Search results for: Meta Object Facility (MOF)

Authors: A. Rakhmadi, M. S. M. Rahim, A. Bade, H. Haron, I. M. Amin

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In this study, a Loop Back Algorithm for component connected labeling for detecting objects in a digital image is presented. The approach is using loop back connected component labeling algorithm that helps the system to distinguish the object detected according to their label. Deferent than whole window scanning technique, this technique reduces the searching time for locating the object by focusing on the suspected object based on certain features defined. In this study, the approach was also implemented for a face detection system. Face detection system is becoming interesting research since there are many devices or systems that require detecting the face for certain purposes. The input can be from still image or videos, therefore the sub process of this system has to be simple, efficient and accurate to give a good result.

Keywords: Image processing, connected components labeling, face detection.

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Authors: Raimund Leitner, Susanne Rosskopf

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Near-infrared (NIR) spectroscopy is a widely used method for material identification for laboratory and industrial applications. While standard spectrometers only allow measurements at one sampling point at a time, NIR Spectral Imaging techniques can measure, in real-time, both the size and shape of an object as well as identify the material the object is made of. The online classification and sorting of recovered paper with NIR Spectral Imaging (SI) is used with success in the paper recycling industry throughout Europe. Recently, the globalisation of the recycling material streams caused that water-based flexographic-printed newspapers mainly from UK and Italy appear also in central Europe. These flexo-printed newspapers are not sufficiently de-inkable with the standard de-inking process originally developed for offset-printed paper. This de-inking process removes the ink from recovered paper and is the fundamental processing step to produce high-quality paper from recovered paper. Thus, the flexo-printed newspapers are a growing problem for the recycling industry as they reduce the quality of the produced paper if their amount exceeds a certain limit within the recovered paper material. This paper presents the results of a research project for the development of an automated entry inspection system for recovered paper that was jointly conducted by CTR AG (Austria) and PTS Papiertechnische Stiftung (Germany). Within the project an NIR SI prototype for the identification of flexo-printed newspaper has been developed. The prototype can identify and sort out flexoprinted newspapers in real-time and achieves a detection accuracy for flexo-printed newspaper of over 95%. NIR SI, the technology the prototype is based on, allows the development of inspection systems for incoming goods in a paper production facility as well as industrial sorting systems for recovered paper in the recycling industry in the near future.

Keywords: spectral imaging, imaging spectroscopy, NIR, waterbasedflexographic, flexo-printed, recovered paper, real-time classification.

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Authors: Maumita Bhattacharya

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Optimization is often a critical issue for most system design problems. Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, finding optimal solution to complex high dimensional, multimodal problems often require highly computationally expensive function evaluations and hence are practically prohibitive. The Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model presented in our earlier work [14] reduced computation time by controlled use of meta-models to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model. Situations like model formation involving variable input dimensions and noisy data certainly can not be covered by this assumption. In this paper we present an enhanced version of DAFHEA that incorporates a multiple-model based learning approach for the SVM approximator. DAFHEA-II (the enhanced version of the DAFHEA framework) also overcomes the high computational expense involved with additional clustering requirements of the original DAFHEA framework. The proposed framework has been tested on several benchmark functions and the empirical results illustrate the advantages of the proposed technique.

Keywords: Evolutionary algorithm, Fitness function, Optimization, Meta-model, Stochastic method.

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Authors: Dimitrios Ntardas, Alexandros Tzanetos, Georgios Dounias

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High Voltage Substations (HVS) are the intermediate step between production of power and successfully transmitting it to clients, making them one of the most important checkpoints in power grids. Nowadays - renewable resources and consequently distributed generation are growing fast, the construction of HVS is of high importance both in terms of quality and time completion so that new energy producers can quickly and safely intergrade in power grids. The resources needed, such as machines and workers, should be carefully allocated so that the construction of a HVS is completed on time, with the lowest possible cost (e.g. not spending additional cost that were not taken into consideration, because of project delays), but in the highest quality. In addition, there are milestones and several checkpoints to be precisely achieved during construction to ensure the cost and timeline control and to ensure that the percentage of governmental funding will be granted. The management of such a demanding project is a NP-hard problem that consists of prerequisite constraints and resource limits for each task of the project. In this work, a hybrid meta-heuristic method is implemented to solve this problem. Meta-heuristics have been proven to be quite useful when dealing with high-dimensional constraint optimization problems. Hybridization of them results in boost of their performance.

Keywords: High voltage substations, nature-inspired algorithms, project management, meta-heuristics.

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Authors: Sepehr Aslani, Homayoun Mahdavi-Nasab

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Automated motion detection and tracking is a challenging task in traffic surveillance. In this paper, a system is developed to gather useful information from stationary cameras for detecting moving objects in digital videos. The moving detection and tracking system is developed based on optical flow estimation together with application and combination of various relevant computer vision and image processing techniques to enhance the process. To remove noises, median filter is used and the unwanted objects are removed by applying thresholding algorithms in morphological operations. Also the object type restrictions are set using blob analysis. The results show that the proposed system successfully detects and tracks moving objects in urban videos.

Keywords: Optical flow estimation, moving object detection, tracking, morphological operation, blob analysis.

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Authors: Sergio Pissanetzky

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The ability of the brain to organize information and generate the functional structures we use to act, think and communicate, is a common and easily observable natural phenomenon. In object-oriented analysis, these structures are represented by objects. Objects have been extensively studied and documented, but the process that creates them is not understood. In this work, a new class of discrete, deterministic, dissipative, host-guest dynamical systems is introduced. The new systems have extraordinary self-organizing properties. They can host information representing other physical systems and generate the same functional structures as the brain does. A simple mathematical model is proposed. The new systems are easy to simulate by computer, and measurements needed to confirm the assumptions are abundant and readily available. Experimental results presented here confirm the findings. Applications are many, but among the most immediate are object-oriented engineering, image and voice recognition, search engines, and Neuroscience.

Keywords: AI, artificial intelligence, complex system, object oriented, OO, refactoring.

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Authors: Konstantinos C. Kartsakalis, Angeliki Skoura, Vasileios Megalooikonomou

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The fuzzy composition of objects depicted in images acquired through MR imaging or the use of bio-scanners has often been a point of controversy for field experts attempting to effectively delineate between the visualized objects. Modern approaches in medical image segmentation tend to consider fuzziness as a characteristic and inherent feature of the depicted object, instead of an undesirable trait. In this paper, a novel technique for efficient image retrieval in the context of images in which segmented objects are either crisp or fuzzily bounded is presented. Moreover, the proposed method is applied in the case of multiple, even conflicting, segmentations from field experts. Experimental results demonstrate the efficiency of the suggested method in retrieving similar objects from the aforementioned categories while taking into account the fuzzy nature of the depicted data.

Keywords: Fuzzy Object, Fuzzy Image Segmentation, Motion Descriptors, MRI Imaging, Object-Based Image Retrieval.

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Authors: Jeffrey A. Dunne, Kevin Ligozio

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In many applications there is a broad variety of information relevant to a focal “object" of interest, and the fusion of such heterogeneous data types is desirable for classification and categorization. While these various data types can sometimes be treated as orthogonal (such as the hull number, superstructure color, and speed of an oil tanker), there are instances where the inference and the correlation between quantities can provide improved fusion capabilities (such as the height, weight, and gender of a person). A service-oriented architecture has been designed and prototyped to support the fusion of information for such “object-centric" situations. It is modular, scalable, and flexible, and designed to support new data sources, fusion algorithms, and computational resources without affecting existing services. The architecture is designed to simplify the incorporation of legacy systems, support exact and probabilistic entity disambiguation, recognize and utilize multiple types of uncertainties, and minimize network bandwidth requirements.

Keywords: Data fusion, distributed computing, service-oriented architecture, SOA

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Authors: Chun-Wei Lin, Yu-Lin Chen

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As global industry developed rapidly, the energy demand also rises simultaneously. In the production process, there’s a lot of energy consumed in the process. Formally, the energy used in generating the heat in the production process. In the total energy consumption, 40% of the heat was used in process heat, mechanical work, chemical energy and electricity. The remaining 50% were released into the environment. It will cause energy waste and environment pollution. There are many ways for recovering the waste heat in factory. Organic Rankine Cycle (ORC) system can produce electricity and reduce energy costs by recovering the waste of low temperature heat in the factory. In addition, ORC is the technology with the highest power generating efficiency in low-temperature heat recycling. However, most of factories executives are still hesitated because of the high implementation cost of the ORC system, even a lot of heat are wasted. Therefore, this study constructs a nonlinear mathematical model of waste heat recovery equipment configuration to maximize profits. A particle swarm optimization algorithm is developed to generate the optimal facility installation plan for the ORC system.

Keywords: Green facility planning, organic rankine cycle, particle swarm optimization, waste heat recovery.

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Authors: Norazlina Khamis, Sufian Idris, Rodina Ahmad

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The most influential programming paradigm today is object oriented (OO) programming and it is widely used in education and industry. Recognizing the importance of equipping students with OO knowledge and skills, it is not surprising that most Computer Science degree programs offer OO-related courses. How do we assess whether the students have acquired the right objectoriented skills after they have completed their OO courses? What are object oriented skills? Currently none of the current assessment techniques would be able to provide this answer. Traditional forms of OO programming assessment provide a ways for assigning numerical scores to determine letter grades. But this rarely reveals information about how students actually understand OO concept. It appears reasonable that a better understanding of how to define and assess OO skills is needed by developing a criterion referenced model. It is even critical in the context of Malaysia where there is currently a growing concern over the level of competency of Malaysian IT graduates in object oriented programming. This paper discussed the approach used to develop the criterion-referenced assessment model. The model can serve as a guideline when conducting OO programming assessment as mentioned. The proposed model is derived by using Goal Questions Metrics methodology, which helps formulate the metrics of interest. It concluded with a few suggestions for further study.

Keywords: Object-oriented programming, programmingassessment, criterion-referenced assessment model, goal questionsmetrics.

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Authors: Hossein Pazhoumand-Dar, Amir Mohsen Toliyat Abolhassani, Ehsan Saeedi

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Speed estimation is one of the important and practical tasks in machine vision, Robotic and Mechatronic. the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in machine vision algorithms. Numerous approaches for speed estimation have been proposed. So classification and survey of the proposed methods can be very useful. The goal of this paper is first to review and verify these methods. Then we will propose a novel algorithm to estimate the speed of moving object by using fuzzy concept. There is a direct relation between motion blur parameters and object speed. In our new approach we will use Radon transform to find direction of blurred image, and Fuzzy sets to estimate motion blur length. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on many images with different range of SNR and is satisfiable.

Keywords: Blur Analysis, Fuzzy sets, Speed estimation.

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Authors: Gouri Prakash

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One of the common problems encountered in software engineering is addressing and responding to the changing nature of requirements. While several approaches have been devised to address this issue, ranging from instilling resistance to changing requirements in order to mitigate impact to project schedules, to developing an agile mindset towards requirements, the approach discussed in this paper is one of conceptualizing the delta in requirement and modeling it, in order to plan a response to it. To provide some context here, change is first formally identified and categorized as either formal change or informal change. While agile methodology facilitates informal change, the approach discussed in this paper seeks to develop the idea of facilitating formal change. To collect, document meta-requirements that represent the phenomena of change would be a pro-active measure towards building a realistic cognition of the requirements entity that can further be harnessed in the software engineering process.

Keywords: Change Management, Agile methodology, Metarequirements

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Authors: Ponsy R.K. Sathia Bhama, Thamarai Selvi Soma Sundaram, R. Sivakama Sundari, R. Bakiyalakshmi, K. Thamizharasi

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Grid scheduling is the process of mapping grid jobs to resources over multiple administrative domains. Traditionally, application-level schedulers have been tightly integrated with the application itself and were not easily applied to other applications. This design is generic that decouples the scheduler core (the search procedure) from the application-specific (e.g. application performance models) and platform-specific (e.g. collection of resource information) components used by the search procedure. In this decoupled approach the application details are not revealed completely to broker, but customer will give the application to resource provider for execution. In a decoupled approach, apart from scheduling, the resource selection can be performed independently in order to achieve scalability.

Keywords: Meta, grid scheduling, application-level scheduler, decouple, scheduler core and performance model.

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Authors: Wahyudin P. Syam, Ibrahim M. Al-Harkan

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This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Scheduling Problem with Parallel Machines. This problem is known to be NP-Hard. This study proposes three algorithms among improvement heuristic searches which are: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). SA and TS are known as deterministic improvement heuristic search. GA is known as stochastic improvement heuristic search. A comprehensive comparison from these three improvement heuristic searches is presented. The results for the experiments conducted show that TS is effective and efficient to solve HFS scheduling problems.

Keywords: Flow shop, genetic algorithm, simulated annealing, tabu search.

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Authors: Fathi Taibi, Fouad Mohammed Abbou, Md. Jahangir Alam

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The complexity of today-s software systems makes collaborative development necessary to accomplish tasks. Frameworks are necessary to allow developers perform their tasks independently yet collaboratively. Similarity detection is one of the major issues to consider when developing such frameworks. It allows developers to mine existing repositories when developing their own views of a software artifact, and it is necessary for identifying the correspondences between the views to allow merging them and checking their consistency. Due to the importance of the requirements specification stage in software development, this paper proposes a framework for collaborative development of Object- Oriented formal specifications along with a similarity detection approach to support the creation, merging and consistency checking of specifications. The paper also explores the impact of using additional concepts on improving the matching results. Finally, the proposed approach is empirically evaluated.

Keywords: Collaborative Development, Formal methods, Object-Oriented, Similarity detection

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Authors: Yao-Hong Tsai

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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: Unmanned aerial vehicle, object tracking, deep learning, collision avoidance.

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Authors: Kamaldeep Kaur, Arvinder Kaur, Ruchika Malhotra

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The aim of this paper is to rank the impact of Object Oriented(OO) metrics in fault prediction modeling using Artificial Neural Networks(ANNs). Past studies on empirical validation of object oriented metrics as fault predictors using ANNs have focused on the predictive quality of neural networks versus standard statistical techniques. In this empirical study we turn our attention to the capability of ANNs in ranking the impact of these explanatory metrics on fault proneness. In ANNs data analysis approach, there is no clear method of ranking the impact of individual metrics. Five ANN based techniques are studied which rank object oriented metrics in predicting fault proneness of classes. These techniques are i) overall connection weights method ii) Garson-s method iii) The partial derivatives methods iv) The Input Perturb method v) the classical stepwise methods. We develop and evaluate different prediction models based on the ranking of the metrics by the individual techniques. The models based on overall connection weights and partial derivatives methods have been found to be most accurate.

Keywords: Artificial Neural Networks (ANNS), Backpropagation, Fault Prediction Modeling.

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Authors: Lidia N. Hernández-Piña, Carlos R. Jaimez-González

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Interoperability in distributed systems is an important feature that refers to the communication of two applications written in different programming languages. This paper presents a serializer and a de-serializer of PHP objects to and from XML, which is an independent library written in the PHP programming language. The XML generated by this serializer is independent of the programming language, and can be used by other existing Web Objects in XML (WOX) serializers and de-serializers, which allow interoperability with other object-oriented programming languages.

Keywords: Interoperability, PHP object serialization, PHP to XML, web objects in XML, WOX.

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Authors: Aissam Belghiat, Mustapha Bourahla

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Models are placed by modeling paradigm at the center of development process. These models are represented by languages, like UML the language standardized by the OMG which became necessary for development. Moreover the ontology engineering paradigm places ontologies at the center of development process; in this paradigm we find OWL the principal language for knowledge representation. Building ontologies from scratch is generally a difficult task. The bridging between UML and OWL appeared on several regards such as the classes and associations. In this paper, we have to profit from convergence between UML and OWL to propose an approach based on Meta-Modelling and Graph Grammars and registered in the MDA architecture for the automatic generation of OWL ontologies from UML class diagrams. The transformation is based on transformation rules; the level of abstraction in these rules is close to the application in order to have usable ontologies. We illustrate this approach by an example.

Keywords: ATOM3, MDA, Ontology, OWL, UML

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Authors: Gia Surguladze, Nino Topuria, Ana Gavardashvili, Tsatsa Namchevadze

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The present article discusses design and development of Information System for monitoring ecology within the Black Sea basin of Georgia. Sea parameters, river, estuary, vulnerable district, water sample, etc. were considered as the major parameters of the sea ecosystem. A conceptual schema has been developed for the Black Sea ecosystem based on object-role model. The experimental database for the Black Sea ecosystem has been constructed using Ms SQL Server, while the object-role model NORMA has been developed using graphical instrument Ms Visual Studio within the integrated environment of .NET Framework 4.5. Web portal has been designed based on Ms SharePoint Server. The server database connection with web-portal has been carried out by means of External List of Ms SharePoint Server Designer.

Keywords: Web-application, service-oriented architecture, database, object-role modelling, SharePoint, Black sea, river, estuary, ecology, monitoring system, automation of data processing.

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Authors: Rachel Hurdley, Jane Hopkinson

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This article is based on observation of a cross-disciplinary, cross-institutional team that worked on an intervention called ‘Memory Mate’ for use in a UK Cancer Centre. This aimed to improve treatment outcomes for patients who had comorbid dementia or other memory impairment. Comorbid patients present ambiguous, spoiled identities, problematising the boundaries of health specialisms and frames of understanding. Memory Mate is theorised as a boundary object facilitating service transformation by changing relations between oncology and mental health care practice. It crosses the boundaries between oncology and mental health. Its introduction signifies an important step in reconfiguring relations between the specialisms. As a boundary object, it contains parallel, even contesting worlds, with potential to enable an eventual synthesis of the double stigma of cancer and dementia. Memory Mate comprises physical things, such as an animation, but its principal value is in the interaction it initiates across disciplines and services. It supports evolution of practices to address a newly emergent challenge for health service provision, namely the cancer patient with comorbid dementia/cognitive impairment. Getting clinicians from different disciplines working together on a practical solution generates a dialogue that can shift professional identity and change the culture of practice.

Keywords: Boundary object, cancer, dementia, interdisciplinary teams.

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Authors: Xinyue Zhao, Yutaka Satoh, Hidenori Takauji, Shun'ichi Kaneko

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A hybrid feature based adaptive particle filter algorithm is presented for object tracking in real scenarios with static camera. The hybrid feature is combined by two effective features: the Grayscale Arranging Pairs (GAP) feature and the color histogram feature. The GAP feature has high discriminative ability even under conditions of severe illumination variation and dynamic background elements, while the color histogram feature has high reliability to identify the detected objects. The combination of two features covers the shortage of single feature. Furthermore, we adopt an updating target model so that some external problems such as visual angles can be overcame well. An automatic initialization algorithm is introduced which provides precise initial positions of objects. The experimental results show the good performance of the proposed method.

Keywords: Hybrid feature, adaptive Particle Filter, robust Object Tracking, Grayscale Arranging Pairs (GAP) feature.

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Authors: Yunyoung Nam, Junghun Ryu, Yoo-Joo Choi, We-Duke Cho

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This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities.

Keywords: Surveillance, multiple camera, people tracking, topology.

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Authors: Hazem M. El-Bakry, Qiangfu Zhao

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Recently, fast neural networks for object/face detection were presented in [1-3]. The speed up factor of these networks relies on performing cross correlation in the frequency domain between the input image and the weights of the hidden layer. But, these equations given in [1-3] for conventional and fast neural networks are not valid for many reasons presented here. In this paper, correct equations for cross correlation in the spatial and frequency domains are presented. Furthermore, correct formulas for the number of computation steps required by conventional and fast neural networks given in [1-3] are introduced. A new formula for the speed up ratio is established. Also, corrections for the equations of fast multi scale object/face detection are given. Moreover, commutative cross correlation is achieved. Simulation results show that sub-image detection based on cross correlation in the frequency domain is faster than classical neural networks.

Keywords: Conventional Neural Networks, Fast Neural Networks, Cross Correlation in the Frequency Domain.

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Authors: Artur Matuck, Guilherme F. Nobre

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Machines can be either tool, media, or social agents. Advances in technology have been delivering machines capable of autonomous expression, both through communication and art. This paper deals with models (theoretical approach) and experiments (applied approach) related to artificial agents. On one hand it traces how social sciences' scholars have worked with topics such as text automatization, man-machine writing cooperation, and communication. On the other hand it covers how computer sciences' scholars have built communicative and artistic machines, including the programming of creativity. The aim is to present a brief survey on artificially intelligent communicators and artificially creative writers, and provide the basis to understand the meta-authorship and also to new and further man-machine co-authorship.

Keywords: Artificial communication, artificial creativity, artificial writers, meta-authorship, robotic art.

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Authors: Fathi Taibi

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Reusability is a quality desired attribute in software products. Generally, it could be achieved through adopting development methods that promote it and achieving software qualities that have been linked with high reusability proneness. With the exponential growth in mobile application development, software reuse became an integral part in a substantial number of projects. Similarly, software reuse has become widely practiced in start-up companies. However, this has led to new emerging problems. Firstly, the reused code does not meet the required quality and secondly, the reuse intentions are dubious. This work aims to propose a framework to support reuse in Object-Oriented (OO) software development. The framework comprises a process that uses a proposed reusability assessment metric and a formal foundation to specify the elements of the reused code and the relationships between them. The framework is empirically evaluated using a wide range of open-source projects and mobile applications. The results are analyzed to help understand the reusability proneness of OO software and the possible means to improve it.

Keywords: Software reusability, software metrics, object-oriented software, modularity, low complexity, understandability.

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Authors: Samee Ullah Khan, Ishfaq Ahmad

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This paper proposes a novel game theoretical technique to address the problem of data object replication in largescale distributed computing systems. The proposed technique draws inspiration from computational economic theory and employs the extended Vickrey auction. Specifically, players in a non-cooperative environment compete for server-side scarce memory space to replicate data objects so as to minimize the total network object transfer cost, while maintaining object concurrency. Optimization of such a cost in turn leads to load balancing, fault-tolerance and reduced user access time. The method is experimentally evaluated against four well-known techniques from the literature: branch and bound, greedy, bin-packing and genetic algorithms. The experimental results reveal that the proposed approach outperforms the four techniques in both the execution time and solution quality.

Keywords: Auctions, data replication, pricing, static allocation.

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Authors: Parvinder S. Sandhu, Satish Kumar Dhiman, Anmol Goyal

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Fault-proneness of a software module is the probability that the module contains faults. A correlation exists between the fault-proneness of the software and the measurable attributes of the code (i.e. the static metrics) and of the testing (i.e. the dynamic metrics). Early detection of fault-prone software components enables verification experts to concentrate their time and resources on the problem areas of the software system under development. This paper introduces Genetic Algorithm based software fault prediction models with Object-Oriented metrics. The contribution of this paper is that it has used Metric values of JEdit open source software for generation of the rules for the classification of software modules in the categories of Faulty and non faulty modules and thereafter empirically validation is performed. The results shows that Genetic algorithm approach can be used for finding the fault proneness in object oriented software components.

Keywords: Genetic Algorithms, Software Fault, Classification, Object Oriented Metrics.

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Authors: A. Goel, S.C. Gupta, S.K.Wasan

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Testable software has two inherent properties – observability and controllability. Observability facilitates observation of internal behavior of software to required degree of detail. Controllability allows creation of difficult-to-achieve states prior to execution of various tests. In this paper, we describe COTT, a Controllability and Observability Testing Tool, to create testable object-oriented software. COTT provides a framework that helps the user to instrument object-oriented software to build the required controllability and observability. During testing, the tool facilitates creation of difficult-to-achieve states required for testing of difficultto- test conditions and observation of internal details of execution at unit, integration and system levels. The execution observations are logged in a test log file, which are used for post analysis and to generate test coverage reports.

Keywords: Controllability, Observability, Test Coverage and Testing Tool.

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Authors: Lorenzo Maiorino, Fabio Fortuna, Giovanni Panebianco, Marco Sanzari, Gabriella Arcese, Valerio Maria Paolozzi

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It is well known that sport is a factor of social cohesion and the breaking down of barriers between people. From this point of view, the aim is to demonstrate how, through the (re)generation of sustainable structures, it is possible to give life to a new social, cultural and economic pathway, where possible, in peripheral areas with problems of abandonment and degradation. The aim of this paper is therefore to study realities such as European programs and funds and to highlight the ways in which planning can be used to respond to critical issues such as urban decay, abandonment, and the mitigation of social differences. For this reason, the analysis will be carried out through the Multiannual Financial Framework (MFF) package, the next generation EU, the Recovery and Resilience Facility (RRF), the Cohesion Fund, the European Social Fund, and other managed funds. The procedure will rely on sources and data of unquestionable origin, and the relation to the object of study in question will be highlighted. The project lends itself to be ambitious and explore a further aspect of the sports theme, which as we know, is one of the foundations for a healthy society

.

Keywords: Sport, social inclusion, urban regeneration, sport facilities, European funds.

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