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

In high-performance systems execution time is of crucial importance justifying advanced optimization techniques. Traditionally, optimization is based on static program analysis. The quality of program optimizations, however, can be substantially improved by utilizing runtime information. Probabilistic data-flow frameworks compute the probability with what data-flow facts may hold at some program point based on representative profile runs. Advanced optimizations can use this information in order to produce highly efficient code. In this paper we introduce a novel optimization technique in the context of High Performance Fortran (HPF) that is based on probabilistic data-flow information. We consider statically undefined attributes which play an important role for parallelization and compute for those attributes the probabilities to hold some specific value during runtime. For the most probable attribute values highly-optimized, specialized code is generated. In this way significantly better performance results can be achieved. The implementation of our optimization is done in the context of VFC, a source-to-source parallelizing compiler for HPF/F90.

Probabilistic communication optimizations and parallelization for distributed-memory systems

Partial redundancy elimination (PRE) is a key technology for modern compilers. However traditional approaches are conservative and fail to exploit many opportunities for optimization. New PRE approaches which greatly increase the number of eliminated redundancies have been developed. However, they either cause the code size to explode or they cannot handle statements with side-effects. In this paper we describe a predicated partial redundancy elimination (PPRE) approach which can potentially remove all partial redundancies. To avoid performance overheads caused by predication, PPRE is applied selectively based on a cost model. The cost analysis presented in the paper utilizes probabilistic data-flow information to decide whether PPRE is profitable for each instance of a partially redundant computation. Refinements of the basic PPRE transformation are described in detail. In contrast to some other approaches our transformation is strictly semantics preserving.

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Predicated Partial Redundancy Elimination using a Cost Analysis

Wireless sensor networks (WSN) and mobile clouds are composed of sensor nodes that have limited energy resources For wireless sensor networks, query processing is the state-of-the-art for data gathering and processing applications to avoid low-level programming The stream programming model has been widely used to represent queries as an information flow from the sensor nodes to the base station The model describes queries as stream graphs consisting of operators that process data and channels that connect operators Operators are deployed in the network to reduce the communication overhead and hence energy The modification of WSN queries at runtime is of key importance due to changes in the environment and the network energy levels, resulting in the migration of operators between the network nodesIn this work, we introduce the migrating operator placement problem (MOPP) that places operators of stream graphs on sensor nodes, such that energy costs are minimized The placement takes changes of queries and migration of operators into account The general MOPP is NP hard, and, therefore, we develop a dynamic program for a compositional subset of the stream graphs with polynomially-bounded running time To improve the performance of our algorithm, we introduce a heuristic that reduces the search space to the proximity of the base station We conduct various experiments using a simulator for wireless sensor networks with different sizes

Migrating operator placement for compositional stream graphs

Computing end-to-end delays in stream query processing

In recent years, Datalog has gained popularity for the implementation of advanced data analysis. Applications benefit from Datalog's high-level, declarative syntax, and availability of efficient algorithms for computing solutions. The efficiency of Datalog engines has reached a point where engines such as Souffle have reported performance results comparable to low-level hand-crafted alternatives [3].

Bernhard Scholz

Papers

Probabilistic communication optimizations and parallelization for distributed-memory systems

Predicated Partial Redundancy Elimination using a Cost Analysis

Migrating operator placement for compositional stream graphs

Computing end-to-end delays in stream query processing

Two concurrent data structures for efficient datalog query processing