Precompilation: an alternative approach to provide native generic programming support in C++

In C++, Generative Programming (GP) techniques are being used to generate highly customized and optimized products automatically manufactured at compile-time; to provide these functionalities increasing compiling power is required. This work presents an improved compilation model for C++ by adding the ‘precompilation’ phase, leading beyond the Template Meta Programming technique to produce constants and conditional code. Procedural, object-oriented and all the remaining language features become available to produce constants, instances, and compiletime checks, opening, at the same time, a new way for metadata types treatment. In addition to that, when compiling for embedded platforms, some calculi may be moved from resource-critical run time to compile time, taking advantage of the processing power of the host platform. A tool named PRECOMP C++ is also presented in this work as a precompilationenabled C++ extension that supports GP in standard C++ execution during compile time, providing the ability to run metaprograms that operate with more complex data types and features than those supported in Template Meta Programming, such as floating point, pointers arithmetic, inclusion polymorphism, and dynamic memoryII Workshop de Ingeniería de Software y Bases de Datos (WISBD)Red de Universidades con Carreras en Informática (RedUNCI

Assessment Resistance Potential to Moisture Damage and Rutting for HMA Mixtures Reinforced by Steel Fibers

Rutting is mainly referring to pavement permanent deformation, it is a major problem for flexible pavement and it is a complicated process and highly observed along with many segments of asphalt pavement in Iraq. The occurrence of this defect is related to several variables such as elevated temperatures and high wheel loads. Studying effective methods to reduce rutting distress is of great significance for providing a safe and along-life road. The asphalt mixture used to be modified by adding different types of additives. The addition of additives typically excesses stiffness, improves temperature susceptibility, and reduces moisture sensitivity. For this work, steel fibres have been used for modifying asphalt mixture as they incorporated in the specimens by three percentages designated as 0.5, 1.0 and 1.5 % by the weight of asphalt mixture. The evaluation process based on conducting Marshall Test, Compressive strength test, and the wheel tracking test. The optimum asphalt content was determined for asphalt mixture. The results of the Marshall quotient and the index of retained strength of modified mixtures were increased by 44.0 and 17.38% respectively with adding 1.0% of steel fibres compared with the conventional mixture. The rut depth and dynamic stability were determined by using a wheel tracking test at two various testing temperatures of 45 and 55°C and two applied stresses of 70 and 80 psi. Results show that adding 1% of steel fibres to asphalt mixtures is very effective in increase the rutting resistance and reduce moisture damage

Precompilation: an alternative approach to provide native generic programming support in C++

In C++, Generative Programming (GP) techniques are being used to generate highly customized and optimized products automatically manufactured at compile-time; to provide these functionalities increasing compiling power is required. This work presents an improved compilation model for C++ by adding the ‘precompilation’ phase, leading beyond the Template Meta Programming technique to produce constants and conditional code. Procedural, object-oriented and all the remaining language features become available to produce constants, instances, and compiletime checks, opening, at the same time, a new way for metadata types treatment. In addition to that, when compiling for embedded platforms, some calculi may be moved from resource-critical run time to compile time, taking advantage of the processing power of the host platform. A tool named PRECOMP C++ is also presented in this work as a precompilationenabled C++ extension that supports GP in standard C++ execution during compile time, providing the ability to run metaprograms that operate with more complex data types and features than those supported in Template Meta Programming, such as floating point, pointers arithmetic, inclusion polymorphism, and dynamic memoryII Workshop de Ingeniería de Software y Bases de Datos (WISBD)Red de Universidades con Carreras en Informática (RedUNCI

Adversarial Attack and Defense on Graph Data: A Survey

Deep neural networks (DNNs) have been widely applied to various applications including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works studying adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph structure data due to its representation challenges. Given the importance of graph analysis, an increasing number of works start to analyze the robustness of machine learning models on graph data. Nevertheless, current studies considering adversarial behaviors on graph data usually focus on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation which makes the comparison among different methods difficult. Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph. Moreover, we also compare different attacks and defenses on graph data and discuss their corresponding contributions and limitations. In this work, we systemically organize the considered works based on the features of each topic. This survey not only serves as a reference for the research community, but also brings a clear image researchers outside this research domain. Besides, we also create an online resource and keep updating the relevant papers during the last two years. More details of the comparisons of various studies based on this survey are open-sourced at https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date information, please check our Github repository: https://github.com/YingtongDou/graph-adversarial-learning-literatur