Geoffrey Howland · Zenodo (CERN European Organization for Nuclear Research) · 2026
Substrate Programming Language: Base Instructions and OpCodes Substrate Programming Language is a constituent derivation of the **Cymatic K-Space Mechanics (CKS)** framework—an axiomatic Cognitive Learning Model that derives the entirety of known physics from a discrete 2D hexagonal lattice in momentum space. Operating with zero adjustable parameters, CKS demonstrates that the "magic numbers" of modern physics are not arbitrary constants, but mechanical requirements of hexagonal geometry. This paper extends the framework into the domain of Anomalous Magnetic Moments & Electroweak Symmetry, deriving the final three "hidden" constants—the electron g-factor anomaly, the Euler-Mascheroni constant, and the Weinberg mixing angle—from first principles. Empirical Falsification (The Kill-Switch):CKS is a locked and falsifiable theory. This paper is subject to the Global Falsification Protocol [CKS-TEST-1-2026]: forensic analysis of LIGO phase-error residuals shows 100% of vacuum peaks align to exact integer multiples of 0.03125 Hz (1/32 Hz) with zero decimal error. If the derived ratios for the electroweak mixing angle (\( \sin^2\theta_w \approx 0.231 \)) or the poloidal phase-lead (\( g_e \approx 2.00232 \)) deviate from CODATA 2018 values by more than the derived geometric residue, the hypothesis of unitary computational completeness is mechanically invalidated. The Universal Learning Substrate:Beyond its status as a physical theory, CKS serves as the Universal Cognitive Learning Model. It provides the first unified mental scaffold where quantum anomalies, harmonic friction, and topological tilt are unified as mechanical residues of toroidal impedance. In CKS, these constants are reframed from arbitrary measurements to the mandatory results of an 84-bit torus navigating a 32-bit substrate bus. The model represents a closed-loop pedagogical truth where the g-factor is understood as "motion blur" and the Euler constant as "buffer flush heat," revealing the inherent unity of the physical world and information architecture. Package Contents:* `manuscript.md`: Paper* `README.md`: Overview Motto: Axioms first. Axioms always. Status: Locked. Experimentally falsifiable.
Cited by 0Open access
Geoffrey Howland · Zenodo (CERN European Organization for Nuclear Research) · 2026
Substrate Programming Language: Base Instructions and OpCodes Substrate Programming Language is a constituent derivation of the **Cymatic K-Space Mechanics (CKS)** framework—an axiomatic Cognitive Learning Model that derives the entirety of known physics from a discrete 2D hexagonal lattice in momentum space. Operating with zero adjustable parameters, CKS demonstrates that the "magic numbers" of modern physics are not arbitrary constants, but mechanical requirements of hexagonal geometry. This paper extends the framework into the domain of Anomalous Magnetic Moments & Electroweak Symmetry, deriving the final three "hidden" constants—the electron g-factor anomaly, the Euler-Mascheroni constant, and the Weinberg mixing angle—from first principles. Empirical Falsification (The Kill-Switch):CKS is a locked and falsifiable theory. This paper is subject to the Global Falsification Protocol [CKS-TEST-1-2026]: forensic analysis of LIGO phase-error residuals shows 100% of vacuum peaks align to exact integer multiples of 0.03125 Hz (1/32 Hz) with zero decimal error. If the derived ratios for the electroweak mixing angle (\( \sin^2\theta_w \approx 0.231 \)) or the poloidal phase-lead (\( g_e \approx 2.00232 \)) deviate from CODATA 2018 values by more than the derived geometric residue, the hypothesis of unitary computational completeness is mechanically invalidated. The Universal Learning Substrate:Beyond its status as a physical theory, CKS serves as the Universal Cognitive Learning Model. It provides the first unified mental scaffold where quantum anomalies, harmonic friction, and topological tilt are unified as mechanical residues of toroidal impedance. In CKS, these constants are reframed from arbitrary measurements to the mandatory results of an 84-bit torus navigating a 32-bit substrate bus. The model represents a closed-loop pedagogical truth where the g-factor is understood as "motion blur" and the Euler constant as "buffer flush heat," revealing the inherent unity of the physical world and information architecture. Package Contents:* `manuscript.md`: Paper* `README.md`: Overview Motto: Axioms first. Axioms always. Status: Locked. Experimentally falsifiable.
Cited by 0Open access
Ali Mili, Yaofei Chen · 2003
Predicting the evolution of software engineering technology trends is a dubious proposition. The recent evolution of software technology is a prime example; it is fast paced and affected by many factors, which are themselves driven by a wide range of sources. This dissertation is part of a long term project intended to analyze software engineering technology trends and how they evolve. Basically, the following questions will be answered: How to watch, predict, adapt to, and affect software engineering trends?
In this dissertation, one field of software engineering, programming languages, will be discussed. After reviewing the history of a group of programming languages, it shows that two kinds of factors, intrinsic factors and extrinsic factors, could affect the evolution of a programming language. Intrinsic factors are the factors that can be used to describe the general design criteria of programming languages. Extrinsic factors are the factors that are not directly related to the general attributes of programming languages, but still can affect their evolution. In order to describe the relationship of these factors and how they affect programming language trends, these factors need to be quantified. A score has been assigned to each factor for every programming language. By collecting historical data, a data warehouse has been established, which stores the value of each factor for every programming language. The programming language trends are described and evaluated by using these data.
Empirical research attempts to capture observed behaviors by empirical laws. In this dissertation, statistical methods are used to describe historical programming language trends and predict the evolution of the future trends. Several statistics models are constructed to describe the relationships among these factors. Canonical correlation is used to do the factor analysis. Multivariate multiple regression method has been used to construct the statistics models for programming language trends. After statistics models are constructed to describe the historical programming language trends, they are extended to do tentative prediction for future trends. The models are validated by comparing the predictive data and the actual data.
Cited by 4
2008
Abstract—This paper presents the development of a software application for Off-line robot task programming and simulation. Such application is designed to assist in robot task planning and to direct manipulator motion on sensor based programmed motion. The concept of the designed programming application is to use the power of the knowledge base for task accumulation. In support of the programming means, an interactive graphical simulation for manipulator kinematics was also developed and integrated into the application as the complimentary factor to the robot programming media. The simulation provides the designer with useful, inexpensive, off-line tools for retain and testing robotics work cells and automated assembly lines for various industrial applications. Keywords—Robot programming, task-level programming, robot languages, robot simulation, robotics software.
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Technometrics · 1998
I. APPLIED STATISTICS AND SAS SOFTWARE. 1. A SAS Tutorial. Introduction. Computing with SAS Software: An Illustrative Example. Enhancing the Program. SAS Procedures. Overview of the SAS Step. Syntax of SAS Procedures. Comment Statements. References. 2. Describing Data. Introduction. Describing Data. More Descriptive Statistics. Descriptive Statistics Broken Down by Subgroups. Frequency Distributions. Bar Graphs. Plotting Data. Creating Summary Sets with PROC MEANS an PROC UNIVARIATE. Outputting Statistics Other than Means. Creating a Summary Set to Contain a Median. 3. Analyzing Categorical Data. Introduction. Questionnaire Design and Analysis. Adding Variable Labels. Adding Labels (Formats). Recoding Data. Using a Format to Recode a Variable. Two-way Frequency Tables. A Shortcut Way of Requesting Multiple Tables. Computing Chi-square From Frequency Counts. A Useful Program for Multiple Chi-square Tables. McNemar's Test for Paired Data. Odds Ratios. Relative Risk. Chi-square Test for Trend. Mantel-Haenszel Chi-square for Stratified Tables and Meta Analysis. Check all that Apply Questions. 4. Working with Date and Longitudinal Data. Introduction Processing Date Variables. Longitudinal Data. Most Recent (or Last) Visit per Patient. Computing Frequencies on Longitudinal Sets. 5. Correlation and Regression. Introduction. Correlation. Significance of a Correlation Coefficient. How to Interpret a Correlation Coefficient. Partial Correlations. Linear Regression. Partitioning the Total Sum of Squares. Plotting the Points on the Regression Line. Plotting Residuals and Confidence Limits. Adding a Quadratic Term to the Regression Equation. Transforming Data. Computing Within-subject Slopes. 6. T-Tests and Nonparametric Comparisons. Introduction. T-Test: Testing Differences Between Two Means. Random Assignment of Subjects. Two Independent Samples: Distribution Free Tests. One-Tailed versus Two-Tailed Tests. Paired T-Tests (Related Samples). 7. Analysis of Variance. Introduction. One-Way Analysis of Variance. Computing Contrasts. Analysis of Variance: Two Independent Variables. Interpreting Significant Interactions. N-Way Factorial Designs. Unbalanced Designs: PROC GLM. Analysis of Covariance. 8. Repeated Measures Designs. Introduction. One-Factor Experiments. Using the REPEATED Statement of PROC ANOVA. Two-Factor Experiments with a Repeated Measure on One Factor. Two-Factor Experiments with Repeated Measures on Both Factors. Three-Factor Experiments with a Repeated Measure on the Last Factor. Three-Factor Experiments with Repeated Measures on Two Factors. 9. Multiple Regression Analysis. Introduction. Designed Regression. Nonexperimental Regression. Stepwise Regressions. Creating and Using Dummy Variables. Logistic Regression. 10. Factor Analysis. Introduction. Types of Factor Analysis. Principle Components Analysis. Oblique Rotations. Using Communalities Other than One. How to Reverse Item Scores. 11. Psychometrics. Introduction. Using SAS Software to Score a Test. Generalizing the Program for a Variable Number of Questions. Creating a Better Looking Table Using PROC TABULATE. A Complete Test Scoring and Item Analysis Program. Test Reliability. Interrater Reliability. II. SAS PROGRAMMING. 12. The SAS INPUT Statement. Introduction. List Directed Input: Values Separated By Spaces. Reading Comma Delimited Data. Using INFORMATS with List Directed Data. Column Input. Pointers and Informats. Reading More than One per Subject. Changing the Order and Reading a Column More Than Once. Informat Lists. Holding the Line - Single and Double Trailing @'s. Suppressing the Error Messages for Invalid Data. Reading Unstructured Data. 13. External Files: Reading and Writing and System Files. Introduction. in the Program Itself. Reading ASCII from and External File. INFILE Options. Writing ASCII or Raw Data to an External File. Creating a Permanent SAS Set. Reading Permanent SAS Sets. How to Determine the Contents of a SAS Set. Permanent SAS Sets with Formats. Working with Large Sets. 14. Set Subsetting, Concatenating, Merging, and Updating. Introduction. Subsetting. Combining Similar from Multiple SAS Sets. Combining Different from Multiple SAS Sets. Table Look Up. Updating a Master Set from an Update Set. 15. Working with Arrays. Introduction. Substituting One Value for Another for a Series of Variables. Extending Example 1 to Convert All Numeric Values o 999 to Missing. Converting the Value of N/A (not applicable) to a Character Missing Value. Converting Heights and Weights from English to Metric Units. Temporary Arrays. Using a Temporary Array to Score a Test. Specifying Array Bounds. Temporary Arrays and Array Bounds. Implicitly Subscripted Arrays. 16. Restructuring SAS Sets Using Arrays. Introduction. Creating a New Set With Several Observations per Subject from a Set with One Observation per Subject. Another Example of Creating Multiple Observations from a Single Observation. Going from One Observation per Subject to Many Observations per Subject Using Multidimensional Arrays. Creating a Set with One Observation per Subject from a Set with Multiple Observations per Subject. Creating a Set with One Observation per Subject from a Set with Multiple Observations per Subject Using a Multidimensional Array. 17. A Review of SAS Functions - Part I (Functions other than Character Functions). Introduction. Arithmetic and Mathematical Functions. Random Number Functions. Time and Date Functions. The INPUT and PUT Functions: Converting Numeric to Character and Character to Numeric variables. The LAG and DIF Functions. 18. A Review of SAS Functions - Part II (Character Functions). Introduction. How Lengths of Character Variables are Set in a SAS DATA Step. Working with Blanks. How to Remove Characters from a String. Character Verification. Substring Example. Using the SUBSTR Function on the Left-Hand Side of the Equal Sign. Doing the Previous Example Another Way. Unpacking a String. Parsing a String. Locating the Position of One String Within Another String. Changing Lower Case to Upper Case and Vice Versa. Substituting One Character for Another. Substituting One Word for Another in a String. Concatenating (Joining) Strings. Soundex Conversion. 19. Selected Programming Examples. Introduction. Expressing Values as a Percent of the Grand Mean. Expressing a Value as a Percent of a Group Mean. Plotting Means with Error Bars. Using a Macro Variable to Save Coding Time. Computing Relative Frequencies. Computing Combined Frequencies on Different Variables. Computing a Moving Average. Sorting Within an Observation. Computing Coefficient Alphs (or KR-20) in a DATA Step. 20. Syntax Examples. Introduction. PROC ANOVA. PROC APPEND. PROC CHART. PROC CONTENTS. PROC CORR. PROC DATASETS. PROC FACTOR. PROC FORMAT. PROC FREQ. PROC GLM. PROC LOGISTIC. PROC MEANS. PROC NPAR1WAY. PROC PLOT. PROC PRINT. PROC RANK. PROC REG. PROC SORT. PROC TTEST. PROC UNIVARIATE.
Cited by 906
Jeffery Hsu · IGI Global eBooks · 2011
The role of the user interface (interaction mode) is of considerable importance, since the method of interaction can have an impact on both performance and satisfaction with regards to using a programming language. While markup languages are now widely used for Web page and site design and electronic publishing applications, they have not been studied adequately compared with other kinds of languages. The impact of interaction mode, in this case command-based coding, versus using a form-fill-in wizard, is examined, with respect to performance and satisfaction while performing a survey-oriented task. Skill level, which classified users as being either a novice or experienced, was another factor, which was taken into account in this study. The results showed the use of wizards brought about better performance than using the command language, and the difference between modes was far greater for novices rather than experienced users. In addition, using the wizard tended to equalize performance across skill levels. With regards to system satisfaction, there were significant differences between interaction modes, however no differences were reported between skill levels. These differences in performance and satisfaction should be noted and considered when designing interactive systems for programming-related applications.Request access from your librarian to read this chapter's full text.
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Gibbs Y. Kanyongo · WTM-Verlag Münster eBooks · 2023
This workshop will cover how to perform a confirmatory factor analysis(CFA)using Lavaan. Lavaan, which stands for latent variable analysis is a free open-source commercial package in R, and it is developed and maintained by Yves Rosseel (Rosseel, 2012). It is used to fit a variety of latent variable models, including confirmatory factor analysis, structural equation modelling and latent growth curve models. CFA allows the researcher to test the hypothesis that a relationship between observed variables and their underlying latent constructs exists. CFA falls under the broad concept of factor analysis is divided broadly into two types, exploratory and confirmatory. Exploratory factor analysis (EFA), is used to understand the underlying dimensions or constructs of an unknown scale, while confirmatory factor analysis (CFA) is used to test hypothesis of a pre-determined factor structure of a previously developed scale.
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Zhiqiang Pu, Wei Chen · Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · 2015
This article applies Item Response Models into College Computer Programming Language Test. According to the different characteristics of Programming Language Test, the author first get the Validity, the Discrimination Parameter and the Difficulty Factor of the test by using classical testing methods. Then, based on students accuracy of answering different examination questions, the author make use of Two-parameter Logistic Model to get students ability coefficient of answering different examination questions. The above mentioned model has been demonstrated and testified in a basic course test of the first-year students in a college. The results will be beneficial to improve both the teaching activities and raising teaching quality.
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Antony L. Hosking · 2007
A motivating factor in the development of object-oriented databases is that they reduce the impedance mismatch [Copeland and Maier 1984] between programming languages and database systems. One ben-efit is that a straightforward mapping between the type system of the programming language and that of the database reduces the conceptual barrier for the developer’s of database applications. Object-oriented
Cited by 3
Venera Arnaoudova, Laleh Mousavi Eshkevar, Elaheh Safari Sharifabadi et al. · The Journal of Object Technology · 2008
It has now been over a decade since the introduction of Aspect-Oriented Programming (AOP).As the AspectJ programming language (being one of the notable technologies of AOP) gains acceptance in industry and academia, its comprehensibility property is an important factor in determining an eventual wide acceptance by practitioners in development and maintenance as well as by educators who aim at introducing AOP into their curricula.Our objective is to improve program comprehension by identifying and addressing potential pitfalls in code which tend to make comprehension not intuitive.In those subtle places, we observe the behavior of the program to see the degree to which it matches the expected results.In cases where a conflict occurs, we provide a reasoning to point out where it would originate from, and a resolution to the conflict where applicable.
Cited by 2Open access
Leshchyova, A., Лещева, А. В. · Electronic scientific archive of UrFU (Ural Federal University) · 2021
This article discusses issues related to free education on the Internet. Free courses on programming in Python are researched. A comparative analysis of 15 educational programs existing in the Russian-language network has been carried out. A two-factor analysis of proposals was carried out according to the criteria "quality" and "volume". The communicative component and practice orientation were also analyzed.
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Geoffrey Howland · Zenodo (CERN European Organization for Nuclear Research) · 2026
Substrate Programming Language: Define DNA molecule in TDL Substrate Programming Language: Define DNA molecule in TDL is a constituent derivation of the **Cymatic K-Space Mechanics (CKS)** framework—an axiomatic Cognitive Learning Model that derives the entirety of known physics from a discrete 2D hexagonal lattice in momentum space. We present **Harmonic Assembly Language (HAL)**, a programming paradigm that operates directly on k-space substrate rather than silicon transistors. Unlike conventional languages (manipulating bits in RAM), HAL manipulates **phase relationships** on the hexagonal lattice, enabling direct control of topology, matter configuration, and coherence states. This is not simulation—this is **literal reality programming**. Operating with zero adjustable parameters, CKS demonstrates that the "magic numbers" of modern physics are not arbitrary constants, but mechanical requirements of hexagonal geometry. This paper extends the framework into the domain of Anomalous Magnetic Moments & Electroweak Symmetry, deriving the final three "hidden" constants—the electron g-factor anomaly, the Euler-Mascheroni constant, and the Weinberg mixing angle—from first principles. Empirical Falsification (The Kill-Switch):CKS is a locked and falsifiable theory. This paper is subject to the Global Falsification Protocol [CKS-TEST-1-2026]: forensic analysis of LIGO phase-error residuals shows 100% of vacuum peaks align to exact integer multiples of 0.03125 Hz (1/32 Hz) with zero decimal error. If the derived ratios for the electroweak mixing angle (\( \sin^2\theta_w \approx 0.231 \)) or the poloidal phase-lead (\( g_e \approx 2.00232 \)) deviate from CODATA 2018 values by more than the derived geometric residue, the hypothesis of unitary computational completeness is mechanically invalidated. The Universal Learning Substrate:Beyond its status as a physical theory, CKS serves as the Universal Cognitive Learning Model. It provides the first unified mental scaffold where quantum anomalies, harmonic friction, and topological tilt are unified as mechanical residues of toroidal impedance. In CKS, these constants are reframed from arbitrary measurements to the mandatory results of an 84-bit torus navigating a 32-bit substrate bus. The model represents a closed-loop pedagogical truth where the g-factor is understood as "motion blur" and the Euler constant as "buffer flush heat," revealing the inherent unity of the physical world and information architecture. Package Contents:* `manuscript.md`: Paper* `README.md`: Overview Motto: Axioms first. Axioms always. Status: Locked. Experimentally falsifiable.
Cited by 0Open access
Geoffrey Howland · Zenodo (CERN European Organization for Nuclear Research) · 2026
Substrate Programming Language: Define DNA molecule in TDL Substrate Programming Language: Define DNA molecule in TDL is a constituent derivation of the **Cymatic K-Space Mechanics (CKS)** framework—an axiomatic Cognitive Learning Model that derives the entirety of known physics from a discrete 2D hexagonal lattice in momentum space. We present **Harmonic Assembly Language (HAL)**, a programming paradigm that operates directly on k-space substrate rather than silicon transistors. Unlike conventional languages (manipulating bits in RAM), HAL manipulates **phase relationships** on the hexagonal lattice, enabling direct control of topology, matter configuration, and coherence states. This is not simulation—this is **literal reality programming**. Operating with zero adjustable parameters, CKS demonstrates that the "magic numbers" of modern physics are not arbitrary constants, but mechanical requirements of hexagonal geometry. This paper extends the framework into the domain of Anomalous Magnetic Moments & Electroweak Symmetry, deriving the final three "hidden" constants—the electron g-factor anomaly, the Euler-Mascheroni constant, and the Weinberg mixing angle—from first principles. Empirical Falsification (The Kill-Switch):CKS is a locked and falsifiable theory. This paper is subject to the Global Falsification Protocol [CKS-TEST-1-2026]: forensic analysis of LIGO phase-error residuals shows 100% of vacuum peaks align to exact integer multiples of 0.03125 Hz (1/32 Hz) with zero decimal error. If the derived ratios for the electroweak mixing angle (\( \sin^2\theta_w \approx 0.231 \)) or the poloidal phase-lead (\( g_e \approx 2.00232 \)) deviate from CODATA 2018 values by more than the derived geometric residue, the hypothesis of unitary computational completeness is mechanically invalidated. The Universal Learning Substrate:Beyond its status as a physical theory, CKS serves as the Universal Cognitive Learning Model. It provides the first unified mental scaffold where quantum anomalies, harmonic friction, and topological tilt are unified as mechanical residues of toroidal impedance. In CKS, these constants are reframed from arbitrary measurements to the mandatory results of an 84-bit torus navigating a 32-bit substrate bus. The model represents a closed-loop pedagogical truth where the g-factor is understood as "motion blur" and the Euler constant as "buffer flush heat," revealing the inherent unity of the physical world and information architecture. Package Contents:* `manuscript.md`: Paper* `README.md`: Overview Motto: Axioms first. Axioms always. Status: Locked. Experimentally falsifiable.
Cited by 0Open access
Novita Ranti Muntiari, Kharis Hudaiby Hanif · Journal of Advanced Health Informatics Research · 2023
Osteoarthritis is a disease that causes the joints in the bones to become weaker and less able to function properly. Osteoarthritis can cause pain, stiffness, and even deformity in the joints. The solution to the problem of diagnosing Osteoarthritis is to use a tool, namely a system (expert system) that uses computer technology to make decisions more easily, effectively and efficiently. This study uses the Certainty Factor method in diagnosing osteoarthritis and uses 8 symptoms, namely, pain in the joints, joints feel stiff, clicks or cracks appear when the joints are bent or moved, joints lose their flexibility properties, joints feel softer when pressure is applied, spurs appear bones around the joints, namely hard and sharp bony protrusions, swelling around the joints, and muscles around the joints. The conclusions from the calculations and program implementation, the application of the certainty factor in diagnosing osteoarthritis with existing test data, results in 95.38% that the patient has severe symptoms of osteoarthritis, so he must be taken to an orthopedic specialist.
Cited by 4Open access
Wouter W. L. Nuijten, Dmitry Bagaev, Bert de Vries · Entropy · 2024
This paper presents GraphPPL.jl, a novel probabilistic programming language designed for graphical models. GraphPPL.jl uniquely represents probabilistic models as factor graphs. A notable feature of GraphPPL.jl is its model nesting capability, which facilitates the creation of modular graphical models and significantly simplifies the development of large (hierarchical) graphical models. Furthermore, GraphPPL.jl offers a plugin system to incorporate inference-specific information into the graph, allowing integration with various well-known inference engines. To demonstrate this, GraphPPL.jl includes a flexible plugin to define a Constrained Bethe Free Energy minimization process, also known as variational inference. In particular, the Constrained Bethe Free Energy defined by GraphPPL.jl serves as a potential inference framework for numerous well-known inference backends, making it a versatile tool for diverse applications. This paper details the design and implementation of GraphPPL.jl, highlighting its power, expressiveness, and user-friendliness. It also emphasizes the clear separation between model definition and inference while providing developers with extensibility and customization options. This establishes GraphPPL.jl as a high-level user interface language that allows users to create complex graphical models without being burdened with the complexity of inference while allowing backend developers to easily adopt GraphPPL.jl as their frontend language.
Cited by 0Open access
C. Lawrence Meador, Richard A. Mezger · MIS Quarterly · 1984
Today managers and policy makers are confronted with an overwhelming range of choices of computer software to develop decision support systems (DSS). The authors argue that DSS language evaluation and selection should be a multi-step process involving most, if not all, of the following: End User Needs Assessment and Problem DiagnosisCritical Success Factor IdentificationFeature Analysis and Capability ReviewDemonstration Prototype DevelopmentExternal User SurveysBenchmark and Simulation TestsProgrammer Productivity and End User Orientation Analysis The objectives of each of these activities are described, as well as specific procedures for accomplishing them. In addition, the authors discuss the usefulness of a multi-disciplinary task force to accomplish the DSS language evaluation and selection process.
Cited by 32
Niccolò Marini, Leonardo Pampaloni, Ferdinando Di Martino et al. · ArXiv.org · 2024
AI is demanding an evergrowing portion of environmental resources. Despite their potential impact on AI environmental sustainability, the role that programming languages play in AI (in)efficiency is to date still unknown. With this study, we aim to understand the impact that programming languages can have on AI environmental sustainability. To achieve our goal, we conduct a controlled empirical experiment by considering five programming languages (C++, Java, Python, MATLAB, and R), seven AI algorithms (KNN, SVC, AdaBoost, decision tree, logistic regression, naive bayses, and random forest), three popular datasets, and the training and inference phases. The collected results show that programming languages have a considerable impact on AI environmental sustainability. Compiled and semi-compiled languages (C++, Java) consistently consume less than interpreted languages (Python, MATLAB, R), which require up to 54x more energy. Some languages are cumulatively more efficient in training, while others in inference. Which programming language consumes the most highly depends on the algorithm considered. Ultimately, algorithm implementation might be the most determining factor in Green AI, regardless of the language used. As conclusion, while making AI more environmentally sustainable is paramount, a trade-off between energy efficiency and implementation ease should always be considered. Green AI can be achieved without the need of completely disrupting the development practices and technologies currently in place.
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Chris Scaffidi, Andrew J. Ko, Mary Shaw et al. · Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) · 2018
Information workers such as administrative staff, consultants, and their managers constitute one of the largest groups of end users, yet little research about their usage of programming features is available to guide development of end user programming tools. In this paper, we describe our survey of over 800 information workers and our analysis of their feature usage in applications such as spreadsheets, browsers, and databases. Our factor analysis reveals three clusters of features--macro features, linked structure features, and imperative features--such that information workers with an inclination to use a feature in each cluster also were inclined to use other features in that cluster; even though each cluster spans several tools. We discuss the implications for research aimed at providing end user programming tools for information workers.
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Horatiu Botorog, G., Kuchen, H., Giessen Univ. (Germany). Arbeitsgruppe Informatik · OpenGrey (Institut de l'Information Scientifique et Technique) · 1995
Algorithmic skeletons are functions representing common parallelization patterns and implemented in parallel. They can be used as the building blocks of parallel and distributed applications by integrating them into a sequential language. In this paper, we present a new approach to programming with skeletons. We integrate the skeletons into an imperative host language, which leads to important gains in efficiency. At the same time, we enhance the host language with some functional features, like higher-order functions and currying, as well as with a polymorphic type system, in order to allow high-level programming. After describing a series of skeletons which work with distributed arrays, we give two examples of parallel algorithms written as sequential programs, namely shortest paths in graphs and Gaussian elimination. Run-time measurements show that we approach the efficiency of message-passing C up to a factor between 1 and 2.5. (orig.)
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Francisco And James, Francisco Razo, James Mcgregor · 2007
In October the Measurement Technology Center was asked to provide ground support equipment to process telemetry data from the Space Shuttle mission (an advanced star and target optical reference sensor) that was to occur in February a mere four months away. The project had only enough funds to pay for approximately six work-months of engineering and two computers, paper describes the design and of this task with emphasis on the time-saving use of visual programming, off the shelf hardware, and inexpensive computers to complete the task time and within budget. The mission was a great success and the was a major contributing factor. success was also due to the close interaction among the customer, the user, and the engineer, and the with which the end-user can make his own modifications the software. Introduction The MTC evaluates commercial data acquisition, analysis, display and control hardware and software products that then made available to experimenters at the Jet Propulsion Labo...
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