Ease Of Coding Research Articles

Evolution of Artificial Intelligence Programming Languages - a Systematic Literature Review

Artificial Intelligence (AI) has received significant attention in recent years. It is being adopted to provide solutions to medicine, engineering, education, government and several other domains. To analyze the state-of-the-art of research in AI, we present a systematic literature review focusing on the Evolution of AI programming languages. Our search returned 7,604 documents; after reviewing these documents, 78 which were relevant for this study were retained. Our research revealed that the prevalence of AI programming language by volume of publications had experienced peaks and valleys between 1963 and 2018; however, between 2015 to 2020, related publications have been experiencing peaks. During the review period, the PROLOG programming language received the most attention in about 49% of publications; this was followed by LISP, which received almost 22%. The remaining attention was shared between Logic and Object-Oriented Programming (LOOP), ARCHLOG, Epistemic Ontology Language with Constraints (EOLC), Python, C++, ADA and JAVA. However, the predominant AI programming language in recent AI software is C/C++, which takes 70% of the modern AI libraries analyzed in this study. Python is used in 60% of the modern AI libraries analyzed. Their prevalence is as a result of their speed, portability and ease of coding, making them effective in developing trending AI libraries such as TensorFlow and Keras.

Can decision support combat incompleteness and bias in routine primary care data?

ObjectiveRoutine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.Materials and MethodsWe used the clinical documentation of 34 UK general practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician’s final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.ResultsSupported documentation contained significantly more codes (incidence rate ratio [IRR] = 5.76 [4.31, 7.70] P < .001) and less free text (IRR = 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b = −0.08 [−0.11, −0.05] P < .001) in the supported consultations, and this was the case for both codes and free text.ConclusionsWe provide evidence that data entry in the EHR is incomplete and reflects physicians’ cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.

Distributed Community Detection based on Apache Spark using Multi Label Propagation for Digital Social Networks

Organization, Government and Individual (OGI) have popularized the use of Digital Social Networks (DSN) that reduces the processing time of social-aware tasks. To accomplish a community-based communication, each social-aware task should identify its community group. The identified group uses a task to avail all the DSN benefits to their customers / citizens. As a result, the community-based detection algorithm has played a significant role in literature. However, the existing algorithms have had several challenging issues, such as performance and scalability. Thus, a distributed community detection algorithm is presented using Apache Spark’s Resilient Distributed Data Set (RDD) framework based on the Scala programming language. The Apache Spark framework provides an ideal solution that offers ease of coding, performance, interactive mode and disk Input-Output bottlenecks in Hadoop /Map Reduce. Besides, it presents a platform of distributed community detection that reduces the computational computation by applying transformations, aggregations and joins. The experimental results show that the proposed framework achieves high accuracy for both real-world and synthetic networks.

Multivariate—Intervariable, Spatial, and Temporal—Bias Correction*

Abstract Statistical methods to bias correct global or regional climate model output are now common to get data closer to observations in distribution. However, most bias correction (BC) methods work for one variable and one location at a time and basically reproduce the temporal structure of the models. The intervariable, spatial, and temporal dependencies of the corrected data are usually poor compared to observations. Here, the authors propose a novel method for multivariate BC. The empirical copula–bias correction (EC–BC) combines a one-dimensional BC with a shuffling technique that restores an empirical multidimensional copula. Several BC methods are investigated and compared to high-resolution reference data over the French Mediterranean basin: notably, (i) a 1D BC method applied independently to precipitation and temperature fields, (ii) a recent conditional correction approach developed for producing correct two-dimensional intervariable structures, and (iii) the EC–BC method. Assessments are realized in terms of intervariable, spatial, and temporal dependencies, and an objective evaluation using the integrated quadratic distance (IQD) is presented. As expected, the 1D methods cannot produce correct multidimensional properties. The conditional technique appears efficient for intervariable properties but not for spatial and temporal dependencies. EC–BC provides realistic dependencies in all respects: intervariable, spatial, and temporal. The IQD results are clearly in favor of EC–BC. As many BC methods, EC–BC relies on a stationarity assumption and is only able to reproduce patterns inherited from historical data. However, because of its ease of coding, its speed of application, and the quality of its results, the EC–BC method is a very good candidate for all needs in multivariate bias correction.

A New Complicated Report Generation Scheme Base on XML and MS Word in B/S Model

It is usually difficult to generate a complicated report in B/S model because of the function restrictions of client browser .This paper proposes a new scheme to generate complicated report with words, tables and images using MS Word and XML. First, introduces the idea and steps of implementation, and then gives the key solutions to the problems, finally analyses the advantages of the proposed scheme. Practices prove that it works well and could create complicated reports more effectively, haves good reliability, cross-platform, high efficiency and ease of coding.

Can Go address the multicore issues of today and the manycore problems of tomorrow?

Current High Energy and Nuclear Physics (HENP) libraries and frameworks were written before multicore systems became widely deployed and used. From this environment, a 'single-thread' processing model naturally emerged but the implicit assumptions it encouraged are greatly impairing our abilities to scale in a multicore/manycore world. While parallel programming - still in an intensive phase of R&D despite the 30+ years of literature on the subject - is an obvious topic to consider, other issues (build scalability, code clarity, code deployment and ease of coding) are worth investigating when preparing for the manycore era. Moreover, if one wants to use another language than C++, a language better prepared and tailored for expressing concurrency, one also needs to ensure a good and easy reuse of already field-proven libraries. We present the work resulting from such investigations applied to the Go programming language. We first introduce the concurrent programming facilities Go is providing and how its module system addresses the build scalability and dependency hell issues. We then describe the process of leveraging the many (wo)man-years put into scientific Fortran/C/C++ libraries and making them available to the Go ecosystem. The ROOT data analysis framework, the C-BLAS library and the Herwig-6 MonteCarlo generator will be taken as examples. Finally, performances of the tools involved in a small analysis written in Go and using ROOT I/O library will be presented.

International Classification of Disease Coding for Obstructive Lung Disease: Does It Reflect Appropriate Clinical Documentation?

International Classification of Disease coding is widely used by physicians, hospitals, health-care payers, and governments to assess the health of populations and as a means of reimbursement for medical care based on diagnosis and severity of illness. The current classification system, International Classification of Diseases, 9th ed (ICD-9), will soon be replaced by International Classification of Diseases, 10th ed (ICD-10). When the codes that relate to COPD and asthma are examined, the clinical relevance of the categories in International Classification of Disease coding must be questioned. In the future, a more simplified terminology that is consistent with clinical usage could improve accuracy and ease of coding. At present, however, clinicians should become familiar with the present ICD-9 and future ICD-10 codes so that their descriptions of illnesses in the medical records more accurately reflect current coding terminology.

Measurement Uncertainty in Racial and Ethnic Identification Among Adolescents of Mixed Ancestry: A Latent Variable Approach

In this article, we operationalize identification of mixed racial and ethnic ancestry among adolescents as a latent variable to (a) account for measurement uncertainty, and (b) compare alternative wording formats for racial and ethnic self-categorization in surveys. Two latent variable models were fit to multiple mixed-ancestry indicator data from 1,738 adolescents in New England. The first, a mixture factor model, accounts for the zero-inflated mixture distribution underlying mixed-ancestry identification. Alternatively, a latent class model allows classification distinction between relatively ambiguous versus unambiguous mixed-ancestry responses. Comparison of individual indicators reveals that the Census 2000 survey version estimates higher prevalence of mixed ancestry but is less sensitive to relative certainty of identification than are alternate survey versions (i.e., offering a “mixed” check box option, allowing a written response). Ease of coding and missing data are also considered in discussing the relative merit of individual mixed-ancestry indicators among adolescents.

Differential Implementation of the Viscoelastic Response of a Curing Thermoset Matrix for Composites Processing

The ability to manufacture thermoset matrix composite materials into large and complex structures can be significantly enhanced by modeling the behavior of the system during the process. As a result there has been much research on all aspects of the cure of these materials. A particularly important aspect is the development of mechanical properties in the thermoset matrix as it evolves from a low molecular weight material into a fully cross-linked solid. The behavior is generally acknowledged to be viscoelastic, and as both temperature and degree of cure vary with time, the characterization and representation of the behavior is both critical and complex. Many approaches have been suggested and tried, ranging from 1D or 2D implementations of simple linear elastic cure hardening responses (which have been shown to be essentially pseudo-viscoelastic formulations) through to more sophisticated representations of viscoelastic behavior as Prony series of Maxwell elements coded in 3D hereditary integral FE implementations. In this paper we present a differential approach for the viscoelastic representation of a curing thermoset matrix composite undergoing an arbitrary temperature cycle by noting that the viscoelastic response can be represented very well by a Prony series. For this case, we show that a differential approach is equivalent to an integral formulation, but appears to have some significant benefits in terms of extension to more general descriptions (e.g., thermo-viscoelastic behavior), ease of coding and implementation, and perhaps most importantly, computer runtimes. Rather than using a differential approach where the order of the governing differential equation grows very fast with the number of springs or dashpots, we use the stresses in the individual Maxwell elements to capture the complete history of the material and allow for a much simpler formulation. A 1D formulation of this differential approach, including thermo-viscoelasticity, is developed, and results and benchmarks are presented.

Wave-indices

In many applications, new data is being generated every day. Often an index of the data of a past window of days is required to answer queries efficiently. For example, in a warehouse one may need an index on the sales records of the last week for efficient data mining, or in a Web service one may provide an index of Netnews articles of the past month. In this paper, we propose a variety of wave indices where the data of a new day can be efficiently added, and old data can be quickly expired, to maintain the required window. We compare these schemes based on several system performance measures, such as storage, query response time, and maintenance work, as well as on their simplicity and ease of coding.

Global arrays: A nonuniform memory access programming model for high-performance computers

Portability, efficiency, and ease of coding are all important considerations in choosing the programming model for a scalable parallel application. The message-passing programming model is widely used because of its portability, yet some applications are too complex to code in it while also trying to maintain a balanced computation load and avoid redundant computations. The shared-memory programming model simplifies coding, but it is not portable and often provides little control over interprocessor data transfer costs. This paper describes an approach, called Global Arrays (GAs), that combines the better features of both other models, leading to both simple coding and efficient execution. The key concept of GAs is that they provide a portable interface through which each process in a MIMD parallel program can asynchronously access logical blocks of physically distributed matrices, with no need for explicit cooperation by other processes. We have implemented the GA library on a variety of computer systems, including the Intel Delta and Paragon, the IBM SP-1 and SP-2 (all message passers), the Kendall Square Research KSR-1/2 and the Convex SPP-1200 (nonuniform access shared-memory machines), the CRAY T3D (a globally addressable distributed-memory computer), and networks of UNIX workstations. We discuss the design and implementation of these libraries, report their performance, illustrate the use of GAs in the context of computational chemistry applications, and describe the use of a GA performance visualization tool.

Programming Environments for Parallel and Distributed Computing: a Comparison of P4, Pvm, Linda, and Tcgmsg

Parallel programmers must choose from a confusing array of parallel programming environments. When success has to be measured by the success of applica tion-software development rather than theoretical re search, the choice must be made quickly without the luxury of experimentation. In this paper, we help the programmer make this choice by looking closely at four of the most heavily used portable programming environments—p4, PVM, TCGMSG, and Linda. For each of these programming environments, we look at three different programs: one that computes π by numerical integration and two that benchmark communication performance. The four programming environments are analyzed in terms of performance, support, ease of coding, and ease of debugging.

A formal algorithmic model compatible with the conceptual modeling of accounting information systems

Accounting and computer science have been uneasy partners in the development of accounting information systems. The history of their association has generally led end users to do the best they could in matching the needs of accounting systems to the demands of current hardware and software developments. While the results have been impressive, recent developments in computer science make it possible to approach the modeling of accounting information systems in a manner more consistent with the accounting model than previously possible. This paper, motivated by program verification issues from an auditor's viewpoint, introduces a functional approach to accounting systems development. The result is a formal algorithmic model of accounting that is consistent with current developments in computer science. A number of benefits are derived from the approach, including: (1) natural representation; (2) ease of coding, design and modification; and (3) verification capabilities.

VLSI circuit design using APL with fortran subroutines

Our group at IBM Yorktown Research has written a number of tool programs for VLSI design, all in APL. Several crucial algorithms were speeded up by recoding them in Fortran and Assembler Language. For example, the algorithm for complementation of a Boolean expression (which can sometimes have thousands of terms in dozens of variables) is 5 times faster in Fortran than the best APL code I could write. The linking mechanism used from APL to outside languages is the experimental AP403 from IBM Heidelberg, though other similar auxiliary processors exist (e.g. the IUP AP999). Thus, each language, Fortran and APL, is used to its best advantage: Fortran for raw speed, and APL for interactiveness and ease of coding.

A comparison of user-optimum versus system-optimum traffic assignment in transportation network design

In this report, we compare the computational efficiency and results of solving two alternative models for the problem of determining improvements to an urban road network. Using a 1462 link, 584 node test network of the north Dallas area, we compare a model which assumes user-optimum behavior of travelers with a model which assumes system-optimum flows. Both of these models allow improvements to the road network to take on any nonnegative value, rather than requiring discrete improvement values. Investment costs are modeled by functions with decreasing marginal costs. Unfortunately, the user-optimum model, which is much more realistic than the system-optimum one, normally cannot be solved optimally. However, the simpler system-optimum model can be optimally solved, provided that investment costs are approximated by linear functions. Thus, for this network design problem we compare an accurate representation which can be solved only approximately with an approximate representation which can be solved optimally. Our computational testing showed that the system-optimum model produces solutions as good as those from the user-optimum model, and thus seems justified when favored by other considerations, such as ease of coding, availability of “canned” programs, etc.

MOUSE4: An improved implementation of the RATFOR preprocessor

AbstractRATFOR is a preprocessor language for FORTRAN that supports structured flow of control statements and macrosubstitution. The RATFOR processor, written in RATFOR, is modular, carefully coded and portable, but extremely inefficient. A profile of the running time revealed that a linear search in the macroprocessor consumed over half of the CPU time. Running time was reduced by over 50 per cent when a binary search was used. Our observation is of interest primarily because it differs from previously reported measurements.A more dramatic improvement in running time was obtained by rewriting the ad hoc lexical scanner using a standard method based on finite automata. For a 3,000‐line source program the standard RATFOR required 185.470 CPU seconds on a CDC 6500 while the automata‐based version needed only 12.723 seconds.We conclude that, contrary to evidence exhibited by the designer of RATFOR, sequential search is often inadequate for production software. Furthermore, in the case of lexical analysis, well‐known techniques do seem to offer efficiency while retaining the simplicity, ease of coding and modularity of ad hoc methods.

Modular Hardware for On-Line Handling of Nuclear Data

At the National Bureau of Standards a flexible data handling system for use with the NBS Linac has been built. In developing this system the need for ease of modifications, ease of coding, and lexibility were carefully considered. The NBS system is designed around a building block concept. Certain of these building blocks represent the necessary interface between the specialized input-output devices and the computer while the remainder of the blocks represent specific devices such as analog-to-digital converters, scalers, and other devices to facilitate man-machine communication at both local and remote stations. The modular concept of both hardware and software development has allowed for great flexibility in the devices connected to the computer at any time and in the programs required for use with these devices. New hardware building blocks can be added to the system with no difficulty and with no change in existing programs. The general building-block approach to the NBS system together with examples of specific units will be described.