CapablePtrs: Securely Compiling Partial Programs using the Pointers-as-Capabilities Principle

Capability machines such as CHERI provide memory capabilities that can be used by compilers to provide security benefits for compiled code (e.g., memory safety). The C to CHERI compiler, for example, achieves memory safety by following a principle called "pointers as capabilities" (PAC). Informally, PAC says that a compiler should represent a source language pointer as a machine code capability. But the security properties of PAC compilers are not yet well understood. We show that memory safety is only one aspect, and that PAC compilers can provide significant additional security guarantees for partial programs: the compiler can provide guarantees for a compilation unit, even if that compilation unit is later linked to attacker-controlled machine code. This paper is the first to study the security of PAC compilers for partial programs formally. We prove for a model of such a compiler that it is fully abstract. The proof uses a novel proof technique (dubbed TrICL, read trickle), which is of broad interest because it reuses and extends the compiler correctness relation in a natural way, as we demonstrate. We implement our compiler on top of the CHERI platform and show that it can compile legacy C code with minimal code changes. We provide performance benchmarks that show how performance overhead is proportional to the number of cross-compilation-unit function calls

Modeling relation paths for knowledge base completion via joint adversarial training

Knowledge Base Completion (KBC), which aims at determining the missing relations between entity pairs, has received increasing attention in recent years. Most existing KBC methods focus on either embedding the Knowledge Base (KB) into a specific semantic space or leveraging the joint probability of Random Walks (RWs) on multi-hop paths. Only a few unified models take both semantic and path-related features into consideration with adequacy. In this paper, we propose a novel method to explore the intrinsic relationship between the single relation (i.e. 1-hop path) and multi-hop paths between paired entities. We use Hierarchical Attention Networks (HANs) to select important relations in multi-hop paths and encode them into low-dimensional vectors. By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i.e. relation classifier and source discriminator), to capture shared/similar information between them. By joint adversarial training, we encourage our model to extract features from the multi-hop paths which are representative for relation completion. We apply the trained model (except for the source discriminator) to several large-scale KBs for relation completion. Experimental results show that our method outperforms existing path information-based approaches. Since each sub-module of our model can be well interpreted, our model can be applied to a large number of relation learning tasks.Comment: Accepted by Knowledge-Based System

FIXES, a system for automatic selection of set-ups and design of fixtures

This paper reports on the development of a computer aided planning system for the selection of set-ups and the design of fixtures in part manufacturing. First, the bottlenecks in the present planning methods are indicated. A brief description is given of the CAPP environment PART, in which FIXES is incorporated. The planning procedure of FIXES consists of two parts: the selection of set-ups and the design of a fixture for each set-up. The automatic selection of set-ups is based on the comparison of the tolerances of the relations between the different shape elements of the part. A tolerance factor has been developed to be able to compare the different tolerances. The system automatically selects the positioning faces and supports the selection of tools for positioning, clamping and supporting the part. A prototype implementation of FIXES is discussed