Runtime Values Research Articles

Feasibility assessments of a dynamical approach to compartmental modelling on graphs: Scaling limits and performance analysis

Sharkey, Kiss and others developed a dynamical approach to modelling epidemic disease on a contact graph by generating systems of first-order ordinary differential equations expressing the model dynamics [1,2], which are solved to yield exact and deterministic modelling results. However, they left algorithmic generation (and solving) of systems and runtime assessment of the approach as an open question. To address this, we give an open source implementation that takes both a compartmental model and a contact graph as input and then generates and solves a system of equations exactly describing the dynamics of the system. Our implementation uses a moment closure result on single-vertex cutsets in the contact graph to reduce the number of equations required. In runtime experiments, we find that the implementation of the dynamical approach is almost always slower than a comparable Monte Carlo simulation in finding the expected state of the modelling system at a specified time. To complement our runtime evaluations, we give results and bounds on the number of equations required to describe a system as a function of the size of the compartmental model and input graph. We show that a natural extension of the moment closure result on single-vertex cutsets to larger cutsets is only possible for restricted projections of the model states on the cutset. We conclude that the dynamical approach is unlikely to be suitable unless exact, deterministic (rather than simulated) results are essential.

Dynamic Human-in-the-Loop Assertion Generation

Test cases use assertions to check program behaviour. While these assertions may not be complex, they are themselves code that must be written correctly in order to determine whether a test case should pass or fail. We claim that most test assertions are relatively repetitive and straight-forward, making their construction well suited to automation and that this automation can reduce developer effort while improving assertion quality. Examining 33,873 assertions from 105 projects revealed that developer-written assertions fall into twelve high-level categories, confirming that the vast majority (>90%) of test assertions are fairly simple in practice. We created AutoAssert, a human-in-the-loop tool to fit naturally into a developer's test-writing workflow by automatically generating assertions for JavaScript and TypeScript test cases. A developer invokes AutoAssert by identifying the variable they want validated; AutoAssert uses dynamic analysis to generate assertions relevant for this variable and its runtime values, injecting the assertions into the test case for the developer to accept, modify, delete. Comparing AutoAssert's assertions to those written by developers, we found that the assertions generated by AutoAssert are the same kind of assertion as was written by developers 84% of the time in a sample of over 1,000 assertions. Additionally we validated the utility of AutoAssert-generated assertions with 17 developers who found the majority of generated assertions to be useful and expressed considerable interest in using such a tool for their own projects.

Analysis based on statistical distributions: A practical approach for stochastic solvers using discrete and continuous problems

This paper proposes an approach for the analysis and comparison of stochastic solvers based on the statistical distribution of their variables. The observed variables of the stochastic solvers are the runtime and number of function evaluations required to reach a (sub)-optimal solution. These variables were measured using a target approach. We extended the conventional approach, which usually predicts the average value of variables, by predicting their statistical distributions. If possible, we can predict, not only the average value, but also all the values of the observed variables according to the identified distribution and given probability. We can also predict the probability of reaching (sub)-optimal solutions according to the variable's values. The approach was empirically validated by comparing solvers for discrete and continuous problems. In our experiment, the differences between predicted and measured values of runtime, number of function evaluations and probability are 10% or less. This proves that the proposed approach is useful and can be used for the analysis and comparison of stochastic solvers. Although, the approach has some limitations, it can mitigate the issue of stochastic solvers being unable to provide (sub)-optimal solutions, and can be used to determine the stopping optimization criteria.

Provenance-Preserving Analysis and Rewrite of Quantum Workflows for Hybrid Quantum Algorithms

Quantum applications are hybrid, i.e., they comprise quantum and classical programs, which must be orchestrated. Workflows are a proven solution for orchestrating heterogeneous programs while providing benefits, such as robustness or scalability. However, the orchestration using workflows can be inefficient for some quantum algorithms, requiring the execution of quantum and classical programs in a loop. Hybrid runtimes are offered to efficiently execute these algorithms. For this, the quantum and classical programs are combined in a single hybrid program, for which the execution is optimized. However, this leads to a conceptual gap between the modeling benefits of workflow technologies, e.g., modularization, reuse, and understandability, and the efficiency improvements when using hybrid runtimes. To close this gap, we introduce a method to model all tasks explicitly in the workflow model and analyze the workflow to detect parts of the workflow that can benefit from hybrid runtimes. Furthermore, corresponding hybrid programs are automatically generated based on the quantum and classical programs, and the workflow is rewritten to invoke them. To ease the live monitoring and later analysis of workflow executions, we integrate process views into our method and collect related provenance data. Thus, the user can visualize and monitor the workflow in the original and rewritten form within the workflow engine. The practical feasibility of our approach is validated by a prototypical implementation, a case study, and a runtime evaluation.

Detection of Weak Pulse Signal in Chaotic Noise Based on Improved Brain Emotional Learning Model and PSO-AGA

A model for detecting weak pulse signals in chaotic noise was proposed. Firstly, based on the short-term predictability of chaotic signals, according to Takens’s theorem, the phase space of observed signal was reconstructed. Then, an improved brain emotional learning (BEL) model combined with PSO-AGA was proposed to predict chaotic signals, and the one-step prediction error was obtained. In order to optimize the parameters of the BEL model, an algorithm named PSO-AGA combined with particle swarm optimization and adaptive genetic algorithm was adopted to achieve the balance of global search and local search capabilities. Finally, the hypothesis testing method was used to detect whether there existed the pulse signal from the one-step prediction error. The experiments simulated the Lorenz system and the magnetic storm loop current system. In the Lorenz system, the MAD of BEL-PSO-AGA, BP-NN-PSO-AGA, and Wavelet-NN-PSO-AGA were 0.0022, 0.0142, and 0.0076; the MSE were 8.95 × 1 0 − 6 , 0.00034, and 0.00016; the RMSE were 0.0029, 0.0187, and 0.0128; the running times were 410 s, 792 s, and 721 s; the ACC were 0.999, 0.972, and 0.997; the F1 were 0.984, 0.423, and 0.878. It could be seen that the BEL model had better performance, shorter running time and higher values of the ACC and F1, indicated that the BEL model ran faster and had a better predictive effect. The MAD of BEL-PSO-AGA, BEL-WOA, BEL-AGA, and BEL-PSO were 0.0022, 0.0065, 0.0135, and 0.0071; the MSE were 8.95 × 1 0 − 6 , 0.00013, 0.00029, and 0.00014; the RMSE were 0.0029, 0.0115, 0.0173, and 0.0119; the ACC were 0.999, 0.992, 0.990, and 0.997; the F1 were 0.984, 0.733, 0.451, and 0.878. This indicated that the PSO-AGA also had better performance and higher prediction accuracy. In the magnetic storm loop current system, the experimental results were similar to the Lorenz experiment, which also indicated that the BEL-PSO-AGA model was better. To sum up, the detection results of simulations showed that the proposed model and algorithm could effectively detect weak pulse signals from the chaotic noise.

Time Domain Implementation of Consistent Boundaries for Continuum Two-Dimensional Wave Propagation Problems Using ABAQUS User-Defined Elements

The present paper details the finite element implementation procedure for applying consistent boundaries for two-dimensional continuum soil-structure interaction systems. Consistent boundary conditions are ensured by applying rigorously calculated interaction forces at the truncated soil-structure interface. These calculations involve the evaluation of convolution integral on acceleration unit-impulse response functions for all boundary degrees of freedom for all time steps. The interactive or run-time evaluation of the interaction forces based on the past response history, is successfully carried out using user-defined element formulation in ABAQUS. This precise implementation of this boundary element procedure is demonstrated through benchmark problems and demonstrated through typical stress-wave propagation problems.

BHGSO: Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

In machine learning and data mining, feature selection (FS) is a traditional and complicated optimization problem. Since the run time increases exponentially, FS is treated as an NP-hard problem. The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios. This paper presents two binary variants of a Hunger Games Search Optimization (HGSO) algorithm based on V- and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset. The proposed technique transforms the continuous HGSO into a binary variant using V- and S-shaped transfer functions (BHGSO-V and BHGSO-S). To validate the accuracy, 16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms. The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features, classification accuracy, run time, and fitness values than other state-of-the-art algorithms. The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems. The proposed BHGSO-V achieves 95% average classification accuracy for most of the datasets, and run time is less than 5 sec. for low and medium dimensional datasets and less than 10 sec for high dimensional datasets.

3D Contour Shaping of Buried Objects in Soil

Abstract. The basic question of this paper was, whether a detected anomaly found in the ground during an explosives disposal process is actually a non-detonated bomb or non-dangerous metallic scrap. Based on a borehole radar, an approach is to be presented in which first a 2-dimensional contour of the object is created with the aid of a spatial runtime evaluation. By repeating this step at different depths with subsequent graphic overlay, a 3D shape of the buried object is created. The method is first tested using a simulation model with inhomogeneous soil. In the second step the method will be applied and evaluated using a field measurement of a real object. The results shows that both 2D and 3D evaluations reflect the position and orientation of the object. Furthermore, the shape and the dimensions can be estimated, with the restriction that the 3D contour has distortions along the vertical axis. The aim of this work is to show an application of borehole radar, with which the identification of buried objects should be facilitated.

Generic approach in a gradient elution HPLC method development that enables troubleshooting free method transfer

Nowadays, method development is strongly focused on reducing time needed for method development and execution. This subject specially concerns gradient elution methods regarding the usual need for troubleshooting assistance with uncertain outcome during the method transfer from one laboratory to another. One of the main reasons for this situation is the dwell volume difference between HPLC systems. Therefore, the aim of this study was to propose a novel method development methodology that would integrate the dwell volumes differences in the optimization process. The proposed approach could be quite useful in industry that has insight in HPLC instruments planned to be used during the method life cycle. It was tested on the model mixture consisting of dabigatran etexilate mesylate and its nine impurities by use of experimental design methodology. Three different (U)HPLC instruments with high dwell volume differences were selected to challenge the methodology. Plan of experiments was defined with Plackett-Burman design for screening phase and D-optimal design for optimization phase. Initial and final amount of organic modifier, time of the gradient elution and pH value of the aqueous phase were selected as variables significant for the gradient programme profile and included in the optimization stage along with dwell volume values. The separation criteria s between critical peak pairs was selected as output for method optimization while indirect modelling together with Monte Carlo simulations enabled selection of optimal and robust chromatographic conditions. They included 24% (v/v) of initial amount of acetonitrile, 54% (v/v) of the final amount of acetonitrile, 15 min of gradient elution run time and pH value equal to 4.9. The proposed method was successfully validated, met all validation criteria and thus proved its utility.

Uniform probabilistic generation of relation instances satisfying a functional dependency

Software engineering includes the runtime evaluation of a prototype by experiments with carefully selected sample inputs. For the development of software intended to operate on relation instances for a given relational schema with a single functional dependency, we are then challenged to generate appropriate sample instances of increasing size. Moreover, studying the impact of varying the sizes of the attribute domains might be important. We focus on seeing uniformly distributed collections of sample instances to be appropriate. If the schema is normalized (in Boyce–Codd normal form with the left-hand side of the functional dependency forming a unique key), then a straightforward heuristic insertion procedure complies with our requirements. However, for a non-normalized schema (the left-hand side not forming a key), based on a complex combinatorial analysis and exploiting an algorithm for restricted integer partitions, we develop a sophisticated probabilistic procedure of high computational complexity for generating any element of such a collection with equal probability. Moreover, we demonstrate that simpler approaches based on uniform local selections, including the heuristic insertion procedure, fail to achieve global uniformity. The conceptual design and analysis are complemented by an experimental evaluation of a prototype implementation.

Improved Genetic Algorithm for Phase-Balancing in Three-Phase Distribution Networks: A Master-Slave Optimization Approach

This paper addresses the phase-balancing problem in three-phase power grids with the radial configuration from the perspective of master–slave optimization. The master stage corresponds to an improved version of the Chu and Beasley genetic algorithm, which is based on the multi-point mutation operator and the generation of solutions using a Gaussian normal distribution based on the exploration and exploitation schemes of the vortex search algorithm. The master stage is entrusted with determining the configuration of the phases by using an integer codification. In the slave stage, a power flow for imbalanced distribution grids based on the three-phase version of the successive approximation method was used to determine the costs of daily energy losses. The objective of the optimization model is to minimize the annual operative costs of the network by considering the daily active and reactive power curves. Numerical results from a modified version of the IEEE 37-node test feeder demonstrate that it is possible to reduce the annual operative costs of the network by approximately 20% by using optimal load balancing. In addition, numerical results demonstrated that the improved version of the CBGA is at least three times faster than the classical CBGA, this was obtained in the peak load case for a test feeder composed of 15 nodes; also, the improved version of the CBGA was nineteen times faster than the vortex search algorithm. Other comparisons with the sine–cosine algorithm and the black hole optimizer confirmed the efficiency of the proposed optimization method regarding running time and objective function values.

Parallel genetic algorithms for optimizing the SARIMA model for better forecasting of the NCDC weather data

Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) models are common techniques that are widely used in analysing and forecasting stationary and seasonal time series data. The three essential steps involved to construct ARIMA are identification, estimation, and checking the validity of the model. The most critical step followed in constructing the ARIMA model is model identification. However, overcoming the difficult local optima problem for both ARIMA and SARIMA is still challenging as there is no appropriate method to solve it. In this paper, the proposed parallel GA-SARIMA model is used to solve the problem of local optima, where the genetic algorithm (GA) is used at the initial stage to identify the order and estimation of the parameters for SARIMA. The National Climate Data Centre (NCDC) time series dataset is used for testing the efficiency of the final parallel GA-SARIMA model to forecast the mean temperature of India from 2000 to 2017. The GA algorithm is successfully implemented to solve the optimization problems by introducing better solutions suitable for SARIMA models. The results of the study showed that the implementation of the combined approach of parallel GA and SARIMA enhances the prediction accuracy of the model. The parallel GA-SARIMA method is particularly robust, faster and performs better than sequential SARIMA models in terms of running time and cost function values.

The Effect Of Percussive Massage Versus Foam Rolling On Recovery Between Two 1000 Meter Runs

Interest in effective methods to reduce recovery time related to athletic performance is an area of interest in competitive sports. Many of the methods purported to enhance recovery and improve repeat performances are unproven. These methods include foam rolling and more recently, percussive massage. PURPOSE: The purpose of this study was to compare repeated running performance under the conditions of no massage (NM), foam rolling (FR) and percussive massage (PM) when implemented during the rest period between two running trials. METHODS: 20 recreationally active college-age students (age: 22.5 ± 2.8 yrs., height: 176.2 ± 8.0 cm, body mass: 70.8 ± 11.1 kg, 11 ♂) volunteered to participate in a randomized, single-blind, crossover design study. Three trials consisting of two repeat 1000m runs separated by 30 minutes of rest were conducted with a minimum of 48 hrs. between trials. The 30 minute recovery was to simulate the time between races in a track event. Finger stick blood lactates were taken three minutes post run, as well as before the second run. During the 30 minute recovery the subjects received one of the three treatments NM, FR, and PM. For PM the treatment was applied bilaterally, for a total of 12 minutes with two minutes per muscle group (calves, hamstrings, and quadriceps). FR trials followed the same protocol. Run time(sec) and lactate values(mmol) were recorded for all trials. RESULTS: Statistical analysis by ANOVA (P<.05) revealed no significant difference in run performance time among all conditions. Mean pre-intervention times were: 245 ± 24, 244 ± 23, and 246 ± 24 and post-invention times were: 246 ± 22, 245 ± 23 sec, and 247 ± 23 for NM, FR, and PM respectively. Blood lactate significantly decreased from post first run: 11.2 ± 2.3, 11.7 ± 2.3, and 10.7 ± 2.0 to the pre-second run with values of 5.1 ± 2.1 mmol, 5.5 ± 2.2mmol, and 5.1 ± 1.7 mmol for NM, FR, and PM respectively, with no significant difference among conditions. CONCLUSION: Although subjects reported enhanced recovery with the interventions, the results of this study suggest that percussive and non-percussive massage do not affect performance time in a 1000m run nor do they alter the time course for blood lactate attenuation. Supported, in part by a grant from Theragun®.

RESPOnSE-A Framework for Enforcing Risk-Aware Security Policies in Constrained Dynamic Environments.

The enforcement of fine-grained access control policies in constrained dynamic networks can become a challenging task. The inherit constraints present in those networks, which result from the limitations of the edge devices in terms of power, computational capacity and storage, require an effective and efficient access control mechanism to be in place to provide suitable monitoring and control of actions and regulate the access over the resources. In this article, we present RESPOnSE, a framework for the specification and enforcement of security policies within such environments, where the computational burden is transferred to high-tier nodes, while low-tier nodes apply risk-aware policy enforcement. RESPOnSE builds on a combination of two widely used access control models, Attribute-Based Access Control and Role-Based Access Control, exploiting the benefits each one provides. Moreover, the proposed mechanism is founded on a compensatory multicriteria decision-making algorithm, based on the calculation of the Euclidean distance between the run-time values of the attributes present in the security policy and their ideal values, as those are specified within the established policy rules.

Integrating Provenance Capture and UML With UML2PROV: Principles and Experience

In response to the increasing calls for algorithmic accountability, UML2PROV is a novel approach to address the existing gap between application design, where models are described by UML diagrams, and provenance design, where generated provenance is meant to describe an application's flows of data, processes and responsibility, enabling greater accountability of this application. The originality of UML2PROV is that designers are allowed to follow their preferred software engineering methodology to create the UML Diagrams for their application, while UML2PROV takes the UML diagrams as a starting point to automatically generate: (1) the design of the provenance to be generated (expressed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PROV templates</i> ); and (2) the software library for collecting runtime values of interest (encoded as variable-value associations known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bindings</i> ), which can be deployed in the application without developer intervention. At runtime, the PROV templates combined with the bindings are used to generate high-quality provenance suitable for subsequent consumption. UML2PROV is rigorously defined by an extensive set of 17 patterns mapping UML diagrams to provenance templates, and is accompanied by a reference implementation based on Model Driven Development techniques. A systematic evaluation of UML2PROV uses quantitative data and qualitative arguments to show the benefits and trade-offs of applying UML2PROV for software engineers seeking to make applications provenance-aware. In particular, as the UML design drives both the design and capture of provenance, we discuss how the levels of detail in UML designs affect aspects such as provenance design generation, application instrumentation, provenance capability maintenance, storage and run-time overhead, and quality of the generated provenance. Some key lessons are learned such as: starting from a non-tailored UML design leads to the capture of more provenance than required to satisfy provenance requirements and therefore, increases the overhead unnecessarily; alternatively, if the UML design is tailored to focus on addressing provenance requirements, only relevant provenance gets to be collected, resulting in lower overheads.

DRM-SLAM: Towards dense reconstruction of monocular SLAM with scene depth fusion

Monocular visual SLAM methods can accurately track the camera pose and infer the scene structure by building sparse correspondence between two/multiple views of the scene. However, the reconstructed 3D maps of these methods are extremely sparse. On the other hand, deep learning is widely used to predict dense depth maps from single-view color images, but the results are subject to blurry depth boundaries, which severely deform the structure of 3D scene. Therefore, this paper proposes a dense reconstruction method under the monocular SLAM framework (DRM-SLAM), in which a novel scene depth fusion scheme is designed to fully utilize both the sparse depth samples from monocular SLAM and predicted dense depth maps from convolutional neural network (CNN). In the scheme, a CNN architecture is carefully designed for robust depth estimation. Besides, our approach also accounts for the problem of scale ambiguity existing in the monocular SLAM. Extensive experiments on benchmark datasets and our captured dataset demonstrate the accuracy and robustness of the proposed DRM-SLAM. The evaluation of runtime and adaptability under challenging environments also verify the practicability of our method.

A Lightweight On-Device Detection Method for Android Malware

Android malware poses severe threats to users, hence raising an urgent demand for malware detection. In-cloud Android malware detection often suffers privacy leakage and communication overheads. Therefore, this article focuses on on-device Android malware detection. At present, on-device malware detectors are usually trained on servers and then transplanted to mobile devices (e.g., smartphones). In practice, on-device training is particularly important due to the demand for offline updates. Because mobile devices are limited in resource, however, on-device training is hard to implement, especially for those high-complexity malware detectors. To overcome this challenge, we design a lightweight on-device Android malware detector, based on the recently proposed broad learning method. Our detector mainly uses one-shot computation for model training. Hence it can be fully or incrementally trained directly on mobile devices. As far as detection accuracy is concerned, our detector outperforms the shallow learning-based models, including support vector machine (SVM) and AdaBoost, and approaches the deep learning-based models multilayer perceptron (MLP) and convolutional neural network (CNN). Moreover, our detector is more robust to adversarial examples than the existing detectors, and its robustness can be further improved through on-device model retraining. Finally, its advantages are confirmed by extensive experiments, and its practicality is demonstrated through runtime evaluation on smartphones.

A hybrid carbon policy inventory model with emission source-based green investments

The present study analyzes a production-inventory system with hybrid carbon regulation policy. This hybrid carbon policy is a combination of carbon tax and cap-and-trade policies. It considers a single item that can be produced in different qualities. Production cost, setup cost, amount of emissions and the demand rate depend on the quality. The demand rate for each quality is price sensitive. Emissions occur from three sources—setup, production process and stock holding. The firm can invest on green technologies in each emission source separately to reduce emissions. This model considers profit maximization policy. The managerial problem is to select the profit-maximizing quality for production, and to find the optimum values of the production run time, green investments and the selling price. An algorithm is provided to solve the model. The model is illustrated by a numerical example. Sensitivity analysis is also performed.

FlowSpec: A declarative specification language for intra-procedural flow-Sensitive data-flow analysis

Abstract Data-flow analysis is the static analysis of programs to estimate their approximate run-time behavior or approximate intermediate run-time values. It is an integral part of modern language specifications and compilers. In the specification of static semantics of programming languages, the concept of data-flow allows the description of well-formedness such as definite assignment of a local variable before its first use. In the implementation of compiler back-ends, data-flow analyses inform optimizations. Data-flow analysis has an established theoretical foundation. What lags behind is implementations of data-flow analysis in compilers, which are usually ad-hoc. This makes such implementations difficult to extend and maintain. In previous work researchers have proposed higher-level formalisms suitable for whole-program analysis in a separate tool, incremental analysis within editors, or bound to a specific intermediate representation. In this paper, we present FlowSpec , an executable formalism for specification of data-flow analysis. FlowSpec is a domain-specific language that enables direct and concise specification of data-flow analysis for programming languages, designed to express flow-sensitive, intra-procedural analyses. We define the formal semantics of FlowSpec in terms of monotone frameworks. We describe the design of FlowSpec using examples of standard analyses. We also include a description of our implementation of FlowSpec . In a case study we evaluate FlowSpec with the static analyses for Green-Marl , a domain-specific programming language for graph analytics.

A path to DOT: formalizing fully path-dependent types

The Dependent Object Types (DOT) calculus aims to formalize the Scala programming language with a focus on path-dependent types — types such as x . a 1 … a n . T that depend on the runtime value of a path x . a 1 … a n to an object. Unfortunately, existing formulations of DOT can model only types of the form x . A which depend on variables rather than general paths. This restriction makes it impossible to model nested module dependencies. Nesting small components inside larger ones is a necessary ingredient of a modular, scalable language. DOT’s variable restriction thus undermines its ability to fully formalize a variety of programming-language features including Scala’s module system, family polymorphism, and covariant specialization. This paper presents the pDOT calculus, which generalizes DOT to support types that depend on paths of arbitrary length, as well as singleton types to track path equality. We show that naive approaches to add paths to DOT make it inherently unsound, and present necessary conditions for such a calculus to be sound. We discuss the key changes necessary to adapt the techniques of the DOT soundness proofs so that they can be applied to pDOT. Our paper comes with a Coq-mechanized type-safety proof of pDOT. With support for paths of arbitrary length, pDOT can realize DOT’s full potential for formalizing Scala-like calculi.