THE OBJECTIVES AND REQUIREMENTS OF MODEL MANAGEEMENT

Model management is a technology evolving by necessity, pushed by the attempts to deal with increasingly complex systems and the perceived inadequacies of past efforts. This rapid evolution of Model Management Systems (MMS) has created different perspectives of the role of the MMS; one arising the user's interaction with a model data bank and the other view from the in the database and decision support systems research community stressing modeling community emphasizing the model development functions. These two perspectives are clarified and reconciled by relating each to the model life cycle, which leads to a more comprehensive statement of MMS requirements

Requirements for Model Development Environments

This paper deals with the initial phase of our ongoing research project on the Definition of a Discrete Event Simulation MDE which started on 1 June 1983. The first phase of the rapid prototyping approach we are using in designing the MDE involves the requirements specification. A literature review revealed eleven current problems in modeling. To address these problems, a MDE was identified as composed of four layers: (1) hardware and operating system, (2) kernel MDE, (3) minimal MDE, and (4:) MDEs. Requirements were then perceived for each layer and are reported in this paper. The feasibility of the requirements have been assessed throughout our proto typing efforts. This paper has provided significant guidance to our research group in designing the MDE and its associated tools. We believe that the designers and implementers of other types of MDEs can benefit from the research described herein

Credibility Assessment of Simulation Results: The State of the Art

The purpose of this paper is to provide a state-of-the-art survey of credibility assessment of simulation results and suggest some future research directions. A hierarchy of the credibility assessment is introduced and the state-of-the-art survey is presented with respect to this hierarchy. A glossary is provided to alleviate the lack of standard terminology. The future research calls upon looking at the global picture when conducting a simulation study and being concerned with all of the eleven credibility assessment stages not just model validation and programmed model verification

Synthesis of Graphene on Gold

Here we report chemical vapor deposition of graphene on gold surface at ambient pressure. We studied effects of the growth temperature, pressure and cooling process on the grown graphene layers. The Raman spectroscopy of the samples reveals the essential properties of the graphene grown on gold surface. In order to characterize the electrical properties of the grown graphene layers, we have transferred them on insulating substrates and fabricated field effect transistors. Owing to distinctive properties of gold, the ability to grow graphene layers on gold surface could open new applications of graphene in electrochemistry and spectroscopy.Comment: 8 pages, 4 figure

Otrouha: A Corpus of Arabic ETDs and a Framework for Automatic Subject Classification

Although the Arabic language is spoken by more than 300 million people and is one of the six official languages of the United Nations (UN), there has been less research done on Arabic text data (compared to English) in the realm of machine learning, especially in text classification. In the past decade, Arabic data such as news, tweets, etc. have begun to receive some attention. Although automatic text classification plays an important role in improving the browsability and accessibility of data, Electronic Theses and Dissertations (ETDs) have not received their fair share of attention, in spite of the huge number of benefits they provide to students, universities, and future generations of scholars. There are two main roadblocks to performing automatic subject classification on Arabic ETDs. The first is the unavailability of a public corpus of Arabic ETDs. The second is the linguistic complexity of the Arabic language; that complexity is particularly evident in academic documents such as ETDs. To address these roadblocks, this paper presents Otrouha, a framework for automatic subject classification of Arabic ETDs, which has two main goals. The first is building a Corpus of Arabic ETDs and their key metadata such as abstracts, keywords, and title to pave the way for more exploratory research on this valuable genre of data. The second is to provide a framework for automatic subject classification of Arabic ETDs through different classification models that use classical machine learning as well as deep learning techniques. The first goal is aided by searching the AskZad Digital Library, which is part of the Saudi Digital Library (SDL). AskZad provides other key metadata of Arabic ETDs, such as abstract, title, and keywords. The current search results consist of abstracts of Arabic ETDs. This raw data then undergoes a pre-processing phase that includes stop word removal using the Natural Language Tool Kit (NLTK), and word lemmatization using the Farasa API. To date, abstracts of 518 ETDs across 12 subjects have been collected. For the second goal, the preliminary results show that among the machine learning models, binary classification (one-vs.-all) performed better than multiclass classification. The maximum per subject accuracy is 95%, with an average accuracy of 68% across all subjects. It is noteworthy that the binary classification model performed better for some categories than others. For example, Applied Science and Technology shows 95% accuracy, while the category of Administration shows 36%. Deep learning models resulted in higher accuracy but lower F-measure; their overall performance is lower than machine learning models. This may be due to the small size of the dataset as well as the imbalance in the number of documents per category. Work to collect additional ETDs will be aided by collaborative contributions of data from additional sources

Providing Reusability and Learning Support in the Simulation Model Development Environment

The Premodels Manager, one of the tools of the Simulation Model Development Environment (SMDE), is required to enable a user to locate and reuse components of successfully completed simulation studies and learn from past experience. This paper presents the SMDE Premodels Manager and describes how it provides reusability and learning support. Objectives are set forth and a design is established and implemented on a Sun workstation. The SMDE Premodels Manager consists of four working windows, three access windows, and three support windows. It uses the SMDE Premodels Database which is a highly modifiable repository of documentation on successfully completed simulation studies. It is evaluated with respect to the design objectives and is shown to provide effective reusability and learning support within the SMDE

Simulation Support: Prototyping The Automation-Based Paradigm

This paper describes our research efforts in prototyping the automation-based software paradigm to provide automated support for discrete-event simulation model development. The automation-based paradigm has been suggested as the software technology in the 1990's. The technology needed to support this paradigm does not yet exist. However, the benefits to be gained are so significant that, if achieved, it could profoundly change the way that simulation models are developed. We have been working to achieve this paradigm in the form of an environment composed of an integrated and comprehensive collection of computer-based tools. Our prototyping efforts have focused on the Model Generator, Model Analyzer, and Assistance Manager tools. The Model Generator tool is crucial for the realization of the paradigm and three prototypes have been developed. Our experimentations with the prototypes indicate that the paradigm can be achieved if a small problem domain is chosen. The problem becomes quite complex in the domain-independent case; nevertheless, we believe that the challenge can be met by way of an evolutionary development of prototypes