Can Component/Service-Based Systems Be Proved Correct?

Component-oriented and service-oriented approaches have gained a strong enthusiasm in industries and academia with a particular interest for service-oriented approaches. A component is a software entity with given functionalities, made available by a provider, and used to build other application within which it is integrated. The service concept and its use in web-based application development have a huge impact on reuse practices. Accordingly a considerable part of software architectures is influenced; these architectures are moving towards service-oriented architectures. Therefore applications (re)use services that are available elsewhere and many applications interact, without knowing each other, using services available via service servers and their published interfaces and functionalities. Industries propose, through various consortium, languages, technologies and standards. More academic works are also undertaken concerning semantics and formalisation of components and service-based systems. We consider here both streams of works in order to raise research concerns that will help in building quality software. Are there new challenging problems with respect to service-based software construction? Besides, what are the links and the advances compared to distributed systems?Comment: 16 page

On the Efficacy and High-Performance Implementation of Quaternion Matrix Multiplication

Quaternion symmetry is ubiquitous in the physical sciences. As such, much work has been afforded over the years to the development of efficient schemes to exploit this symmetry using real and complex linear algebra. Recent years have also seen many advances in the formal theoretical development of explicitly quaternion linear algebra with promising applications in image processing and machine learning. Despite these advances, there do not currently exist optimized software implementations of quaternion linear algebra. The leverage of optimized linear algebra software is crucial in the achievement of high levels of performance on modern computing architectures, and thus provides a central tool in the development of high-performance scientific software. In this work, a case will be made for the efficacy of high-performance quaternion linear algebra software for appropriate problems. In this pursuit, an optimized software implementation of quaternion matrix multiplication will be presented and will be shown to outperform a vendor tuned implementation for the analogous complex matrix operation. The results of this work pave the path for further development of high-performance quaternion linear algebra software which will improve the performance of the next generation of applicable scientific applications

Recent Applications of Deep Learning Algorithms in Medical Image Analysis

Advances in deep learning have enabled researchers in the field of medical imaging to employ such techniques for various applications, including early diagnosis of different diseases. Deep learning techniques such as convolutional neural networks offer the capability of extracting invariant features from images that can improve the performance of most predictive models in medical and diagnostic imaging. This work concentrates on reviewing deep learning architectures along with medical imaging modalities where the crucial applications of such algorithms, including image classification and segmentation, are discussed. Also, brain imaging as a branch of medical imaging which allows scientists to explore the structure and function of the brain is explored, and the applications of deep learning to early diagnose Alzheimer’s Disease, and Autism as the most critical brain disorders are studied. Moreover, the recent research findings revealed that employing deep learning-based semantic segmentation techniques could significantly improve the accuracy of models developed for brain tumor detection. Such advances in early diagnosis of disorders and tumors encourage medical imaging practitioners to implement software applications assisting them to improve their decision-making process

Cybersecurity issues in software architectures for innovative services

The recent advances in data center development have been at the basis of the widespread success of the cloud computing paradigm, which is at the basis of models for software based applications and services, which is the "Everything as a Service" (XaaS) model. According to the XaaS model, service of any kind are deployed on demand as cloud based applications, with a great degree of flexibility and a limited need for investments in dedicated hardware and or software components. This approach opens up a lot of opportunities, for instance providing access to complex and widely distributed applications, whose cost and complexity represented in the past a significant entry barrier, also to small or emerging businesses. Unfortunately, networking is now embedded in every service and application, raising several cybersecurity issues related to corruption and leakage of data, unauthorized access, etc. However, new service-oriented architectures are emerging in this context, the so-called services enabler architecture. The aim of these architectures is not only to expose and give the resources to these types of services, but it is also to validate them. The validation includes numerous aspects, from the legal to the infrastructural ones e.g., but above all the cybersecurity threats. A solid threat analysis of the aforementioned architecture is therefore necessary, and this is the main goal of this thesis. This work investigate the security threats of the emerging service enabler architectures, providing proof of concepts for these issues and the solutions too, based on several use-cases implemented in real world scenarios

Efficient design and implementation of image processing algorithms on reconfigurable hardware using Handel-C

Computer manipulation of images is generally defined as Digital Image Processing (DIP). DIP is used in variety of applications, including video surveillance, target recognition, and image enhancement. These applications are usually implemented in software but may use special purpose hardware for speed. With advances in the VLSI technology hardware implementation has become an attractive alternative. Assigning complex computation tasks to hardware and exploiting the parallelism and pipelining in algorithms yield significant speedup in running times. In this thesis the image processing algorithms like median filter, basic morphological operators, convolution and edge detection algorithms are implemented on FPGA. A pipelined architecture of these algorithms is presented. The proposed architectures are capable of producing one output on every clock cycle. The hardware modeling was accomplished using Handel-C (DK2 environment). The algorithm was tested on standard image processing benchmarks and the results are compared with that obtained on software

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Model Based Development of Quality-Aware Software Services

Modelling languages and development frameworks give support for functional and structural description of software architectures. But quality-aware applications require languages which allow expressing QoS as a first-class concept during architecture design and service composition, and to extend existing tools and infrastructures adding support for modelling, evaluating, managing and monitoring QoS aspects. In addition to its functional behaviour and internal structure, the developer of each service must consider the fulfilment of its quality requirements. If the service is flexible, the output quality depends both on input quality and available resources (e.g., amounts of CPU execution time and memory). From the software engineering point of view, modelling of quality-aware requirements and architectures require modelling support for the description of quality concepts, support for the analysis of quality properties (e.g. model checking and consistencies of quality constraints, assembly of quality), tool support for the transition from quality requirements to quality-aware architectures, and from quality-aware architecture to service run-time infrastructures. Quality management in run-time service infrastructures must give support for handling quality concepts dynamically. QoS-aware modeling frameworks and QoS-aware runtime management infrastructures require a common evolution to get their integration