Variable Names Research Articles

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.

Combinators as presheaves

Abstract Computer scientists, logicians and other researchers have recently paid renewed attention to the model of computation based on the logic of combinators. We develop a new formalization of the syntax of combinators employing the ‘generic figures’ approach to the categories of presheaves that provides an intuitive and easily interpreted diagrammatic syntax for combinators, one that eschews the need to label proper combinators with variable names. Furthermore, we show that this formal framework provides the possibility of representing choices of operand from among given alternatives at each level of a combinatory term, allowing for a more general type of combinatory expression, which we call a multi-combinator.

Minding the abstraction gap: approaches supporting implementation

AbstractRegardless of insights gained from building and analyzing dynamic models, the only strategies people can act on are those in their heads. The strategies people internalize are related to their perceived capacities to act—the verbs they believe they can do. If we want others to implement model‐informed policies, then we must connect model abstractions with new situated, concrete actions stakeholders can take. We can emphasize opportunities to act with SD representations, navigating levels of abstraction cleanly, identifying flows as verbs, and choosing variable names that signal who is acting. By drawing on social‐science theories as we offer our grammar of accumulations, activities, and relationships in the language of actions accessible to stakeholders, we help connect experiential understandings to richer, dynamic explanations people can internalize and so discover situated steps to implement policies informed by modeling. © 2024 The Author(s). System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Implementing a new Research Data Alliance recommendation, the I-ADOPT framework, for the naming of environmental variables of continental surfaces

To improve data usage in an interdisciplinary context, a clear understanding of the variables being measured is required for both humans and machines. In this paper, the I-ADOPT framework, which decomposes variable names into atomic elements, was tested within the context of continental surfaces and critical zone science, characterized by a large number and variety of observed environmental variables. We showed that the I-ADOPT framework can be used effectively to describe environmental variables with precision and that it was flexible enough to be used in the critical zone science context. Variable names can be documented in detail while allowing alignment with other ontologies or thesauri. We have identified difficulties in modeling complex variables, such as those monitoring fluxes between different environmental compartments and for variables monitoring ratios of physical quantities. We also showed that, for some variables, different decompositions were possible, which could make alignments with other ontologies and thesauri more difficult. The precision of variable names proved inadequate for data discovery services and a non-standard label (SimplifiedLabel) had to be defined for this purpose. In the context of open science and interdisciplinary research, the I-ADOPT framework has the potential to improve the interoperability of information systems and the use of data from various sources and disciplines.

Harmonizing data on correlates of sleep in children within and across neurodevelopmental disorders: lessons learned from an Ontario Brain Institute cross-program collaboration.

There is an increasing desire to study neurodevelopmental disorders (NDDs) together to understand commonalities to develop generic health promotion strategies and improve clinical treatment. Common data elements (CDEs) collected across studies involving children with NDDs afford an opportunity to answer clinically meaningful questions. We undertook a retrospective, secondary analysis of data pertaining to sleep in children with different NDDs collected through various research studies. The objective of this paper is to share lessons learned for data management, collation, and harmonization from a sleep study in children within and across NDDs from large, collaborative research networks in the Ontario Brain Institute (OBI). Three collaborative research networks contributed demographic data and data pertaining to sleep, internalizing symptoms, health-related quality of life, and severity of disorder for children with six different NDDs: autism spectrum disorder; attention deficit/hyperactivity disorder; obsessive compulsive disorder; intellectual disability; cerebral palsy; and epilepsy. Procedures for data harmonization, derivations, and merging were shared and examples pertaining to severity of disorder and sleep disturbances were described in detail. Important lessons emerged from data harmonizing procedures: prioritizing the collection of CDEs to ensure data completeness; ensuring unprocessed data are uploaded for harmonization in order to facilitate timely analytic procedures; the value of maintaining variable naming that is consistent with data dictionaries at time of project validation; and the value of regular meetings with the research networks to discuss and overcome challenges with data harmonization. Buy-in from all research networks involved at study inception and oversight from a centralized infrastructure (OBI) identified the importance of collaboration to collect CDEs and facilitate data harmonization to improve outcomes for children with NDDs.

On the use of statistical machine translation for suggesting variable names for decompiled code: The Pharo case

Adequately selecting variable names is a difficult activity for practitioners. In 2018, Jaffe et al. proposed the use of statistical machine translation (SMT) to suggest descriptive variable names for decompiled code. A large corpus of decompiled C code was used to train the SMT model. Our paper presents the results of a partial replication of Jaffe’s experiment. We apply the same technique and methodology to a dataset made of code written in the Pharo programming language. We selected Pharo since its syntax is simple – it fits on half of a postcard – and because the optimizations performed by the compiler are limited to method scope. Our results indicate that SMT may recover between 8.9% and 69.88% of the variable names depending on the training set. Our replication concludes that: (i) the accuracy depends on the code similarity between the training and testing sets; (ii) the simplicity of the Pharo syntax and the satisfactory decompiled code alignment have a positive impact on predicting variable names; and (iii) a relatively small code corpus is sufficient to train the SMT model, which shows the applicability of the approach to less popular programming languages. Additionally, to assess SMT’s potential in improving original variable names, ten Pharo developers reviewed 400 SMT name suggestions, with four reviews per variable. Only 15 suggestions (3.75%) were unanimously viewed as improvements, while 45 (11.25%) were perceived as improvements by at least two reviewers, highlighting SMT’s limitations in providing suitable alternatives.

There Are Infinite Ways to Formulate Code: How to Mitigate the Resulting Problems for Better Software Vulnerability Detection

Given a set of software programs, each being labeled either as vulnerable or benign, deep learning technology can be used to automatically build a software vulnerability detector. A challenge in this context is that there are countless equivalent ways to implement a particular functionality in a program. For instance, the naming of variables is often a matter of the personal style of programmers, and thus, the detection of vulnerability patterns in programs is made difficult. Current deep learning approaches to software vulnerability detection rely on the raw text of a program and exploit general natural language processing capabilities to address the problem of dealing with different naming schemes in instances of vulnerability patterns. Relying on natural language processing, and learning how to reveal variable reference structures from the raw text, is often too high a burden, however. Thus, approaches based on deep learning still exhibit problems generating a detector with decent generalization properties due to the naming or, more generally formulated, the vocabulary explosion problem. In this work, we propose techniques to mitigate this problem by making the referential structure of variable references explicit in input representations for deep learning approaches. Evaluation results show that deep learning models based on techniques presented in this article outperform raw text approaches for vulnerability detection. In addition, the new techniques also induce a very small main memory footprint. The efficiency gain of memory usage can be up to four orders of magnitude compared to existing methods as our experiments indicate.

Frequency of major congenital malformations in neonates born in three tertiary care hospitals of Pakistan.

Objective: To assess the frequency of major congenital anomalies in newborns delivered in three different hospitals of Pakistan. Study Design: Cross Sectional study. Setting: Department of Pediatrics Hameed Latif Teaching Hospital, Lahore, Ittefaq Hospital Lahore and HITEC-IMS Taxilla. Period: Jan 2023 to November 2023. Methods: 205 neonates who met the inclusion criteria were included in the study from labour room of Hameed Latif Teaching Hospital Lahore, Ittefaq Hospital Lahore and HITEC-IMS Taxilla. Informed consent was obtained from parents. Demographic variables (name of mother, gender, birthweight and gestational age at birth) were recorded. Then neonates were admitted in NICU and evaluated for presence of congenital malformations including cardiovascular system defects, limbs anomalies, genitourinary system, central nervous system and chromosomal anomalies. All data was entered in specially designed performa. Results: We found that overall gestational age was greater than 32 weeks with mean age of 37.190±2.00 weeks. Mean birth weight was 2.938±0.31 Kg. Frequency and %age of patients according to gender was 60% males and 40% females. Major congenital malformation was seen in 2.9% patients. Among infants with major congenital malformations, cardiovascular system defects were seen in 33.3% infants, limb anomalies 16.7%, genitourinary system 16.7% and central nervous system defects were 33.3%. Parents of 67.7% of cases were cousins. It was observed that only 32.3% of the mothers had taken the recommended daily dose of folic acid in antenatal period. Conclusion: Central nervous system and cardiovascular system defects were the most prominent anomalies detected. Prenatal diagnosis may be helpful in decreasing mortality by offering early termination.

Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning

Large Language Models (LLM) exhibit zero-shot mathematical reasoning capacity as a behavior emergent with scale, commonly manifesting as chain-of-thoughts (CoT) reasoning. However, multiple empirical findings suggest that this prowess is exclusive to LLMs that have exorbitant sizes (beyond 50 billion parameters). Meanwhile, educational neuroscientists suggest that symbolic algebraic manipulation be introduced around the same time as arithmetic word problems so as to modularize language-to-formulation, symbolic manipulation of the formulation, and endgame arithmetic. In this paper, we start with the hypothesis that much smaller LMs, which are weak at multi-step reasoning, can achieve reasonable arithmetic reasoning if arithmetic word problems are posed as a formalize-then-solve task. In our architecture, which we call SyReLM, the LM serves the role of a translator to map natural language arithmetic questions into a formal language (FL) description. A symbolic solver then evaluates the FL expression to obtain the answer. A small frozen LM, equipped with an efficient low-rank adapter, is capable of generating FL expressions that incorporate natural language descriptions of the arithmetic problem (e.g., variable names and their purposes, formal expressions combining variables, etc.). We adopt policy-gradient reinforcement learning to train the adapted LM, informed by the non-differentiable symbolic solver. This marks a sharp departure from the recent development in tool-augmented LLMs, in which the external tools (e.g., calculator, Web search, etc.) are essentially detached from the learning phase of the LM. SyReLM shows massive improvements (e.g., +30.65 absolute point improvement in accuracy on the SVAMP dataset using GPT-J 6B model) over base LMs, while keeping our testbed easy to diagnose and interpret, and within the reach of most researchers.

Exploring ChatGPT’s code refactoring capabilities: An empirical study

ChatGPT has shown great potential in the field of software engineering with its ability to generate code. Yet, ChatGPT’s ability to interpret code has been deemed unreliable and faulty, which causes concern for the platform’s ability to properly refactor code. To confront this concern, we carried out a study to assess ChatGPT’s abilities and limitations in refactoring code. We divided the study into three parts: if ChatGPT can refactor the code, if the refactored code preserves the behavior of the original code segments, and if ChatGPT is capable of providing documentation for the refactored code to provide insights into intent, instructions, and impact. We focused our research specifically on eight quality attributes to use when prompting ChatGPT to refactor our dataset of 40 Java code segments. After collecting the refactored code segments from ChatGPT, as well as data on whether the behavior was preserved, we ran the refactored code through PMD, a source code analyzer, to find programming flaws. We also tested ChatGPT’s accuracy in generating documentation for the refactored code and analyzed the difference between the results of each quality attribute. We conclude that ChatGPT can provide many useful refactoring changes that can improve the code quality which is crucial. ChatGPT offered improved versions of the provided code segments 39 out of 40 times even if it is as simple as suggesting clearer names for variables or better formatting. ChatGPT was able to recommend numerous options ranging from minor changes such as renaming methods and variables to major changes such as modifying the data structure. ChatGPT’s strengths and accuracy were in suggesting minor changes because it had difficulty addressing and understanding complex errors and operations. Although most of the changes were minor, they made significant improvements because converting loops, simplifying calculations, and removing redundant statements have a crucial effect on runtime, memory, and readability. However, our results also indicate how ChatGPT can be unpredictable in its responses which threatens the reliability of ChatGPT. Asking ChatGPT the same prompt often yields different results, so some outputs were more accurate than others. This makes it difficult to fully access ChatGPT’s capabilities due to its variation and inconsistency. Due to ChatGPT’s limitations of its reliance on its data set, it lacks understanding of the broader context so it may occasionally make errors and suggest alternations that are neither applicable nor necessary. Overall, ChatGPT has proved to be a beneficial tool for programming as it is capable of providing advantageous suggestions, even if it is on a small scale. However, human programmers are still needed to oversee these changes and determine their significance. ChatGPT should be used as an aid to programmers since we cannot completely depend on it yet.

Determination of the radioanatomical factors related to the position of facial nerve in accessing jugular foramen and carotid artery in temporal bone

IntroductionPreservation of the facial nerve is of great importance in temporal bone surgeries. We intend to investigate the measurements of the radioanatomical factors related to the position of the facial nerve in accessing jugular foramen and internal carotid artery (ICA) in temporal bone of patients who were candidates for temporal high resolution computed tomography (HRCT) scan. MethodsIn this correlation cross-sectional study, samples were selected from patients referred to Amir Alam Hospital who were previously candidates for temporal HRCT. Radioanatomic factors were evaluated in three axial, coronal and sagittal views. Analyzes were performed using descriptive statistics, correlation analysis and factor analysis. ResultsA total of 173 samples were investigated. The most reliable radioanatomical factor based on coefficient of variation (CV) was the distance of the 7th nerve to the temporomandibular joint (TMJ) in the inferior to the cochlea in the sagittal view (variable name S2) (CV = 8.1%) and then the distance from the 7th nerve to the TMJ in the inferior section of the cochlea in the axial view (variable name AI3) (CV = 8.4%). Based on correlation analysis and then confirmatory factor analysis, three common latent factors were identified (overall R2 = 0.999). ConclusionThe results of this study can be used for two purposes. First, the direct use of the estimated measures in surgical operations, and the second is more advanced modeling to choose the approach in the surgical operation and how to implement that approach. For the first aim, the two factors AI3 and S2 were the most reliable radioanatomical factors in different people. For the second aim, the three-dimensional understanding of the obtained measurements and the further identification of the anatomical nature of the latent factors can help in choosing the approach in surgery.

Data management plan and REDCap mobile data capture for a multi-country Household Air Pollution Intervention Network (HAPIN) trial.

Household air pollution (HAP) is a leading environmental risk factor accounting for about 1.6 million premature deaths mainly in low- and middle-income countries (LMICs). However, no multicounty randomized controlled trials have assessed the effect of liquefied petroleum gas (LPG) stove intervention on HAP and maternal and child health outcomes. The Household Air Pollution Intervention Network (HAPIN) was the first to assess this by implementing a common protocol in four LMICs. This manuscript describes the implementation of the HAPIN data management protocol via Research Electronic Data Capture (REDCap) used to collect over 50 million data points in more than 4000 variables from 80 case report forms (CRFs). We recruited 800 pregnant women in each study country (Guatemala, India, Peru, and Rwanda) who used biomass fuels in their households. Households were randomly assigned to receive LPG stoves and 18 months of free LPG supply (intervention) or to continue using biomass fuels (control). Households were followed for 18 months and assessed for primary health outcomes: low birth weight, severe pneumonia, and stunting. The HAPIN Data Management Core (DMC) implemented identical REDCap projects for each study site using shared variable names and timelines in local languages. Field staff collected data offline using tablets on the REDCap Mobile Application. Utilizing the REDCap application allowed the HAPIN DMC to collect and store data securely, access data (near real-time), create reports, perform quality control, update questionnaires, and provide timely feedback to local data management teams. Additional REDCap functionalities (e.g. scheduling, data validation, and barcode scanning) supported the study. While the HAPIN trial experienced some challenges, REDCap effectively met HAPIN study goals, including quality data collection and timely reporting and analysis on this important global health trial, and supported more than 40 peer-reviewed scientific publications to date.

Deobfuscating JavaScript Code Using Character-Based Tokenization

The JavaScript code deployed goes through the process of minification, in which variables are renamed using single-character names and spaces are removed in order for the files to have a smaller size, thus loading faster. Because of this, the code becomes unintelligible, making it harder to be analyzed manually. Since JavaScript experts can under- stand it, machine learning approaches to deobfuscate the minified file are possible. Thus, we propose a technique that finds a fitting name for each obfuscated variable, which is both intuitive and meaningful based on the usage of that variable, based on a Sequence-to-Sequence model, which generates the name character by character to cover all the possible variable names. The proposed approach achieves an average exact name generation accuracy of 70.53%, outperforming the state-of-the-art by 12%. Keywords and phrases: JavaScript deobfuscation, variable name prediction, Deep Learning, Recurrent Neural Network, Abstract Syntax Tree.

A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Case Study.

In recent years, many researchers have focused on the use of legacy data, such as pooled analyses that collect and reanalyze data from multiple studies. However, the methodology for the integration of preexisting databases whose data were collected for different purposes has not been established. Previously, we developed a tool to efficiently generate Study Data Tabulation Model (SDTM) data from hypothetical clinical trial data using the Clinical Data Interchange Standards Consortium (CDISC) SDTM. This study aimed to design a practical model for integrating preexisting databases using the CDISC SDTM. Data integration was performed in three phases: (1) the confirmation of the variables, (2) SDTM mapping, and (3) the generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the data sets were converted to a vertical structure. In phase 2, the items derived from the SDTM format were set as mapping items. Three types of metadata (domain name, variable name, and test code), based on the CDISC SDTM, were embedded in the Research Electronic Data Capture (REDCap) field annotation. In phase 3, the data dictionary, including the SDTM metadata, was outputted in the Operational Data Model (ODM) format. Finally, the mapped SDTM data were generated using REDCap2SDTM version 2. SDTM data were generated as a comma-separated values file for each of the 7 domains defined in the metadata. A total of 17 items were commonly mapped to 3 databases. Because the SDTM data were set in each database correctly, we were able to integrate 3 independently preexisting databases into 1 database in the CDISC SDTM format. Our project suggests that the CDISC SDTM is useful for integrating multiple preexisting databases.

VSCode-Antimony: a source editor for building, analyzing, and translating antimony models.

Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface. However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not "model aware," they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features. We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of Systems Biology Markup Language (SBML) models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g. to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g. between Antimony and SBML). VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony.

Convert-Pheno: A software toolkit for the interconversion of standard data models for phenotypic data

Efficient sharing and integration of phenotypic data is crucial for advancing biomedical research and enhancing patient outcomes in precision medicine and public health. To achieve this, the health data community has developed standards to promote the harmonization of variable names and values. However, the use of diverse standards across different research centers can hinder progress. Here we present Convert-Pheno, an open-source software toolkit that enables the interconversion of common data models for phenotypic data such as Beacon v2 Models, CDISC-ODM, OMOP-CDM, Phenopackets v2, and REDCap. Along with the software, we have created a detailed documentation that includes information on deployment and installation.

Pervasive Micro Information Flow Tracking

Detection of advanced security attacks that exploit zero-day vulnerabilities or application-specific logic loopholes has been challenging due to the lack of attack signatures or substantial deviations in the overall system behavior. One has to zoom in to the affected code regions and look for local anomalies distinguishable from the benign workload to detect such attacks. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pervasive micro information flow tracking</i> (P <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</small> RMIT) that realizes variable-level online dynamic information flow tracking (DIFT) as a means to detect the attacks. The system uses hardware virtualization extension to monitor access to taint source variables and performs asynchronous code emulation to infer the local information flow. We demonstrate that the pervasive micro information flow can sufficiently capture the attacks and incurs only a small overhead. Given the program source code, the system can further enrich the semantics of micro information flow by embedding the variable names. We have integrated the system with machine learning algorithms to demonstrate the effectiveness of anomaly detection for zero-day attacks with pervasive micro information flow.

PENGARUH PELATIHAN KERJA DAN PENGEMBANGAN KARIR TERHADAP KINERJA KARYAWAN PT. JIN HEE INDONESIA, KABUPATEN TANGERANG

The purpose of this research is to determine how much influence the variable of work training (X1) and career development (X2) jointly on the employee performance PT. Jin Hee Indonesia, Kabupaten. Tangerang. The method used is the quantitative method and analyses the relationship between the independent variable with the dependent variable, the method used is measuring the Likert scale by taking 85 respondents. The study used statistical analysis with the SmartPLS 3.2.9 program. From the results of the study note the linear equation of the KK model = 0,609PEL + 0,327PK + ζ. partially the competence variable has a positive and significant effect with a path coefficient of 0,349 and a p-value below 0,5 or 5%. Partially the career development variable has a positive and significant effect with the value of the path coefficient of 0,514 and a p-value below 0,5 or 5%. And the work training variable (X1) and career development (X2) jointly affect the employee performance (Y) with a value of 763 which means 76,3% of the employee performance (Y) which can be explained by the variable names the work training (X1) and the career development variable (X2)

A review on statistical language and neural network based code completion

Code completion, also referred to as intellisense, is a prevalent feature of Integrated Development Environments (IDEs) and code editors. It aids developers by automatically recommending and inserting code segments, variable names, and method names, among other things. With the accelerated growth of the software industry and the process of digitalization in recent years, the demand for software engineers has reached a record-high level. Thus, the advancement of code completion is encouraged and has become a popular topic in software engineering. This paper examines and summarizes the development of a statistical language and neural network-based code completion system. The main contents consist of introducing the concepts of code completion system, summarizing the general process of code completion and the evaluation metrics used for performance benchmarking, reviewing and summarizing the existing work conducted on statistical language approach and neural network approach respectively, as well as the limitations and challenges of existing code completion method, and finally forecasting the future development of code completion techniques.

AmeriFlux BASE data pipeline to support network growth and data sharing

AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.