Conceptual roles of data in program: analyses and applications

Program comprehension is the prerequisite for many software evolution and maintenance tasks. Currently, the research falls short in addressing how to build tools that can use domain-specific knowledge to provide powerful capabilities for extracting valuable information for facilitating program comprehension. Such capabilities are critical for working with large and complex program where program comprehension often is not possible without the help of domain-specific knowledge.;Our research advances the state-of-art in program analysis techniques based on domain-specific knowledge. The program artifacts including variables and methods are carriers of domain concepts that provide the key to understand programs. Our program analysis is directed by domain knowledge stored as domain-specific rules. Our analysis is iterative and interactive. It is based on flexible inference rules and inter-exchangeable and extensible information storage. We designed and developed a comprehensive software environment SeeCORE based on our knowledge-centric analysis methodology. The SeeCORE tool provides multiple views and abstractions to assist in understanding complex programs. The case studies demonstrate the effectiveness of our method. We demonstrate the flexibility of our approach by analyzing two legacy programs in distinct domains

A general algebra of business rules for heterogeneous systems

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis

Code transplantation for adversarial malware

In the nefarious fight against attackers, a wide range of smart algorithms have been introduced, in order to block and even prevent new families of malware before their appearance. Machine learning, for instance, recently gained a lot of attention thanks to its ability to use generalization to possibly detect never-before-seen attacks or variants of a known one. During the past years, a lot of works have tested the strength of machine learning in the cybersecurity field, exploring its potentialities and weaknesses. In particular, various studies highlighted its robustness against adversarial attacks, proposing strategies to mitigate them . Unfortunately, all these findings have focused in testing their own discoveries just operating on the dataset at feature layer space, which is the virtual data representation space, without testing the current feasibility of the attack at the problem space level, modifying the current adversarial sample . For this reason, in this dissertation, we will introduce PRISM, a framework for executing an adversarial attack operating at the problem space level. Even if this framework focuses only on Android applications, the whole methodology can be generalized on other platforms, like Windows, Mac or Linux executable files. The main idea is to successfully evade a classifier by transplanting chunks of code, taken from a set of goodware to a given malware. Exactly as in medicine, we have a donor who donates organs and receivers who receive them, in this case, goodware applications are our donors, the organs are the needed code and the receiver is the targeted malware. In the following work we will discuss about concepts related to a wide variety of topics, ranging from machine learning, due to the target classifier, to static analysis, due to the possible countermeasures considered, to program analysis, due to the extraction techniques adopter, ending in mobile application, because the target operating system is Android

FOAL 2004 Proceedings: Foundations of Aspect-Oriented Languages Workshop at AOSD 2004

Aspect-oriented programming is a paradigm in software engineering and FOAL logos courtesy of Luca Cardelli programming languages that promises better support for separation of concerns. The third Foundations of Aspect-Oriented Languages (FOAL) workshop was held at the Third International Conference on Aspect-Oriented Software Development in Lancaster, UK, on March 23, 2004. This workshop was designed to be a forum for research in formal foundations of aspect-oriented programming languages. The call for papers announced the areas of interest for FOAL as including, but not limited to: semantics of aspect-oriented languages, specification and verification for such languages, type systems, static analysis, theory of testing, theory of aspect composition, and theory of aspect translation (compilation) and rewriting. The call for papers welcomed all theoretical and foundational studies of foundations of aspect-oriented languages. The goals of this FOAL workshop were to: � Make progress on the foundations of aspect-oriented programming languages. � Exchange ideas about semantics and formal methods for aspect-oriented programming languages. � Foster interest within the programming language theory and types communities in aspect-oriented programming languages. � Foster interest within the formal methods community in aspect-oriented programming and the problems of reasoning about aspect-oriented programs. The papers at the workshop, which are included in the proceedings, were selected frompapers submitted by researchers worldwide. Due to time limitations at the workshop, not all of the submitted papers were selected for presentation. FOAL also welcomed an invited talk by James Riely (DePaul University), the abstract of which is included below. The workshop was organized by Gary T. Leavens (Iowa State University), Ralf L?ammel (CWI and Vrije Universiteit, Amsterdam), and Curtis Clifton (Iowa State University). The program committee was chaired by L?ammel and included L?ammel, Leavens, Clifton, Lodewijk Bergmans (University of Twente), John Tang Boyland (University of Wisconsin, Milwaukee), William R. Cook (University of Texas at Austin), Tzilla Elrad (Illinois Institute of Technology), Kathleen Fisher (AT&T Labs�Research), Radha Jagadeesan (DePaul University), Shmuel Katz (Technion�Israel Institute of Technology), Shriram Krishnamurthi (Brown University), Mira Mezini (Darmstadt University of Technology), Todd Millstein (University of California, Los Angeles), Benjamin C. Pierce (University of Pennsylvania), Henny Sipma (Stanford University), Mario S?udholt ( ?Ecole des Mines de Nantes), and David Walker (Princeton University). We thank the organizers of AOSD 2004 for hosting the workshop

Volume ray casting techniques and applications using general purpose computations on graphics processing units

Traditional 3D computer graphics focus on rendering the exterior of objects. Volume rendering is a technique used to visualize information corresponding to the interior of an object, commonly used in medical imaging and other fields. Visualization of such data may be accomplished by ray casting; an embarrassingly parallel algorithm also commonly used in ray tracing. There has been growing interest in performing general purpose computations on graphics processing units (GPGPU), which are capable exploiting parallel applications and yielding far greater performance than sequential implementations on CPUs. Modern GPUs allow for rapid acceleration of volume rendering applications, offering affordable high performance visualization systems. This thesis explores volume ray casting performance and visual quality enhancements using the NVIDIA CUDA platform, and demonstrates how high quality volume renderings can be produced with interactive and real time frame rates on modern commodity graphics hardware. A number of techniques are employed in this effort, including early ray termination, super sampling and texture filtering. In a performance comparison of a sequential versus CUDA implementation on high-end hardware, the latter is capable of rendering 60 frames per second with an impressive price-performance ratio heavily favoring GPUs. A number of unique volume rendering applications are explored including multiple volume rendering capable of arbitrary placement and rigid volume registration, hypertexturing and stereoscopic anaglyphs, each greatly enhanced by the real time interaction of volume data. The techniques and applications discussed in this thesis may prove to be invaluable tools in fields such as medical and molecular imaging, flow and scientific visualization, engineering drawing and many others