based on a color based approach adopting a non-parametric modeling. Our contribution consists mainly in the choice of the most relevant color axes and the choice of the decision rules automatically using a process of the data-mining as a new philosophy of data processing. To improve the achieved results of skin detection and to determine the hand region in the image, a succession of post-processing was proposed. Our hand detection method was evaluated experimentally on a real database; the outcomes of this evaluation show promising results and demonstrate the effectiveness of the proposed method.

This paper introduces a novel low-computation discriminative regions representation for expression analysis task. The proposed approach relies on interesting studies in psychology which show that most of the descriptive and responsible regions for facial expression are located around some face parts. The contributions of this work lie in the proposition of new approach which supports automatic facial expression recognition based on automatic regions selection. The regions selection step aims to select the descriptive regions responsible or facial expression and was performed using Mutual Information (MI) technique. For facial feature extraction, we have applied Local Binary Patterns Pattern (LBP) on Gradient image to encode salient micro-patterns of facial expressions. Experimental studies have shown that using discriminative regions provide better results than using the whole face regions whilst reducing features vector dimension.

International audience

Hand detection is the first step of any hand biometric recognition process, which determines the outcome of the following treatments. In this study, the authors propose a robust method for hand detection without contact and without constraints on the capture environment. This method is based on a data‐mining process for skin‐colour modelling. The presented data‐mining process offers several advantages like the choice of the most relevant colour axes and the automatic choice of the decision rules. To improve the achieved results of skin detection and to determine the hand region in the image, a succession of postprocessings was proposed. The authors hand detection method was evaluated experimentally on a real database, namely, ‘Sfax‐Miracl hand database’; the outcomes of this evaluation show promising results and demonstrate the effectiveness of the proposed method.

Design patterns capitalize the knowledge of expert designers and offer reuse that provides for higher design quality and overall faster development. To attain these advantages, a designer must, however, overcome the difficulties in understanding design patterns and determining those appropriate for his/her particular application. On the other hand, one way to benefit from design patterns is to assist inexperienced designers in pattern detection during the design elaboration. Such detection should tolerate variations between the design and the pattern since the exact instantiation of a pattern is infrequent in a design. However, not all variations of a pattern are tolerated. In particular, some structural variations may result in non-optimal instantiations where the requirements are respected but the structure is different; such variations are called spoiled patterns and should also be detected and transformed into acceptable pattern instantiations. This paper first presents an improvement of our design/spoiled pattern detection approach, named MAPeD (Multi-phase Approach for Pattern Discovery). The latter uses an XML information retrieval technique to identify design/spoiled pattern occurrences in a design using, first, static and semantic information and, secondly, dynamic information. This multi-phase detection approach tolerates structural differences between the examined design and the identified design pattern. Furthermore, thanks to the matching information it collects, our identification technique can offer assistance for the improvement of a design. In its second contribution, this paper evaluates MAPeD by comparing its recall and precision rates for five open source systems: JHotDraw, JUnit, JRefactory, MapperXML, QuickUML. The latter were used by other approaches in experimental evaluations. Our evaluation shows that our design pattern identification approach has an average improvement of 9.98% in terms of precision over the best known approach.

In this paper we deal with the problem of lowering down the difficulty of face detection in video. Most of the recently developed systems swap detection accuracy for higher speeds, or vice versa. We have proposed a robust approach which makes use of spatial and temporal information in video to reduce time execution and improve precision rate. Our experiments show that our proposed approach proves efficiency without sacrificing real-time performance which makes it well-suited for live video applications.

Obstacle detection is an important component in driver assistance as it helps systems to locate obstacles and then to prevent collisions. The aim of this study is to develop an obstacle detection module through digital images processing. We present a hybrid stereo vision-based method that combines stereo matching and homographic transformation methods. We use a sparse matching method in order to get a rapid geometric representation of the road scene that allows us to extract the upper and lower parts of obstacles. According to the position of the lower part, our method uses either the dense stereo matching or the homographic transformation methods to extract the candidate obstacles regions. A verification test is performed to verify whether the retained region is an obstacle or not. In order to avoid collisions, we compute the distance to the preceding obstacle to maintain the vehicle carrying the camera at a safety distance. The method presented here was tested on DIPLODOC road stereo sequence captured on a highway. The obtained results prove the efficiency of our proposed method.

Goal-oriented modeling is a promising approach to elicit the analytical requirements of decision makers. However, the lack of a technique that formalizes the decision making knowledge necessary for goal elicitation hinders the automation of this type of approaches. In this paper, we propose a process for building a decision making ontology that represents the domain knowledge. The formal aspect of the ontology makes the domain knowledge machine readable, which facilitates the decomposition of goals and automates their elicitation. The usefulness of the ontology in analytical requirements elicitation is assessed through an experimental evaluation.

In this paper, we propose a new ant based clustering algorithm. The algorithm takes inspiration from the sound communication properties of real ants. Artificial ants communicate directly with each others in order to merge similar group of objects. The proposed algorithm was tested and evaluated. The obtained results are very encouraging in comparison with the famous k-means and some ant based clustering algorithms.

Software functional size (FS) prediction early in its lifecycle is vital for software project management. Such prediction requires the definition of software measures in terms of the specification and/or design language concepts. Within this context, several researchers have projected COSMIC functional size measures (FSM) onto various UML diagrams. However, these projections treated the diagrams separately despite their syntactic and semantic overlap within a model. In this paper, we present a fine-grain measurement for the UML use case diagram. Being a central diagram for models derived through the unified process, the use case diagram is implicitly related to all other diagrams. Thus, the detailed measurement of this diagram provides a reference measurement standard for other UML diagrams, e.g., the sequence and class diagrams.

In this paper, we propose a new approach to identify programs in TV streams. In the first step of our approach, we construct a reference catalogue for video grammars of visual jingles. In the second step, we identify programs in TV streams by examining the similarity of the video signal to the visual grammars in the catalogue. After presenting our approach, we report the results of its experimental evaluation on several streams extracted from different channels and composed of several programs.

Design patterns provide for architecture reuse of reoccurring design problems. They were proposed to improve software quality and accelerate software development. However, the difficulty of their comprehension reinforces the need for a suitable design language. The new design language has to express certain concepts specific to patterns, e.g., their reuse variability and context of applicability, which cannot be expressed with UML. This paper overviews currently proposed representations for design patterns, that try to express pattern specific concepts. It then proposes a UML-based design language that better represents the design patterns. This latter is supported by a CASE toolset that both helps in pattern representation and guides a pattern reuse. The proposed language and toolset are illustrated through the graphical drawing editor JHotDraw, a framework for graphical editors.

Summary form only given. Object-oriented frameworks are recognized as a promising technique for architectural reuse. To guide a framework reuse and help in the traceability of a model, we have proposed an UML profile for framework design, called F-UML. In addition, to provide for precise analysis and reuse validation, we defined a formal semantics for the class diagram of F-UML. In this paper, we complete the formalization of F-UML in object-Z. We, then, show how the formal semantics can be used to analyze both syntactic and semantic/domain specific properties of a framework, and to validate a framework reuse.

Frameworks offer reuse through the generality they have to encompass. This same property, however, often makes a framework design fairly complex, hard to understand and, hence, to reuse. This paper briefly presents the F-UML design. It then focuses on the definition of the formal semantics of F-UML. This latter is defined through a translation of the meta-model of F-UML to Object-Z. The Object-Z semantics allows a designer to prove the syntactic well-formedness of an F-UML design. In addition, it allows the verification of several design properties through a theorem prover.

Frameworks are a promising technology for software design reuse. However, their difficult design may impede their widespread use in software engineering. This paper presents a framework design process for F-UML, an object-oriented design language that offers graphical annotations to guide framework reuse. The F-UML design process is based on a stepwise, bottom-up unification by applying a set of comparison rules on various applications in the framework domain.

We describe the Monitoring and Checking (MaC) framework which provides assurance on the correctness of an execution of a real-time system at runtime. Monitoring is performed based on a formal specification of system requirements. MaC bridges the gap between formal specification, which analyzes designs rather than implementations, and testing, which validates implementations but lacks formality. An important aspect of the framework is a clear separation between implementation-dependent description of monitored objects and high-level requirements specification. Another salient feature is automatic instrumentation of executable code. The paper presents an overview of the framework, languages to express monitoring scripts and requirements, and a prototype implementation of MaC targeted at systems implemented in Java.

The Graphical Communicating Shared Resources, GCSR, is a specification language with a precise, operational semantics for the specification and analysis of real-time systems. GCSR allows a designer to integrate the functional and temporal requirements of a real-time system along with its run-time resource requirements. The integration is orthogonal in the sense that it produces system models that are easy to modify, e.g., to reflect different resource requirements, allocations and scheduling disciplines. In addition, it renders the verification of resource related requirements natural and straightforward. The formal semantics of GCSR allows the simulation of a system model and the thorough verification of system requirements through equivalence checking and state space exploration. This paper reviews GCSR and reports our experience with the production cell case study.

Graphical communicating shared resources, GCSR, is a formal language for the specification and analysis of real-time systems including their functional and resource requirements. GCSR allows a modular and hierarchical, and thus, scalable specification of a real-time system. GCSR supports notions of communication through events, interrupt, concurrency, and time to describe a real-time system. In addition, GCSR allows the explicit representation of resources and priorities to arbitrate resource contention in a natural way that produces easy to understand and modify specifications. The semantics of GCSR is the algebra of communicating shared resources, a timed process algebra with operational semantics. The process algebra provides behavioral equivalence relations which can be used to verify the correctness of one GCSR specification with respect to the other.