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Practically every programming language is equipped with primitive data types, iteration and selection constructs, procedures, functions, class and structure definition, exceptions, and so forth to facilitate program development. These are similar in functionality but may differ a little in syntax. Similarly, libraries and packages for functionalities like collections, input/output, and so forth are available with different names to perform similar functions. A programmer has a hard time memorizing the syntax and features of each programming language. The research aims to choose a single unified high-level programming language, a Unified Programming Language (UPL), whose syntax is like of the most commonly existing programming languages. The benefits of learning and adopting a single programming language for programmers can be comforting, but there are some unintended consequences that need to be addressed. This research has gone through several brainstorming sessions and structured walkthroughs within an accessible community to identify and enlist the issues. The most common concern is the usage of a massive amount of existing code/libraries, which results in uncountable manhours and worthwhile investment spent to build it. The subsequent concerns are the availability of programming tools like IDEs and various commonly required libraries and frameworks related to accessing range of databases, files, network streams, and building GUIs and reports. UPL, when released, will be equipped with all necessary libraries, and will provide a set of tools to hook existing libraries frameworks of existing commonly used programming languages like C/C++, JAVA, PHP, PYTHON, .NET languages and JAVASCRIPT, and more. This may be achieved through employing reflection API and documentation, whichever is appropriate. The UPL requires a compiler to generate binary executable code for various platforms. [ABSTRACT FROM AUTHOR]

There are numerous application areas of computing similarity between process models. It includes finding similar models from a repository, controlling redundancy of process models, and finding corresponding activities between a pair of process models. The similarity between two process models is computed based on their similarity between labels, structures, and execution behaviors. Several attempts have been made to develop similarity techniques between activity labels, as well as their execution behavior. However, a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them. However, neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity. To that end, we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models. Furthermore, we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences. Finally, we have evaluated the proposed approach using our generated collection of process models. [ABSTRACT FROM AUTHOR]

Almost every modern high-level programming language includes primitives and user-defined data types, constructs for iterations, selections, functions, structures, and class definitions, handling exceptions, etc. Such a construct provides similar functionality across multiple languages but may slightly differ syntactically. Similarly, libraries are also available in every language, providing similar functionalities but with different names. For programmers, it is hard to learn the syntax and remember the names of library functions of each of the existing programming languages. Hence, in large, a programmer has expertise in a small subset of programming languages. In this research, we are proposing a Unified Programming Language (UPL) whose syntax is inspired by the programming languages commonly used nowadays. The proposed UPL may have its compiler or may accompany a transpiler to translate UPL code to any of the existing high-level programming languages like C/C++, Java, Python, etc. It will make the life of programmers easier as they have to learn and adopt only a single programming language. The UPL may become the baseline of development of some Visual Programming Languages (VPL). Unification may lead to some consequences, which need to be identified and addressed. [ABSTRACT FROM AUTHOR]

Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge. One of the establishedways of improving process performance is to assign the most appropriate resources to each task of the process. However, evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way, is missing. It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored. To address this problem an integrated resource classification framework is presenting that identifies competence, suitability, and preference as the relationship of task with resources. But, only the competence relationship of human resources with a task is presented in this research as a resource competence model. Furthermore, the competency calculation method is presented as a user guider layer for business process intelligencebased resource competence evaluation. The computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the process. Applicability of method is illustrated through a heathcare case study. [ABSTRACT FROM AUTHOR]

Women with heart disease during pregnancy are at higher risk, which can harm the fetus. This risk can be reduced if we diagnose and treat it early. The decision-making system is very helpful in such situations. Many clinical decision-making systems have been proposed, but they are too complicated for medical experts to understand and adapt. Here, we develop a new neutrosophic model for early diagnosis and explain it using explainable artificial techniques. Our model is taking eight symptoms and signs as inputs and determines the diagnosis, type of treatment, and prognosis. Age, obesity, smoking, family pathological history, personal pathological history, electrocardiogram, ultrasound, and functional class are the inputs of this model. Six diagnoses can be made- obstruction at existing, obstruction at entry, rhythm disorder, conduction disorders, congenital diseases, genetic diseases. The types of treatments are- pregnancy interruption, diuretic treatment, anti-arrhythmic treatment, treatment with beta-blockers and anticoagulants treatment. The prognosis is- eutectic delivery, dystocic delivery, the child with complications, child without complications, mother with complications, and mother without complications. The main parts of this system are neutrosophication, knowledge base, inference engine, de-neutrosophication, and explainability. To present the entire execution of the proposed system, we design an algorithm and compute its time complexity to demonstrate the working of the entire system. We compared the results of different methods to gain confidence in our model. [ABSTRACT FROM AUTHOR]

In this paper, we discuss aggregation operators for single-valued neutrosophic N-soft numbers. Further, we develop singlevalued neutrosophic N-soft TOPSIS method based on single-valued neutrosophic N-soft aggregate operators in order to cumulate the decisions of all experts according to the worth of experts' opinion and parameters related to each alternative. For the final decision, we use revised closeness index depending upon the distance measures of alternatives from single-valued neutrosophic N-soft positive ideal solution and single-valued neutrosophic N-soft negative ideal solution. A numerical example is described to illustrate the importance of the proposed method. A comparison of single-valued neutrosophic N-soft TOPSIS method with single-valued neutrosophic TOPSIS method ensures the significance and trustworthiness of the proposed model. [ABSTRACT FROM AUTHOR]

Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service. Integral to that is analyzing the current performance of each task of the process and assigning the most appropriate resources to each task. In continuation of our previous work, we categorize resources into human and non-human resources. For instance, in the healthcare domain, human resources include doctors, nurses, and other associated staff responsible for the execution of healthcare activities; whereas the non-human resources include surgical and other equipment needed for execution. In this study, we contend that the two types of resources (human and non-human) have a different impact on the process performance, so their suitability should be measured differently. However, no work has been done to evaluate the suitability of non-human resources for the tasks of a process. Consequently, it becomes difficult to identify and subsequently overcome the inefficiencies caused by the non-human resources to the task. To address this problem, we present a three-step method to compute a suitability score of non-human resources for the task. As an evaluation of the proposed method, a healthcare case study is used to illustrate the applicability of the proposed method. Furthermore, we performed a controlled experiment to evaluate the usability of the proposed method. The encouraging response shows the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]