Input Program Research Articles

Automatic and efficient variability-aware lifting of functional programs

A software analysis is a computer program that takes some representation of a software product as input and produces some useful information about that product as output. A software product line encompasses many software product variants, and thus existing analyses can be applied to each of the product variations individually, but not to the entire product line as a whole. Enumerating all product variants and analyzing them one by one is usually intractable due to the combinatorial explosion of the number of product variants with respect to product line features. Several software analyses (e.g., type checkers, model checkers, data flow analyses) have been redesigned/re-implemented to support variability. This usually requires a lot of time and effort, and the variability-aware version of the analysis might have new errors/bugs that do not exist in the original one. Given an analysis program written in a functional language based on PCF, in this paper we present two approaches to transforming (lifting) it into a semantically equivalent variability-aware analysis. A light-weight approach (referred to as shallow lifting ) wraps the analysis program into a variability-aware version, exploring all combinations of its input arguments. Deep lifting, on the other hand, is a program rewriting mechanism where the syntactic constructs of the input program are rewritten into their variability-aware counterparts. Compositionally this results in an efficient program semantically equivalent to the input program, modulo variability. We present the correctness criteria for functional program lifting, together with correctness proof sketches of shallow lifting. We evaluate our approach on a set of program analyses applied to the BusyBox C-language product line.

Compiling symbolic execution with staging and algebraic effects

Building effective symbolic execution engines poses challenges in multiple dimensions: an engine must correctly model the program semantics, provide flexibility in symbolic execution strategies, and execute them efficiently. This paper proposes a principled approach to building correct, flexible, and efficient symbolic execution engines, directly rooted in the semantics of the underlying language in terms of a high-level definitional interpreter. The definitional interpreter induces algebraic effects to abstract over semantic variants of symbolic execution, e.g., collecting path conditions as a state effect and path exploration as a nondeterminism effect. Different handlers of these effects give rise to different symbolic execution strategies, making execution strategies orthogonal to the symbolic execution semantics, thus improving flexibility. Furthermore, by annotating the symbolic definitional interpreter with binding-times and specializing it to the input program via the first Futamura projection, we obtain a "symbolic compiler", generating efficient instrumented code having the symbolic execution semantics. Our work reconciles the interpretation- and instrumentation-based approaches to building symbolic execution engines in a uniform framework. We illustrate our approach on a simple imperative language step-by-step and then scale up to a significant subset of LLVM IR. We also show effect handlers for common path selection strategies. Evaluating our prototype's performance shows speedups of 10~30x over the unstaged counterpart, and ~2x over KLEE, a state-of-the-art symbolic interpreter for LLVM IR.

Field-programmable biological circuits and configurable (bio)logic blocks for distributed biological computing

Synthetic biology applications often require engineered computing structures, which can be programmed to process the information in a given way. However, programming of these structures usually requires significant amount of trial-and-error genetic engineering. This process is to some degree analogous to the design of application-specific integrated circuits (ASIC) in the domain of digital electronic circuits, which often require complex and time-consuming workflows to obtain a desired response.We describe a design of programmable biological circuits that can be configured without additional genetic engineering. Their configuration can be changed in vivo, i.e. during the execution of their biological program, simply with an introduction of programming inputs. These, e.g., increase the degradation rates of selected proteins that store the current configuration of the circuit. Programming can be thus performed in the field as in the case of field-programmable gate array (FPGA) circuits, which present an attractive alternative of ASICs in digital electronics.We describe a basic programmable unit, which we denote configurable (bio)logical block (CBLB) inspired by the architecture of configurable logic blocks (CLBs), basic functional units within the FPGA circuits. The design of a CBLB is based on distributed cellular computing modules, which makes its biological implementation easier to achieve. We establish a computational model of a CBLB and analyse its response with a given set of biologically feasible parameter values. Furthermore, we show that the proposed CBLB design exhibits correct behaviour for a vast range of kinetic parameter values, different population ratios, and as well preserves this response in stochastic simulations.

An error-propagation aware method to reduce the software mutation cost using genetic algorithm

PurposeThe purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40% of injected faults (mutants) in the source code are effect-less (equivalent). Equivalent mutants are one of the major costs of mutation testing and the identification of equivalent and effect-less mutants has been known as an undecidable problem.Design/methodology/approachIn a program with n branch instructions (if instruction) there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Given the role and impact of data in a program, some of data and codes propagates the injected mutants more likely to the output of the program. In this study, firstly the error-propagation rate of the program data is quantified using static analysis of the program control-flow graph. Then, the most error-propagating test paths are identified by the proposed heuristic algorithm (Genetic Algorithm [GA]). Data and codes with higher error-propagation rate are only considered as the strategic locations for the mutation testing.FindingsIn order to evaluate the proposed method, an extensive series of mutation testing experiments have been conducted on a set of traditional benchmark programs using MuJava tool set. The results depict that the proposed method reduces the number of mutants about 24%. Also, in the corresponding experiments, the mutation score is increased about 5.6%. The success rate of the GA in finding the most error-propagating paths of the input programs is 99%. On average, only 7.46% of generated mutants by the proposed method are equivalent. Indeed, 92.54% of generated mutants are non-equivalent.Originality/valueThe main contribution of this study is as follows: Proposing a set of equations to measure the error-propagation rate of each data, basic-block and execution path of a program. Proposing a genetic algorithm to identify a most error-propagating path of program as locations of mutations. Developing an efficient mutation-testing framework that mutates only the strategic locations of a program identified by the proposed genetic algorithms. Reducing the time and cost of mutation testing by reducing the equivalent mutants.

What We Do Not Know About the Costs of Immunization Programs in Low- and Middle-Income Countries

Evidence on immunization economics is a critical input for country immunization programs, particularly in low- and middle-income countries (LMICs) with government- and donor-funded vaccination programs. This evidence allows programs to budget for current services, plan for new vaccine introduction, and evaluate the efficiency of service delivery strategies. Evidence on immunization costs is also useful for international funders when making resource allocation decisions.1-4 A large number of studies have reported on the effectiveness and cost-effectiveness of childhood immunization for reducing the impact of vaccine-preventable diseases in LMICs.

Maskara: Compilation of a Masking Countermeasure With Optimized Polynomial Interpolation

Side-channel attacks are amongst the major threats for embedded systems and IoT devices. Masking is one of the most used countermeasure against such attacks, but its application remains a difficult process. We propose a target-independent approach for applying a first-order Boolean masking countermeasure during compilation, on the static single assignment (SSA) form. Contrary to the state-of-the art automated approaches that require to simplify the control flow of the input program, our approach supports regular control-flow program structures. Moreover, our compiler is the first to automatically mask table lookups using a polynomial interpolation approach. We also present new optimizations to speedup the evaluation of polynomials: we reduce the number of terms of the polynomial, and we accelerate finite-field multiplication. We show that our approach is faster than the standard masked table approach with mask refresh after each access, with speedups up to ×2.4 in our experiments. Finally, using a formal verification approach, we show that the compiled machine code is secure, i.e., that all intermediate computations are statistically independent of the secrets.

Test data generation using genetic programming

Typically, search-based test data generation methods search on a population of program input values. Program input values can be regarded as solutions to underlying path constraints over program input parameters. One way to discover these path constraints is to use the symbolic execution method. Search-based methods attempt to find input values which are solutions to these path constraints, without knowing the actual constraints. In this paper, we show that we can search for the underlying path constraints using search-based methods, without resorting to symbolic execution. Trying to discover the exact or a good enough approximation of the underlying constraints may lead to a more targeted search, compared to directly searching for program input values. Besides, the construction of approximate constraints by searching may help to avoid some problems of symbolic execution. The proposed method uses genetic programming for learning constraints on program input parameters. To evaluate the performance of the proposed approach, a series of experiments have been conducted on a number of different benchmark programs. For 91.8% of benchmark programs, the proposed method achieved the best efficiency among the competitive algorithms. The results show that, if constraint solving can be provided for some or all parameter types of the methods of programs under test, our approach can improve the efficiency and effectiveness of search-based test data generation.

Exploring Impact of Profile Data on Code Quality in the HotSpot JVM

Managed language virtual machines (VM) rely on dynamic or just-in-time (JIT) compilation to generate optimized native code at run-time to deliver high execution performance. Many VMs and JIT compilers collect profile data at run-time to enable profile-guided optimizations (PGO) that customize the generated native code to different program inputs. PGOs are generally considered integral for VMs to produce high-quality and performant native code. In this work, we study and quantify the performance benefits of PGOs, understand the importance of profiling data quantity and quality/accuracy to effectively guide PGOs, and assess the impact of individual PGOs on VM performance. The insights obtained from this work can be used to understand the current state of PGOs, develop strategies to more efficiently balance the cost and exploit the potential of PGOs, and explore the implications of and challenges for the alternative ahead-of-time (AOT) compilation model used by VMs.

Autogenous welding of copper pipe using orbital TIG welding technique for application as high vacuum boundary parts of nuclear fusion devices

This research work describes the development of autogenous orbital gas tungsten arc welding for oxygen-free copper pipe. Neutral beam system for fusion devices consists of the different water-cooled component, which is designed and fabricated from pure copper to have the excellent thermal and electrical conductivity from the functionality point of view. Due to their application in a high vacuum along with the nuclear environment, a weld joint of full penetration configuration with a 100% volumetric examination is a mandatory requirement. Welding of pure copper has always been a challenging task because of its high conductivity and reflectivity, even with the use of high energy beam welding techniques (e.g., Electron beam welding and laser beam welding). Further, to compensate the heat dissipation, if the higher arc energy is provided to the base material, it results in the loss of strength, and therefore the selection of welding process and determining the parameters plays a crucial role for pressure withstanding welded connections. Though electron beam welding is a conventionally practiced process for welding of pure copper, the need for massive infrastructure limits its applicability to the large components. The present aim to have an understanding of the applicability of pulsed TIG process, in an autogenous mode for welding of pure copper material is presented. During this research work range of process parameters were used to assess the quality and strength of the joint. The range of process parameters was used to retain the mechanical properties of base material to the extent possible level. While doing, so weldability assessment was made for the shielding gas, welding program (pulse current, background current, and their duty cycles), joint geometry, arc length, and heat inputs. The mechanical and microstructural properties have been evaluated for the range of these parameters. The results have also been compared with the properties achieved by the electron beam welding process to understand what best researchers can achieve with the conventional welding process compared to high energy beam welding.

Reproductivity And Validity Analysis Of Badminton Player Cognitive Performance Using Badminton Reaction Inhibition Test (BRIT)

The purpose of this study was to analyze the reproducibility and validity of the Badminton Reaction Inhibition Test (BRIT) or the Badminton Reaction Barrier Test. BRIT measures four components: general-domain reaction time, badminton-specific reaction time, general-domain inhibition control, and badminton-specific inhibition control. Fifteen male national badminton athletes and nine non-national badminton players in Region 3 Cirebon participated in this study. Five participants were retested within three weeks on a specific badminton component. Reproducibility was acceptable for badminton specific reaction times (ICC = 0.626, CV = 6%) and for badminton specific inhibition controls (ICC = 0.317, CV = 13%). The validity of a good construct was shown in the specific poor reaction time that differentiated between national level athletes and non-national athletes (F = 6.650, p 0.05). Concurrent validity for general-domain reaction times was good, as it was associated with the national rankings for national level athletes (? pro = 0.70, p 0.05). In conclusion, the reproducibility and validity of the control inhibition assessment are not confirmed, however, BRIT appears to be a valid and reproducible measure of reaction time in badminton players. Reaction times as measured by BRIT can provide input for training programs aimed at improving badminton player performance.

Competency improvement through internship: An evaluation of corporate social responsibility program in vocational school

The high unemployment rate in Indonesia derived from vocational school graduates could be alleviated through internship as a part of Corporate Social Responsibility program that deployed by company together with school to equip its students or graduates with set of competences, therefore they will be competent and able to compete in job market, especially in the era of disruptive technologies. This applied research of evaluation using qualitative approach aimed to evaluate the input and process of the corporate social responsibility program in vocational internship conducted by an oil and gas manufacturing company in Indonesia. The results of this revealed some inputs that were the source of program success and processes that determined the quality of the program. Aspects of input generally met the ideal criteria such as competent program staff, participant characteristic reflecting program objectives, flexible and synchronized curriculum, adequate facility, and available procedure. Aspects of the process complied with regulations and procedures as well. Both aspects resulted the achievement of outcome that exceeded expectation such as competence improvement, cost saving, awards and provide information to be replicated by other institutions.

PeptideWitch-A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data.

PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for ‘control’ and ‘treated’ samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.

Modeling Crosstie-Ballast Load Distribution in a Railroad Trackbed using a Linear-Elastic Analysis

This paper details a linear-elastic approach to modeling crosstie-ballast interfacial load distribution in a railroad timber crosstie trackbed using RISA 3D, a finite element modeling software. RISA results closely mirrored experimental findings with some slight deviation as discussed in the paper. The modeling methodology outlined here advances previous trackbed research at the University of Kentucky, U.S., and could be used in future trackbed finite element analyses for design or research in load path distribution and load magnitude predictability. This paper details the approach used to model a timber crosstie trackbed superstructure, including initial troubleshooting stages, and explains the derivation of program input variables. A sensitivity analysis performed on the main model determined the workable extent of linearity. The analysis also examined the effect of a poor/muddy trackbed support loading condition. The paper concludes with potential recommendations in relation to the work.

BIOSPECKLE LASER TO MONITOR THE GERMINATION OF ANGICO-VERMELHO SEEDS

Seed viability is an important parameter in its germination and in the establishment of seedlings, as well as for maintaining a seed bank, mainly for forest seeds, since they are responsible for the maintenance and perpetuation of plant communities and are a basic input for ecosystem recovery and conservation programs. To assess germination, traditional techniques use hundreds of seeds in a destructive manner, obtaining results after many days and can express the analyst’s subjectivity. The use of the Biospeckle Laser to monitor seed germination allows the analysis to be objective, fast, non-invasive, and automated analysis. This work proposed to measure the viability of Angico-Vermelho seeds during the first 72 hours of germination using the Biospeckle Laser with numerical analysis. The results obtained in the first experiment allowed monitoring of biological activity throughout the 72 hours of germination, showing high cell activity in the embryo region already in the first 4 hours of germination. In the second experiment, the results showed greater biological activity in the mature seeds when compared to immature ones during the first 4 hours of germination, also detailing the biological activity in different areas of the Angico-Vermelho seed, reporting a greater biological activity in the embryonic axis region when compared to the endosperm. In conclusion, the Biospeckle Laser technique with numerical AVD analysis can be used to analyze the germination of of A. colubrina already in the first 4 hours of germination, dispensing with traditional methods. This technique allows to study the seed germination in different regions and at different times.

The Challenges and Opportunities; CIPP Model for Evaluation for TEFL Following the COVID-19 Pandemic

This study are focused in : (1) how the context of the Online teaching learning process program on Covid-19 outbreak. (2) how the input of the Online teaching learning process program on Covid-19 to the success program, (3) how far the challenge and the opportunities of the Online teaching learning process program on Covid-19 pandemic.
 The objects of this study were: the English teacher in government’ schools and private school of junior high school and senior high school, and the students. The data were obtained from interview, classroom observation, and document analysis. Those data were analyzed by using Context, Input, Process, Product (CIPP) Model by Kellaghan and Stufflebeam (2003).
 The findings revealed some facts. First, in the context evaluation, it can be concluded that the online teaching and learning program are relevant and formulated in order to meet the students’ needs on this pandemic. Second, in term of input evaluation, the teacher did not fulfill the requirements of a qualified teacher yet. Therefore, this pandemic has become a challenge for educators especially for English teachers and students in higher education level. It impacted classrooms to be hardly. Here, the researcher advice the government to conduct a training and workshop in order to improve the quality and the professionalism.

Identifying and characterizing translationally modulated molecular crystal structures.

Most structural (i.e. displacive) modulations make molecules independent that had been related by translation in a phase having a smaller or centered unit cell. In the modulated structure the independent molecules are differentiated by small translations, rotations, and/or conformational changes but an approximate translational relationship is normally retained. A program has been written to identify such pseudotranslations because they can be difficult to find by eye and because they combine with each other and with lattice translations in ways that can be confusing. To characterize the pseudotranslations the program calculates their fractional translational, orientational, and conformational components as well as several quality indicators. While many pseudotranslations are obvious, others are borderline; setting tolerances for identifying a pseudotranslation proved difficult. Defaults were chosen to reproduce experience-based judgment but they can be varied in the program input. The program was run for organic and for metallo-organic structures with R ≤ 0.075 in the 2019 release of the Cambridge Structural Database. The frequency of pseudotranslations increases with Z' and is approximately 50% for Z' > 4. Some structures were found in which an identified pseudotranslation cannot correspond to a modulation. These include structures in which some but not all of the molecules are related by pseudotranslations and structures in which pseudotranslations in different parts of the unit cell have different directions.

Mathematical Modeling to Predict the Geometrical Properties of Float Stitches in Single Jersey Knitted Fabrics

This paper presents a novel mathematical model of float stitches in single Jersey knitted fabrics. The mathematical model has been used to deduce the fabric geometrical relationships that can be useful for predicting the properties of the fabric. Using AutoLISP and AutoCAD software, a program has been designed to simulate the surface appearance of knitted fabrics. The program inputs and outputs are also displayed with each type of float stitches. The proposed program and mathematical model demonstrated their effectiveness in using a wide range of yarn count, number of wales and courses, and predicting the different geometrical properties.

Using a Program Based on Computerized Integration of Flooding and Enhancement Input for the Acquisition of Vocabulary Items of First Year Preparatory School Students

The aim of this study was to investigate the effect of a computerized integration of flooding and enhancement input program on the acquisition of vocabulary items of first year preparatory school students. The participants were sixty students. They were randomly assigned into a treatment group and a non-treatment group (30 students each). The instruments of the study included: a test in vocabulary items. The treatment group was taught using the computerized integration of flooding and enhancement input program while the non - treatment group was taught following the plan of the Ministry of Education in Egypt. The participants were pre and post tested. Results revealed that the computerized integration of flooding and enhancement input program had positive results. Suggestions for further research were presented.

Adapting guided inquiry learning worksheets for emergency remote learning

PurposeProcess-oriented guided inquiry learning (POGIL) is a series of learning activities building on student prior knowledge guiding them to construct their own understanding of new concepts in collaborative roles. This paper aims to illustrate how POGIL worksheets can be adapted for low bandwidth and low-computing environments to accommodate the largest swathe of learners in higher education, as was the case during the switch to emergency remote learning in 2020.Design/methodology/approachThe POGIL worksheets in this paper scaffold the discovery of new concepts while providing sample computer program output, guiding students to make predictions about the connection between program input and program output. Answers are provided to these questions after completion so that students may check their understanding or look to the answers as worked examples. These POGIL worksheets were used for the past two years in an in-person classroom situation with minimal computing resources, replacing 4/5 of a classroom lecture doing POGILs collaboratively. In the midst of emergency remote learning, these worksheets were adapted to complement asynchronous lecture videos, and also serve as lecture replacement as needed.FindingsThis paper discusses an approach to adapting POGIL worksheets for introduction to computer science for students who may not have the necessary digital tools (programing software, bandwidth for streaming video, etc.). While the context for this paper is computer science, POGIL has a deep history in chemistry education and other natural sciences, suggesting an approach that may be adapted for situations where hands-on laboratory experiments may not be possible.Originality/valueCS-POGIL has many materials available for computer science, but this paper discusses 23 new worksheets and how to adapt them to the novel situation of emergency remote teaching.

Mapping and Estimating Weeds in Cotton Using Unmanned Aerial Systems-Borne Imagery

In recent years, Unmanned Aerial Systems (UAS) have emerged as an innovative technology to provide spatio-temporal information about weed species in crop fields. Such information is a critical input for any site-specific weed management program. A multi-rotor UAS (Phantom 4) equipped with an RGB sensor was used to collect imagery in three bands (Red, Green, and Blue; 0.8 cm/pixel resolution) with the objectives of (a) mapping weeds in cotton and (b) determining the relationship between image-based weed coverage and ground-based weed densities. For weed mapping, three different weed density levels (high, medium, and low) were established for a mix of different weed species, with three replications. To determine weed densities through ground truthing, five quadrats (1 m × 1 m) were laid out in each plot. The aerial imageries were preprocessed and subjected to Hough transformation to delineate cotton rows. Following the separation of inter-row vegetation from crop rows, a multi-level classification coupled with machine learning algorithms were used to distinguish intra-row weeds from cotton. Overall, accuracy levels of 89.16%, 85.83%, and 83.33% and kappa values of 0.84, 0.79, and 0.75 were achieved for detecting weed occurrence in high, medium, and low density plots, respectively. Further, ground-truthing based overall weed density values were fairly correlated (r2 = 0.80) with image-based weed coverage assessments. Among the specific weed species evaluated, Palmer amaranth (Amaranthus palmeri S. Watson) showed the highest correlation (r2 = 0.91) followed by red sprangletop (Leptochloa mucronata Michx) (r2 = 0.88). The results highlight the utility of UAS-borne RGB imagery for weed mapping and density estimation in cotton for precision weed management.