Mathematica--A System for Doing Mathematics by Computer.

The Internet of Things (IoT) envisages a future in which digital and physical things or objects (e.g., smartphones, TVs, cars) can be connected by means of suitable information and communication technologies, to enable a range of applications and services. The IoT’s characteristics, including an ultra-large-scale network of things, device and network level heterogeneity, and large numbers of events generated spontaneously by these things, will make development of the diverse applications and services a very challenging task. In general, middleware can ease a development process by integrating heterogeneous computing and communications devices, and supporting interoperability within the diverse applications and services. Recently, there have been a number of proposals for IoT middleware. These proposals mostly addressed wireless sensor networks (WSNs), a key component of IoT, but do not consider RF identification (RFID), machine-to-machine (M2M) communications, and supervisory control and data acquisition (SCADA), other three core elements in the IoT vision. In this paper, we outline a set of requirements for IoT middleware, and present a comprehensive review of the existing middleware solutions against those requirements. In addition, open research issues, challenges, and future research directions are highlighted.

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Middleware for Internet of Things: A Survey

Concrete mathematics, a foundation for computer science, by Ronald L. Graham, Donald E. Knuth and Oren Patashnik. Pp 625. £24·95. 1989. ISBN 0-201-14236-8 (Addison-Wesley)

Permissionless blockchains allow the execution of arbitrary programs (called smart contracts), enabling mutually untrusted entities to interact without relying on trusted third parties. Despite their potential, repeated security concerns have shaken the trust in handling billions of USD by smart contracts. To address this problem, we present Securify, a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property. Securify's analysis consists of two steps. First, it symbolically analyzes the contract's dependency graph to extract precise semantic information from the code. Then, it checks compliance and violation patterns that capture sufficient conditions for proving if a property holds or not. To enable extensibility, all patterns are specified in a designated domain-specific language. Securify is publicly released, it has analyzed >18K contracts submitted by its users, and is regularly used to conduct security audits by experts. We present an extensive evaluation of Securify over real-world Ethereum smart contracts and demonstrate that it can effectively prove the correctness of smart contracts and discover critical violations.

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Securify: Practical Security Analysis of Smart Contracts

This paper presents S2E, a platform for analyzing the properties and behavior of software systems. We demonstrate S2E's use in developing practical tools for comprehensive performance profiling, reverse engineering of proprietary software, and bug finding for both kernel-mode and user-mode binaries. Building these tools on top of S2E took less than 770 LOC and 40 person-hours each.S2E's novelty consists of its ability to scale to large real systems, such as a full Windows stack. S2E is based on two new ideas: selective symbolic execution, a way to automatically minimize the amount of code that has to be executed symbolically given a target analysis, and relaxed execution consistency models, a way to make principled performance/accuracy trade-offs in complex analyses. These techniques give S2E three key abilities: to simultaneously analyze entire families of execution paths, instead of just one execution at a time; to perform the analyses in-vivo within a real software stack--user programs, libraries, kernel, drivers, etc.--instead of using abstract models of these layers; and to operate directly on binaries, thus being able to analyze even proprietary software.Conceptually, S2E is an automated path explorer with modular path analyzers: the explorer drives the target system down all execution paths of interest, while analyzers check properties of each such path (e.g., to look for bugs) or simply collect information (e.g., count page faults). Desired paths can be specified in multiple ways, and S2E users can either combine existing analyzers to build a custom analysis tool, or write new analyzers using the S2E API.

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S2E: a platform for in-vivo multi-path analysis of software systems

We propose a new approach to synthesize Datalog programs from input-output specifications. Our approach leverages query provenance to scale the counterexample-guided inductive synthesis (CEGIS) procedure for program synthesis. In each iteration of the procedure, a SAT solver proposes a candidate Datalog program, and a Datalog solver evaluates the proposed program to determine whether it meets the desired specification. Failure to satisfy the specification results in additional constraints to the SAT solver. We propose efficient algorithms to learn these constraints based on “why” and “why not” provenance information obtained from the Datalog solver. We have implemented our approach in a tool called ProSynth and present experimental results that demonstrate significant improvements over the state-of-the-art, including in synthesizing invented predicates, reducing running times, and in decreasing variances in synthesis performance. On a suite of 40 synthesis tasks from three different domains, ProSynth is able to synthesize the desired program in 10 seconds on average per task—an order of magnitude faster than baseline approaches—and takes only under a second each for 28 of them.

https://dl.acm.org/doi/pdf/10.1145/3371130

Provenance-guided synthesis of Datalog programs

In this paper we describe efficient symbolic evaluation techniques to compute the values of variables and symbolic expressions, and to determine the condition under which control flow reaches a program statement at compile time. Computations are represented as algebraic expressions over the input data which maintains the crucial relationship between input data and the resulting analysis information. Our symbolic evaluation techniques comprise accurate modeling of assignment and conditional statements, loops, recurrences, arrays (including indirect accesses) and procedures. Efficiency and accuracy is highly improved by aggressive usage of simplification techniques. Examples including program verification, dependence analysis, array privatization, communication vectorization, and elimination of redundant communication are used to illustrate how our symbolic evaluation techniques support program optimization in the context of a distributed memory parallelizing compiler.

Symbolic evaluation for parallelizing compilers

We present a novel 2-approximation algorithm for deploying stream graphs on multicore computers and a stream graph transformation that eliminates bottlenecks. The key technical insight is a data rate transfer model that enables the computation of a "closed form", i.e., the data rate transfer function of an actor depending on the arrival rate of the stream program. A combinatorial optimization problem uses the closed form to maximize the throughput of the stream program. Although the problem is inherently NP-hard, we present an efficient and effective 2-approximation algorithm that provides a lower bound on the quality of the solution. We introduce a transformation that uses the closed form to identify and eliminate bottlenecks.We show experimentally that state-of-the art integer linear programming approaches for orchestrating stream graphs are (1) intractable or at least impractical for larger stream graphs and larger number of processors and (2)our 2-approximation algorithm is highly efficient and its results are close to the optimal solution for a standard set of StreamIt benchmark programs.

Orchestration by approximation: mapping stream programs onto multicore architectures

In this work we present a new heuristic for PBQP which significantly improves the quality of its register allocations and extends the range of viable target architectures. We also introduce a new branch-and-bound technique for PBQP that is able to find optimal register allocations.

We evaluate each of these methods, as well as a state of the art graph colouring method, using SPEC2000 and IA-32 as a testbed. Spill costs are used as a metric for comparison. We provide experimental evidence that our new heuristic allows PBQP to remain effective even for relatively regular architectures such as IA-32, generating results equal to those of a start-of-the-art graph colouring technique. Our method is shown to run 3–4 times slower than graph colouring, however it supports a wide range of irregularities.

Using our branch-and-bound solver for PBQP we were able to solve 97.4% of the functions in SPEC2000 optimally. These results are used as a yardstick to show that both PBQP and graph colouring produce results which are very close to optimal.

Nearly optimal register allocation with PBQP

Dynamic binary translators compile machine code from a source architecture to a target architecture at run time. Due to the hard time constraints of just-in-time compilation only highly efficient optimization algorithms can be employed. Common problems are an insufficient number of registers on the target architecture and the different handling of condition codes in source and target architecture. Without optimizations useless stores and computations are generated by the dynamic binary translator and cause significant performance losses. In order to eliminate these useless operations, a very fast liveness analysis is required. We present a dynamic liveness analysis algorithm that trades precision for fast execution and conducted experiments with the SpecInt95 benchmark suite using our PowerPC to Alpha translator. The optimizations reduced the number of stores by about 50 percent. This resulted in a speed-up of 10 to 30 percent depending on the target machine. The dynamic liveness analysis results are very close to the most precise solution.

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Bernhard Scholz

Papers

Provenance-guided synthesis of Datalog programs

Symbolic evaluation for parallelizing compilers

Orchestration by approximation: mapping stream programs onto multicore architectures

Nearly optimal register allocation with PBQP

Register liveness analysis for optimizing dynamic binary translation