Certifying the synthesis of heap-manipulating programs

Automated deductive program synthesis promises to generate executable programs from concise specifications, along with proofs of correctness that can be independently verified using third-party tools. However, an attempt to exercise this promise using existing proof-certification frameworks reveals significant discrepancies in how proof derivations are structured for two different purposes: program synthesis and program verification. These discrepancies make it difficult to use certified verifiers to validate synthesis results, forcing one to write an ad-hoc translation procedure from synthesis proofs to correctness proofs for each verification backend. In this work, we address this challenge in the context of the synthesis and verification of heap-manipulating programs. We present a technique for principled translation of deductive synthesis derivations (a.k.a. source proofs) into deductive target proofs about the synthesised programs in the logics of interactive program verifiers. We showcase our technique by implementing three different certifiers for programs generated via SuSLik, a Separation Logic-based tool for automated synthesis of programs with pointers, in foundational verification frameworks embedded in Coq: Hoare Type Theory (HTT), Iris, and Verified Software Toolchain (VST), producing concise and efficient machine-checkable proofs for characteristic synthesis benchmarks.

Bin2vec: learning representations of binary executable programs for security tasks

Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks. We introduce Bin2vec, a new approach leveraging Graph Convolutional Networks (GCN) along with computational program graphs in order to learn a high dimensional representation of binary executable programs. We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks – functional algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results, and demonstrate improvement over state-of-the-art methods for both tasks. We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task. We set a new state-of-the-art result by reducing the classification error by 40% compared to the source-code based inst2vec approach, while working on binary code. For almost every vulnerability class in our dataset, our prediction accuracy is over 80% (and over 90% in multiple classes).

Elaboration of an Expert System for Sizing, Designing and Verifying Flexible Pavements

The use of expert systems in the world of civil engineering, and in particular for roads, has become a necessity for the reason of the particularity, complexity, and diversity of the influencing parameters at the level of the design calculation, the latter of which represents the major source of subsequent degradation. This system consists of proposing a tool for helping the user firstly to size the body of the roadway, with several analytical methods and models (Pre-project, Boussinesq, Westgaard, and Burmister), and secondly, to offer different design possibilities (thickness and type of the material) that make up the layers. Lastly, it is to calculate the stresses and strains in order to compare them with admissible limits. The management of a knowledge base of complex natures (words, sentences, numbers, symbols, tables, calculation methods, equations, conditions, etc.) requires an adequate methodology which goes beyond the simple use of the technology but enables you to imagine the process of regrouping this mass of complex data and classifying the data, which can then be integrated into a database or spreadsheets and external programs designed with code compatible with the expert system generator. Our contribution relates initially to the formulation, organization, and preparation of the algorithms, and then the starting of the programs in order to conceive fully executable programs, the latter of which we can call the expert system. The validation of such a system was made as the work progressed, changes were made in the formulation of the rules, and the order and orientation of the data in the knowledge that the advantage of this type of system is the possibility of permanently reinforcing the database with human expertise in the field, or in books, especially so that we can avoid data loss due to illnesses, retirement, etc.

LayNii: A software suite for layer-fMRI. Revision 1

High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of con-ventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed open-source and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain ‘layerification’ and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based deveining of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data.Graphical abstract

PERANGKAT LUNAK KOMPUTER

AbstrakSoftware komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program atau prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan intruksi (data) yang diberikan. (Yahfizham, 2019 : 19) Sebagian besar dari software komputer dibuat oleh (programmer) dengan menggunakan bahasa pemprograman. Orang yang membuat bahasa pemprograman menuliskan perintah dalam bahasa pemprograman seperti layaknya bahasa yang digunakan oleh orang pada umumnya dalam melakukan perbincangan. Perintah-perintah tersebut dinamakan (source code). Program komputer lainnya dinamakan (compiler) yang digunakan pada (source code) dan kemudian mengubah perintah tersebut kedalam bahasa yang dimengerti oleh komputer lalu hasilnya dinamakan program executable (EXE). Pada dasarnya, komputer selalu memiliki perangkat lunak komputer atau software yang terdiri dari sistem operasi, sistem aplikasi dan bahasa pemograman.AbstractComputer software or computer software is a collection of instructions (programs or procedures) to be able to carry out work automatically by processing or processing the collection of instructions (data) provided. (Yahfizham, 2019: 19) Most of the computer software is made by (programmers) using the programming language. People who make programming languages write commands in the programming language like the language used by people in general in conducting conversation. The commands are called (source code). Other computer programs called (compilers) are used in (source code) and then change the command into a language understood by the computer and the results are called executable programs (EXE). Basically, computers always have computer software or software consisting of operating systems, application systems and programming languages.

Jurnal PERANGKAT LUNAK KOMPUTER Ayubi Simatupang PMM-1

Abstrak- Perangkat lunak komputer atau yang sering disebut sebagai (software) mempunyai sifat yang berbeda dengan (hardware) atau perangkat keras komputer. Jika perangkat keras komputer dapat dilihat dan disentuh keberadaannya maka perangkat lunak pada suatu komputer hanya dapat dilihat saja tanpa dapat kita rasa atau raba bendanya. Lebih tepatnya, perangkat lunak tidak dapat disentuh dan memang secara fisik tidak tampak namun kita dapat mengoperasikannya. Namun walaupun tidak tampak secara fisik perangkat lunak sangat berguna dalam pengoperasiannya dengan adanya perangkat lunak suatu komputer dapat menjalankan suatu perintah. Sebagian besar dari software komputer dibuat oleh (programmer) dengan menggunakan bahasa pemprograman. Orang yang membuat bahasa pemprograman menuliskan perintah dalam bahasa pemprograman seperti layaknya bahasa yang digunakan oleh orang pada umumnya dalam melakukan perbincangan. Perintah-perintah tersebut dinamakan (source code). Program komputer lainnya dinamakan (compiler) yang digunakan pada (source code) dan kemudian mengubah perintah tersebut kedalam bahasa yang dimengerti oleh komputer lalu hasilnya dinamakan program executable (EXE).Kata Kunci: Software, ProgrammerAbstac t- Computer software or often referred to as (software) has different properties from (hardware) or computer hardware. If the computer hardware can be seen and touched, then the software on a computer can only be seen without our feeling or feeling. More precisely, the software cannot be touched and it is physically invisible but we can operate it. But even though the software does not appear physically very useful in its operation with the software a computer can run a command. Most of the computer software is made by (programmers) using the programming language. People who make programming languages write commands in the programming language like the language used by people in general in conducting conversation. The commands are called (source code). Other computer programs called (compilers) are used in (source code) and then change the command into a language understood by the computer and the results are called executable programs (EXE).Keywords: Software, Programmer

PERANGKAT LUNAK KOMPUTER

AbstrakSoftware komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program atau prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan intruksi (data) yang diberikan. (Yahfizham, 2019 : 19) Sebagian besar dari software komputer dibuat oleh (programmer) dengan menggunakan bahasa pemprograman. Orang yang membuat bahasa pemprograman menuliskan perintah dalam bahasa pemprograman seperti layaknya bahasa yang digunakan oleh orang pada umumnya dalam melakukan perbincangan. Perintah-perintah tersebut dinamakan (source code). Program komputer lainnya dinamakan (compiler) yang digunakan pada (source code) dan kemudian mengubah perintah tersebut kedalam bahasa yang dimengerti oleh komputer lalu hasilnya dinamakan program executable (EXE). Pada dasarnya, komputer selalu memiliki perangkat lunak komputer atau software yang terdiri dari sistem operasi, sistem aplikasi dan bahasa pemograman.AbstractComputer software or computer software is a collection of instructions (programs or procedures) to be able to carry out work automatically by processing or processing the collection of instructions (data) provided. (Yahfizham, 2019: 19) Most of the computer software is made by (programmers) using the programming language. People who make programming languages write commands in the programming language like the language used by people in general in conducting conversation. The commands are called (source code). Other computer programs called (compilers) are used in (source code) and then change the command into a language understood by the computer and the results are called executable programs (EXE). Basically, computers always have computer software or software consisting of operating systems, ap

Neural Program Induction for KBQA Without Gold Programs or Query Annotations

Neural Program Induction (NPI) is a paradigm for decomposing high-level tasks such as complex question-answering over knowledge bases (KBQA) into executable programs by employing neural models. Typically, this involves two key phases: i) inferring input program variables from the high-level task description, and ii) generating the correct program sequence involving these variables. Here we focus on NPI for Complex KBQA with only the final answer as supervision, and not gold programs. This raises major challenges; namely, i) noisy query annotation in the absence of any supervision can lead to catastrophic forgetting while learning, ii) reward becomes extremely sparse owing to the noise. To deal with these, we propose a noise-resilient NPI model, Stable Sparse Reward based Programmer (SSRP) that evades noise-induced instability through continual retrospection and its comparison with current learning behavior. On complex KBQA datasets, SSRP performs at par with hand-crafted rule-based models when provided with gold program input, and in the noisy settings outperforms state-of-the-art models by a significant margin even with a noisier query annotator.

Distribution of computations in hybrid computing systems when translating NORMA language programs

Рассмотрены методы распределения вычислительной нагрузки при трансляции программ с непроцедурного (декларативного) языка НОРМА в исполняемые программы для различных параллельных архитектур. Приведены краткие характеристики языка НОРМА и основные возможности компилятора программ на языке НОРМА. Описаны способы автоматического распределения вычислительной нагрузки при генерации исполняемых программ следующих типов: OpenMP, NVIDIA CUDA, MPIOpenMP и MPIOpenMPNVIDIA CUDA. Рассмотрена задача динамической балансировки вычислительной нагрузки, возникающая в случае неоднородной вычислительной среды MPIOpenMPNVIDIA CUDA, и предложен метод ее решения. Приведены результаты практического применения компилятора программ на языке НОРМА для решения двух различных задач и оценена скорость выполнения получаемых при этом исполняемых программ для различных параллельных архитектур. The methods of computational load distribution when translating programs from the nonprocedural (declarative) NORMA language into executable programs for various parallel architectures are discussed. Some brief characteristics of the NORMA language and the main features of the compiler for programs in NORMA language are given. The methods of automatic distribution of computational load when generating executable programs of the following types are described: OpenMP, NVIDIA CUDA, MPIOpenMP, and MPIOpenMPNVIDIA CUDA. The problem of dynamic computational load balancing arising in the case of the heterogeneous computing environment MPIOpenMPNVIDIA CUDA is considered and a method of solving it is proposed. The results of practical application of the compiler for the programs in NORMA language for solving two different mathematical problems are given and the performance of the resulting executable programs is estimated for various parallel architectures.