Low-level Programming Research Articles

Quadratic Program on a Structured Nonconvex Set

In this paper, we focus on a special nonconvex quadratic program whose feasible set is a structured nonconvex set. To find an effective method to solve this nonconvex program, we construct a bilevel program, where the low-level program is a convex program while the upper-level program is a small-scale nonconvex program. Utilizing some properties of the bilevel program, we propose a new algorithm to solve this special quadratic program. Finally, numerical results show that our new method is effective and efficient.

Towards Near-Real-Time Intrusion Detection for IoT Devices using Supervised Learning and Apache Spark

In the fields of Internet of Things (IoT) infrastructures, attack and anomaly detection are rising concerns. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing proportionally. In this paper the performances of several machine learning algorithms in identifying cyber-attacks (namely SYN-DOS attacks) to IoT systems are compared both in terms of application performances, and in training/application times. We use supervised machine learning algorithms included in the MLlib library of Apache Spark, a fast and general engine for big data processing. We show the implementation details and the performance of those algorithms on public datasets using a training set of up to 2 million instances. We adopt a Cloud environment, emphasizing the importance of the scalability and of the elasticity of use. Results show that all the Spark algorithms used result in a very good identification accuracy (>99%). Overall, one of them, Random Forest, achieves an accuracy of 1. We also report a very short training time (23.22 sec for Decision Tree with 2 million rows). The experiments also show a very low application time (0.13 sec for over than 600,000 instances for Random Forest) using Apache Spark in the Cloud. Furthermore, the explicit model generated by Random Forest is very easy-to-implement using high- or low-level programming languages. In light of the results obtained, both in terms of computation times and identification performance, a hybrid approach for the detection of SYN-DOS cyber-attacks on IoT devices is proposed: the application of an explicit Random Forest model, implemented directly on the IoT device, along with a second level analysis (training) performed in the Cloud.

An interactive ImageJ plugin for semi-automated image denoising in electron microscopy

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets.

Formal Reasoning Under Cached Address Translation

Operating system (OS) kernels achieve isolation between user-level processes using hardware features such as multi-level page tables and translation lookaside buffers (TLBs). The TLB caches address translation, and therefore correctly controlling the TLB is a fundamental security property of OS kernels—yet all large-scale formal OS verification projects we are aware of leave the correct functionality of TLB as an assumption. In this paper, we present a verified sound abstraction of a detailed concrete model of the memory management unit (MMU) of the ARMv7-A architecture. This MMU abstraction revamps our previous address space specific MMU abstraction to include new software-visible TLB features such as caching of globally-mapped and partial translation entries in a two-stage TLB. We use this abstraction as the underlying model to develop a logic for reasoning about low-level programs in the presence of cached address translation. We extract invariants and necessary conditions for correct TLB operation that mirrors the informal reasoning of OS engineers. We systematically show how these invariants adapt to global and partial translation entries. We show that our program logic reduces to a standard logic for user-level reasoning, reduces to side-condition checks for kernel-level reasoning, and can handle typical OS kernel tasks such as context switching.

A Practical Approach to Testing Random Number Generators in Computer Algebra Systems

This paper has a practical aim. For a long time, implementations of pseudorandom number generators in standard libraries of programming languages had poor quality. The situation started to improve only recently. Up to now, a large number of libraries and weakly supported mathematical packages use outdated algorithms for random number generation. Four modern sets of statistical tests that can be used for verifying random number generators are described. It is proposed to use command line utilities, which makes it possible to avoid low-level programming in such languages as C or C++. Only free open source systems are considered.

TTADF: Power Efficient Dataflow-Based Multicore Co-Design Flow

The era of mobile communications and the Internet of Things (IoT) has introduced numerous challenges for mobile processing platforms that are responsible for increasingly complex signal processing tasks from different application domains. In recent years, the power efficiency of computing has been improved by adding more parallelism and workload-specific computing resources to such platforms. However, programming of parallel systems can be time-consuming and challenging if only low-level programming methods are used. This work presents a dataflow-based co-design framework TTADF that reduces the design effort of both software and hardware design for mobile processing platforms. The paper presents three application examples from the fields of video coding, machine vision, and wireless communications. The application examples are mapped and profiled both on a pipelined and a shared-memory multicore platform that is generated by TTADF. The results of the TTADF co-design-based solutions are compared against previous manually created designs and a recent dataflow-based design flow, showing that TTADF provides very high energy efficiency together with a high level of automation in software and hardware design.

Reduced testicular steroidogenesis in rat offspring by prenatal nicotine exposure: Epigenetic programming and heritability via nAChR/HDAC4

Prenatal nicotine exposure (PNE) may lead to offspring's testicular dysplasia. Here, we confirmed the intergenerational effect of PNE on testosterone synthetic function and explored its epigenetic programming mechanism. Pregnant Wistar rats were injected subcutaneously with nicotine (2 mg/kg.d) from gestational day 9–20. Some dams were anesthetized to obtain fetal rats, the rest were allowed to spontaneous labor to generate F1 and F2 generation. In utero, PNE impaired testicular development and testosterone production. Meanwhile, the expression of steroidogenic acute regulatory protein (StAR) and 3β-hydroxysteroid dehydrogenase (3β-HSD) were decreased both in F1 and F2 generations. Furthermore, PNE enhanced the expression of fetal testicular nicotinic acetylcholine receptors (nAChRs) and histone deacetylase 4 (HDAC4), while obviously weakened histone 3 lysine 9 acetylation (H3K9ac) level of StAR/3β-HSD promoter from GD20 to postnatal week 12 and even in F2 generation. In vitro, nicotine increased nAChRs and HDAC4 expression, and decreased the StAR/3β-HSD H3K9ac level and expression, as well as the testosterone production in Leydig cells. Antagonism of nAChRs and inhibition of HDAC4 reversed the aforementioned changes. In conclusion, PNE programmed testicular low steroidogenesis and its heritability in male offspring rats. The underlying mechanism was associated to the low-level programming of StAR/3β-HSD H3K9ac via nAChR/HDAC4.

LAURA architecture: Towards a simpler way of building situation-aware and business-aware IoT applications

The explosion of smart objects made companies rethink their Business Model (BM) using Wireless Sensor Networks (WSN) and the Internet of Things (IoT) aiming to improve their Business Processes (BP) to achieve competitiveness. Business environments are complex due to the wide variety of technologies, hardware and software solutions that compose heterogeneous enterprise environments. On the other hand, putting real-world IoT scenarios into practice is still a challenge for even experienced developers, because it requires low-level programming skills and, at the same time, specific domain knowledge of a company`s BM. This research paper proposes LAURA – Lean AUtomatic code generation for situation-aware and business-awaRe Applications, a flexible, service-oriented and general open-source conceptual architecture, designed to support the deployment of decoupled IoT applications. Empirical evaluation has shown that LAURA simplifies the development of final Situation-Aware or Business-Aware applications, reducing the need for specialized IoT low-level knowledge, while showing an acceptable performance. LAURA also provides the freedom and independence to modify, adapt or integrate its architecture according to specific needs of the stakeholders.

Bilevel Optimization Problems of Distribution of Interbudgetary Transfers Under Given Limitations

The problems of optimal distribution of transfers within given budget limitations are formulated and analyzed. The mathematical model is presented as a bilevel linear optimization problem that contains linear problems of integer optimization at the lower level. Both optimistic and pessimistic versions of the problem are considered. For the approximate solution of optimistic version, the algorithm of finding local solutions for parametric lower-level integer programming problems on the basis of the method of directing neighborhoods is proposed. The auxiliary integer programming problem with Boolean variables of a higher level is solved based on local algorithms.

Development and implementation of a software and hardware complex of the controls automation into the self-driving car system based on the "GAZelle NEXT" vehicle

This article describes the stages and the results of automation of the car controls, as part of the self-driving car based o n the light commercial vehicle (LCV) car class. Some aspects of automation of the car equipped with a manual transmission are touched upon. A block diagram of the automation system of the vehicle controls is provided. The variant of integration of modules of controls automation in the base car is shown. The description of the lower level program is given.

Simulated annealing and genetic algorithm based method for a bi-level seru loading problem with worker assignment in seru production systems

<p style='text-indent:20px;'><i>Seru</i> production is one of the latest manufacturing modes arising from Japanese production practice. <i>Seru</i> can achieve efficiency, flexibility, and responsiveness simultaneously. To accommodate the current business environment with volatile demands and fierce competitions, <i>seru</i> has attracted more and more attention both from researchers and practitioners. A new planning management system, just-in-time organization system (JIT-OS), is used to manage and control a <i>seru</i> production system. The JIT-OS contains two decisions: <i>seru</i> formation and <i>seru</i> loading. By <i>seru</i> formation, a <i>seru</i> system with one or multiple appropriate <i>serus</i> is configured; by <i>seru</i> loading, customer ordered products are allocated to <i>serus</i> to implement production plans. In the process of <i>seru</i> formation, workers have to be assigned to <i>serus</i>. In this paper, a <i>seru</i> loading problem with worker assignment is constructed as a bi-level programming model, and the worker assignment on the upper level is to minimize total idle time while the lower level is to minimize the makespan by finding out optimal product allocation. A product lot can be splitted and allocated to different <i>serus</i>. The problem of this paper is shown to be NP-hard. Therefore, a simulated annealing and genetic algorithm (SA-GA) is developed. The SA is for the upper level programming and the GA is for the lower level programming. The practicality and effectiveness of the model and algorithm are verified by two numerical examples, and the results show that the SA-GA algorithm has good scalability.

Brian 2, an intuitive and efficient neural simulator.

Brian 2 allows scientists to simply and efficiently simulate spiking neural network models. These models can feature novel dynamical equations, their interactions with the environment, and experimental protocols. To preserve high performance when defining new models, most simulators offer two options: low-level programming or description languages. The first option requires expertise, is prone to errors, and is problematic for reproducibility. The second option cannot describe all aspects of a computational experiment, such as the potentially complex logic of a stimulation protocol. Brian addresses these issues using runtime code generation. Scientists write code with simple and concise high-level descriptions, and Brian transforms them into efficient low-level code that can run interleaved with their code. We illustrate this with several challenging examples: a plastic model of the pyloric network, a closed-loop sensorimotor model, a programmatic exploration of a neuron model, and an auditory model with real-time input.

Prospects for Declarative Mathematical Modeling of Complex Biological Systems

Declarative modeling uses symbolic expressions to represent models. With such expressions, one can formalize high-level mathematical computations on models that would be difficult or impossible to perform directly on a lower-level simulation program, in a general-purpose programming language. Examples of such computations on models include model analysis, relatively general-purpose model reduction maps, and the initial phases of model implementation, all of which should preserve or approximate the mathematical semantics of a complex biological model. The potential advantages are particularly relevant in the case of developmental modeling, wherein complex spatial structures exhibit dynamics at molecular, cellular, and organogenic levels to relate genotype to multicellular phenotype. Multiscale modeling can benefit from both the expressive power of declarative modeling languages and the application of model reduction methods to link models across scale. Based on previous work, here we define declarative modeling of complex biological systems by defining the operator algebra semantics of an increasingly powerful series of declarative modeling languages including reaction-like dynamics of parameterized and extended objects; we define semantics-preserving implementation and semantics-approximating model reduction transformations; and we outline a “meta-hierarchy” for organizing declarative models and the mathematical methods that can fruitfully manipulate them.

MakeCode and CODAL: Intuitive and efficient embedded systems programming for education

Abstract Historically, embedded systems development has been a specialist skill, requiring knowledge of low-level programming languages, complex compilation toolchains, and specialist hardware, firmware, device drivers and applications. However, it has now become commonplace for a broader range of non-specialists to engage in the making (design and development) of embedded systems - including educators to motivate and excite their students in the classroom. This diversity brings its own set of unique requirements, and the complexities of existing embedded systems development platforms introduce insurmountable barriers to entry. In this paper we present the motivation, requirements, implementation, and evaluation of a new programming platform that enables novice users to create effective and efficient software for embedded systems. The platform has two major components: (1) Microsoft MakeCode ( www.makecode.com ), a web app that encapsulates an accessible IDE for microcontrollers; and (2) CODAL, an efficient component-oriented C++ runtime for microcontrollers. We show how MakeCode and CODAL combine to provide an accessible, cross-platform, installation-free, high level programming experience for embedded devices without sacrificing performance and efficiency.

Generation of high-performance code based on a domain-specific language for algorithmic skeletons

Parallel programming can be difficult and error prone, in particular if low-level optimizations are required in order to reach high performance in complex environments such as multi-core clusters using MPI and OpenMP. One approach to overcome these issues is based on algorithmic skeletons. These are predefined patterns which are implemented in parallel and can be composed by application programmers without taking care of low-level programming aspects. Support for algorithmic skeletons is typically provided as a library. However, optimizations are hard to implement in this setting and programming might still be tedious because of required boiler plate code. Thus, we propose a domain-specific language for algorithmic skeletons that performs optimizations and generates low-level C++ code. Our experimental results on four benchmarks show that the models are significantly shorter and that the execution time and speedup of the generated code often outperform equivalent library implementations using the Muenster Skeleton Library.

Big SQL systems: an experimental evaluation

Recently, Big Data systems have been gaining increasing popularity on handling the massive amounts of data that are continuously generated in our digital world. While the Hadoop framework has pioneered the area of Big Data processing systems, it had clear performance limitations on providing the best performance of processing massive amounts of structured data. In addition, practically, many users of the big data systems face some challenges on dealing with the APIs and the low level programming abstractions of the Big Data System and they would prefer to use SQL (in which they are more proficient) as a high-level declarative language to express their tasks while leaving all of the execution optimization details to the backend engine. Thus, several systems have been designed and implemented to tackle these challenges by designing and implementing scalable query execution engines for processing massive structured data while supporting SQL interfaces. In this article, we present an extensive experimental study of four popular systems in this domain, namely, Apache Hive, SPARK SQL, Apache Impala and PrestoDB. In particular, we report and analyze the performance characteristics of these systems using three different benchmarks, namely, TPC-H, TPC-DS and TPCx-BB. Finally, we report a set of insights and important lessons that we have learned from conducting our experiments.

Optimal Scheduling of Demand Response in Pre-Emptive Markets Based on Stochastic Bilevel Programming Method

This paper proposes a new strategy for an independent system operator (ISO) to trade demand response (DR) with different DR aggregators while considering various operational constraints. The ISO determines the energy scheduling and reserve deployment in a pre-emptive market while setting DR contracts with the DR aggregators. The ISO applies a two-stage stochastic programming to cope with the uncertainty associated with wind power production. DR aggregators’ behavior is modeled through a profit maximization function. Aggregators determine their DR trading shares with ISO and customers through three DR options, including load curtailment, load shifting, and load recovery. A stochastic bilevel problem is formulated, in which in the upper level, the ISO minimizes the total operation cost, and in the lower level, the DR aggregator maximizes the profit. Afterwards, the problem is transferred to a single-level mathematical problem with equilibrium constraints by replacing the lower level program with its Karush–Kuhn–Tucker (KKT) conditions. As a result, the total operation cost is reduced using the proposed method comparatively to run the program without considering the lower level.

Application of laboratory stands SDK in education

The up-to-date labor market in the field of IT-technologies in Russia is studied, the required competencies for applicants are analyzed. The problems are shown associated with teaching students to design embedded and cyber-physical systems, as well as the features of traditional approaches to teaching software engineers. We propose an approach to the training quality improvement for software engineers of information systems through the use of low-level programming. The developed modular training laboratory stand SDK-1.1M is presented.

Focus Programming: A Fundamental Alternative for Stochastic Optimization Problems

A fundamental alternative for stochastic optimization problems named focus programming is proposed based on the focus theory of choice. Different from the existing approaches such as chance-constrained programming and two-stage stochastic programming which are based on expected utility theory, focus programming determines the optimal solution according to which solution’s focus (the most salient realization of random vector) is the most preferred. Focus programming models are bilevel programming problems with maximin-type upper and lower level programs which are interesting and challenging. Two equivalent single-level reformulations of the focus programming models have been proposed for the discrete random vector case.

Криптоэкономика: пилотный анализ инвестиций в ICO-стартапы с использованием нейронных сетей

In the field of cryptoeconomics the Ethereum (Ethereum Foundation) project gave opportunity to create “own” cryptocurrency – new token based on its smart-contract platform to everyone without lowlevel programming skills. Then it became obvious that tokens could be used for crowdfunding as the Ethereum did in 2014. Unregulated and easy to access such scheme became popular among related to the blockchain tech startups. It was named Initial Coin Offering (ICO/or ITO). Despite its name, which is similar to IPO, this scheme is usually used for venture funding of a new project instead of expanding already well-established working business. The authors use machine-learning algorithms to classify ICOs and estimate ROI based on public digital data and web-sources. The goal of the research is to develop sustainable and efficient model, which will predict target profit ROI (profit trends) of ICO startup. Data collection and analysis period: Feb-Mar 2018. The prediction model and the application (service) of ICO startups’ selection are developed as the result of the study. Results. There were over 3000 samples of ICO-startups in the research dataset. After cleaning and elimination of outliers, it contained only 518. The number of samples with positive ROI (which means that these ICOs were profitable) was 234. Cross validation metric was confirmed to be accurate. The model achieved 79 % accuracy (average value). To prove this score separated prediction was executed the metrics: for test dataset AUC is 0.78; for profitable samples Precision: 0.76; Recall: 0.9 for profitable; F1-score: 0.82. Discussions. In order to achieve the objectives of this study, various IT components of the service architecture (applications) were developed to monitor, analyze and predict the risks of ICO startups. An artificial neural network was developed to solve the problem of ROI classification and prediction. The average ROI among profitable ICOS was 47 %. Taking into consideration that the crypto market is highly volatile and that there is a possibility that such investments will not bring any profit, this model of monitoring, analysis and prediction can be very valuable for the purposes of critical selection (exclusion) of a number of ICO projects from potential investment. Conclusion. The developed components can be used as a basis of monitoring service of ICO startups. The risk-forecasting model can be improved, foremost, by using the most complete (and wider) set of data. In this case, individual data collection and processing tasks can be performed manually, which will require additional resources. It should be noted that other types of neural networks can be developed for both text analysis and trading data analysis. This may lead to the logic of using a combination of models, which will potentially help to provide the most accurate predictions.