GrammaTech (United States)

La collaboration, constitutive de l'ducation inclusive, suppose l'interrelation entre les expertises dans un but commun (UNESCO, 2021).Sur cette voie, la cration de rseaux d'coles et de communauts d'apprentissage favoriserait le dveloppement professionnel collectif et l'agir ensemble (Conseil suprieur de l'ducation, 2017 ; UNESCO, 2021).Pour surmonter les contraintes individuelles, organisationnelles et contextuelles susceptibles d'entraver ces ambitions, des recherches ont montr l'intrt des mesures de soutien et d'accompagnement (Curchod-Ruedi, et al., 2013 ; Maillot & Merini, 2021).Au Qubec, le dveloppement de l'agir collectif dans une perspective inclusive entre en contradiction avec le cloisonnement des champs d'expertise professionnelle, qui se trouve au fondement d'un ensemble de cadres d'organisation du travail (Conseil suprieur de l'ducation, 2017 ; Tardif & LeVasseur, 2010). Une dmarche de recherche-action a t mene auprs de membres du personnel de centres de services scolaires de la province pour dvelopper des aptitudes de collaboration interprofessionnelle et interorganisationnelle dans une perspective inclusive. partir de la thorie du leadership transformatif et de la notion d'agir ensemble (Blanger, et al., 2018 ;Glinas-Proulx & Shields, 2016), les rsultats mettent en lumire quatre constats et six dfis qui se rapportent aux leviers et freins qui conditionnent le dveloppement de ce type de collaboration.Les constats permettent d'approfondir les enjeux de confiance, respect et communication qui fondent la collaboration ; les dfis prcisent les difficults relatives la division du travail et aux rapports de pouvoir.

Recent advancements in speech-driven 3D talking head generation have made significant progress in lip synchronization. However, existing models still struggle to capture the perceptual alignment between varying speech characteristics and corresponding lip movements. In this work, we claim that three criteria—Temporal Synchronization, Lip Readability, and Expressiveness—are crucial for achieving perceptually accurate lip movements. Motivated by our hypothesis that a desirable representation space exists to meet these three criteria, we introduce a speech-mesh synchronized representation that captures intricate correspondences between speech signals and 3D face meshes. We found that our learned representation exhibits desirable characteristics, and we plug it into existing models as a perceptual loss to better align lip movements to the given speech. In addition, we utilize this representation as a perceptual metric and introduce two other physically grounded lip synchronization metrics to assess how well the generated 3D talking heads align with these three criteria. Experiments show that training 3D talking head generation models with our perceptual loss significantly improve all three aspects of perceptually accurate lip synchronization. Codes and datasets are available at https://perceptual-3d-talking-head.github.io/.

Binary rewriting is a rapidly-maturing technique for modifying software for instrumentation, customization, optimization, and hardening without access to source code. Unfortunately, the practical applications of binary rewriting tools are often unclear to users because their limitations are glossed over in the literature. This, among other challenges, has prohibited the widespread adoption of these tools. To address this shortcoming, we collect ten popular binary rewriters and assess their generality across a broad range of input binary classes and the functional reliability of the resulting rewritten binaries. Additionally, we evaluate the performance of the rewriters themselves as well as the rewritten binaries they produce. The goal of this broad evaluation is to establish a shared context for future research and development of binary rewriting tools by providing a state of the practice for their capabilities. To support potential binary rewriter users, we also identify input binary features that are predictive of tool success and show that a simple decision tree model can accurately predict whether a particular tool can rewrite a target binary. The binary rewriters, our corpus of 3344 sample binaries, and the evaluation infrastructure itself are all freely available as open-source software.

DevOps emphasizes a high degree of automation at all phases of the software development lifecyle. Meanwhile, Genetic Improvement (GI) focuses on the automatic improvement of software artifacts. In this paper, we discuss why we believe that DevOps offers an excellent technical context for easing the adoption of GI techniques by software developers. We also discuss A/B testing as a prominent and clear example of GI taking place in the wild today, albeit one with human-supervised fitness and mutation operators.

Decompilation, recovering source code from binary, is useful in many situations where it is necessary to analyze or understand software for which source code is not available. Source code is much easier for humans to read than binary code, and there are many tools available to analyze source code. Existing decompilation techniques often generate source code that is difficult for humans to understand because the generated code often does not use the coding idioms that programmers use. Differences from human-written code also reduce the effectiveness of analysis tools on the decompiled source code. To address the problem of differences between decompiled code and human-written code, we present a novel technique for decompiling binary code snippets using a model based on Recurrent Neural Networks. The model learns properties and patterns that occur in source code and uses them to produce decompilation output. We train and evaluate our technique on snippets of binary machine code compiled from C source code. The general approach we outline in this paper is not language-specific and requires little or no domain knowledge of a language and its properties or how a compiler operates, making the approach easily extensible to new languages and constructs. Furthermore, the technique can be extended and applied in situations to which traditional decompilers are not targeted, such as for decompilation of isolated binary snippets; fast, on-demand decompilation; domain-specific learned decompilation; optimizing for readability of decompilation; and recovering control flow constructs, comments, and variable or function names. We show that the translations produced by this technique are often accurate or close and can provide a useful picture of the snippet's behavior.

The prevalent usage of runtime packers has complicated Android malware analysis, as both legitimate and malicious apps are leveraging packing mechanisms to protect themselves against reverse engineer. Although recent efforts have been made to analyze particular packing techniques, little has been done to study the unique characteristics of Android packers. In this paper, we report the first systematic study on mainstream Android packers, in an attempt to understand their security implications. For this purpose, we developed DROIDUNPACK, a whole-system emulation based Android packing analysis framework, which compared with existing tools, relies on intrinsic characteristics of Android runtime (rather than heuristics), and further enables virtual machine inspection to precisely recover hidden code and reveal packing behaviors. Running our tool on 6 major commercial packers, 93,910 Android malware samples and 3 existing state-of-the-art unpackers, we found that not only are commercial packing services abused to encrypt malicious or plagiarized contents, they themselves also introduce securitycritical vulnerabilities to the apps being packed. Our study further reveals the prevalence and rapid evolution of custom packers used by malware authors, which cannot be defended against using existing techniques, due to their design weaknesses.

Automatic generation of non-linear loop invariants is a long-standing challenge in program analysis, with many applications. For instance, reasoning about exponentials provides a way to find invariants of digital-filter programs, and reasoning about polynomials and/or logarithms is needed for establishing invariants that describe the time or memory usage of many well-known algorithms. An appealing approach to this challenge is to exploit the powerful recurrence-solving techniques that have been developed in the field of computer algebra, which can compute exact characterizations of non-linear repetitive behavior. However, there is a gap between the capabilities of recurrence solvers and the needs of program analysis: (1) loop bodies are not merely systems of recurrence relations---they may contain conditional branches, nested loops, non-deterministic assignments, etc., and (2) a client program analyzer must be able to reason about the closed-form solutions produced by a recurrence solver (e.g., to prove assertions). This paper presents a method for generating non-linear invariants of general loops based on analyzing recurrence relations. The key components are an abstract domain for reasoning about non-linear arithmetic, a semantics-based method for extracting recurrence relations from loop bodies, and a recurrence solver that avoids closed forms that involve complex or irrational numbers. Our technique has been implemented in a program analyzer that can analyze general loops and mutually recursive procedures. Our experiments show that our technique shows promise for non-linear assertion-checking and resource-bound generation.

Recently, Esparza et al. generalized Newton's method-a numerical-analysis algorithm for finding roots of real-valued functions-to a method for finding fixed-points of systems of equations over semirings. Their method provides a new way to solve interprocedural dataflow-analysis problems. As in its real-valued counterpart, each iteration of their method solves a simpler "linearized" problem.

Machine-code synthesis is the problem of searching for an instruction sequence that implements a semantic specification, given as a formula in quantifier-free bit-vector logic (QFBV). Instruction sets like Intel's IA-32 have around 43,000 unique instruction schemas; this huge instruction pool, along with the exponential cost inherent in enumerative synthesis, results in an enormous search space for a machine-code synthesizer: even for relatively small specifications, the synthesizer might take several hours or days to find an implementation. In this paper, we present several improvements to the algorithms used in a state-of-the-art machine-code synthesizer McSynth. In addition to a novel pruning heuristic, our improvements incorporate a number of ideas known from the literature, which we adapt in novel ways for the purpose of speeding up machine-code synthesis. Our experiments for Intel's IA-32 instruction set show that our improvements enable synthesis of code for 12 out of 14 formulas on which McSynth times out, speeding up the synthesis time by at least 1981X, and for the remaining formulas, speeds up synthesis by 3X.

For many compiled languages, source-level types are erased very early in the compilation process. As a result, further compiler passes may convert type-safe source into type-unsafe machine code. Type-unsafe idioms in the original source and type-unsafe optimizations mean that type information in a stripped binary is essentially nonexistent. The problem of recovering high-level types by performing type inference over stripped machine code is called type reconstruction, and offers a useful capability in support of reverse engineering and decompilation. In this paper, we motivate and develop a novel type system and algorithm for machine-code type inference. The features of this type system were developed by surveying a wide collection of common source- and machine-code idioms, building a catalog of challenging cases for type reconstruction. We found that these idioms place a sophisticated set of requirements on the type system, inducing features such as recursively-constrained polymorphic types. Many of the features we identify are often seen only in expressive and powerful type systems used by high-level functional languages. Using these type-system features as a guideline, we have developed Retypd: a novel static type-inference algorithm for machine code that supports recursive types, polymorphism, and subtyping. Retypd yields more accurate inferred types than existing algorithms, while also enabling new capabilities such as reconstruction of pointer const annotations with 98% recall. Retypd can operate on weaker program representations than the current state of the art, removing the need for high-quality points-to information that may be impractical to compute.