A model driven approach to analysis and synthesis of sequence diagrams

Software design is a vital phase in a software development life cycle as it creates a blueprint for the implementation of the software. It is crucial that software designs are error-free since any unresolved design-errors could lead to costly implementation errors. To minimize these errors, the software community adopted the concept of modelling from various other engineering disciplines. Modelling provides a platform to create and share abstract or conceptual representations of the software system – leading to various modelling languages, among them Unified Modelling Language (UML) and Petri Nets. While Petri Nets strong mathematical capability allows various formal analyses to be performed on the models, UMLs user-friendly nature presented a more appealing platform for system designers. Using Multi Paradigm Modelling, this thesis presents an approach where system designers may have the best of both worlds; SD2PN, a model transformation that maps UML Sequence Diagrams into Petri Nets allows system designers to perform modelling in UML while still using Petri Nets to perform the analysis. Multi Paradigm Modelling also provided a platform for a well-established theory in Petri Nets – synthesis to be adopted into Sequence Diagram as a method of putting-together different Sequence Diagrams based on a set of techniques and algorithms

Unveiling the Future of Education Exploring the Elements of a Smart Campus Ecosystem

A smart campus embodies a technologically advanced educational environment, often found within universities and colleges, where cutting-edge innovations and data-driven solutions converge to elevate multiple facets of campus life and academic pursuits. By seamlessly integrating state-of-the art technologies such as the Internet of Things (IoT), data analytics, automation systems, and digital communication platforms, a smart campus is designed to optimize efficiency, connectivity, and creativity, benefiting students, faculty, staff, and the broader academic community

A Business Process Modelling and Notation Meta-Model Approach to Enhance Prioritization for Decision-Making in Requirement Engineering

It is has always been the main focus of requirements engineer in making sure a set of optimal requirements is prepared in development of a project. With the current issue of getting the desired result, engineers would prioritize the set of requirements and utilize this to produce a list of optimal requirements. This paper will discuss some introduction of the evolution of software requirements prioritization, some related works, approach of conducting the research and finally discussing the expected result of this research. This will be the first step in the effort of translating Business Process Modelling (BPM) into its meaningful value to be used as a criterion in prioritizing Business Process (BP). During prioritizing BP, modelling usually provide decision-maker with only outcome of producing qualitative criterion without the basis of any facts and figures. The idea is to be able to derive a quantitative criterion from a model through the use of meta-modelling. The outcome of this research should be able to justify the need of prioritizing requirements based on its root, and that is business proces

A Question-Answering System that Can Count

This paper proposes a conceptual architectural design of Question- Answering (QA) system that can solve “counting” problem. Counting problem is the inability of QA system to produce numerical answer based on retrieved rationale (in text passage) containing list of items. For example, consider “How many items are on sale?” as question and “Currently shampoo, soap and conditioner are on sale” as retrieved rationale from text passage. Normally, system will produce “shampoo, soap and conditioner” as an answer while the ground truth answer is “three”. In other words, system is simply unable to perform the counting process needed in order to correctly answer such questions. To solve this problem, QA system architecture with following components is proposed: (1) A classifier to determine if given question requires a counting answer, (2) A classifier to determine if current system’s answer is not numeric, and (3) A counting method to produce numerical answer based on given rationale. Despite looking like a whole system, the proposed architecture is actually a modular system whereby each component can operate independently (allowing each component to be separately implemented by other systems). In essence, this paper intents to demonstrate a general idea of how the defined problem can be solved using a modular system, that hopefully also opens up more flexible enhancements in the future

Question classification of CoQa (QCOC) dataset

This paper proposes a new dataset for question classification process. Named QCoC (Question Classification of CoQA), this dataset is created based on Stanford’s CoQA (A Conversational Question Answering Challenge) dataset. The total of QCoC datapoint is 116630 (total of combined questionanswer pairs in CoQA training and evaluation dataset). Common question classification datasets are classifying question based on its paired answer’s knowledge (the semantic of answer’s context). For QCoC, classification is done differently that is per answer’s feature (semantic and syntactic of answer’s type). This paper discusses the question classification datasets, QA datasets, and justification of CoQA as selected base for QCoC. Then QCoC specification is discussed with class definition, classification method and result subsections. To the author’s knowledge, such dataset is still nonexistent to date. This paper suggests that this type of dataset is useful in solving abstractive answers issue in Question-Answering (QA) system. While factual answers can be directly produced by regular QA system, abstractive answers need some additional components. Although it is a recognizable issue, lack of suitable dataset perhaps is the reason why such direction is not being pursued. With QCoC dataset made publicly available1, hopefully such direction is open for further exploration

General Words Representation Method for Modern Language Model

This paper proposes a new word representation method emphasizes general words over specific words. The main motivation for developing this method is to address the weighting bias in modern Language Models (LMs). Based on the Transformer architecture, contemporary LMs tend to naturally emphasize specific words through the Attention mechanism to capture the key semantic concepts in a given text. As a result, general words, including question words are often neglected by LMs, leading to a biased word significance representation (where specific words have heightened weight, while general words have reduced weights). This paper presents a case study, where general words' semantics are as important as specific words' semantics, specifically in the abstractive answer area within the Natural Language Processing (NLP) Question Answering (QA) domain. Based on the selected case study datasets, two experiments are designed to test the hypothesis that "the significance of general words is highly correlated with its Term Frequency (TF) percentage across various document scales”. The results from these experiments support this hypothesis, justifying the proposed intention of the method to emphasize general words over specific words in any corpus size. The output of the proposed method is a list of token (word)- weight pairs. These generated weights can be used to leverage the significance of general words over specific words in suitable NLP tasks. An example of such task is the question classification process (classifying question text whether it expects factual or abstractive answer). In this context, general words, particularly the question words are more semantically significant than the specific words. This is because the same specific words in different questions might require different answers based on their question words (e.g. "How many items are on sale?" and "What items are on sale?" questions). By employing the general weight values produced by this method, the weightage of question and specific words can be heightened, making it easier for the classification system to differentiate between these questions. Additionally, the token (word)-weight pair list is made available online at https://www.kaggle.com/datasets/saliimiabbas/genwords-weight

A Systematic Survey on the Research of AI-predictive Models for Wastewater Treatment Processes

Context: To increase the efficiency of wastewater treatment, modeling and optimization of pollutant removal processes are the best solutions. The relationship between input and output parameters in wastewater treatment processes (WWTP) is a complicated one, and it is difficult for designing models using statistics. Artificial Intelligence (AI) models are generally more flexible when compared with statistical models while modeling complex datasets with nonlinearity and missing data. Objective: Studies on WWTP of AI-based are increasing day by day. Therefore, it is crucial to systematically review the AI techniques available which are implemented for WWTP. Such kind of review helps for classifying the techniques that are invented and helps to identify challenges as well as gaps for future studies. Lastly, can sort out the best AI technique to design predictive models for WWTP. Method: With the help of the most relevant digital libraries, the total number of papers collected is 1222 which are based on AI modeling on WWTP. Then the filtration of the papers is mainly based on the inclusion and exclusion criteria. Also, to identify new relevant papers, snowballing is the other technique applied. Results: Finally selected 76 primary papers to reach the result were published between 2004 and 2020. Conclusion: ANN with MLP approach on BP algorithm become a supervised neural network called BPNN is the most used AI modeling for WWTP and around 40% of the experimental research done with BPNN. Then there are some limitations on AI modeling of WWTP using photoreforming which is the current study of WWTP represents a promising path for generating renewable and sustainable energy resources like chemicals and fuels

Blockchain-enabled Secure Privacy-preserving System for Public Health-center Data

Health center data implicates a large scale of individual health records and is immensely concealment sensory. In the virtual era of large-size data, the increasingly different health informatization causes it important that health data needs to be stored precisely and securely. However, daily health data transactions carry the risk of privacy leaks that make sharing difficult. Moreover, the recently permitted blockchain applications suffer from deficient performance and lack of privacy. This study presents a privacy-preserving and secure sharing and storage system for public health centers based on the blockchain method to dispose of these issues. This system utilizes a hash-256-based access controller and transaction signature with the consensus policy and provides security to share and store health data in the blockchain. In this approach, blockchain guarantees scalability, privacy, integrity, and availability for data retention. Also, this paper measures the performance of transactions with supporting confidentiality-preserving and shows the average transaction time and acceptable latency when accessing health data

Software defined internet of things in smart city: A review

The concept of smart cities has gained traction to enhance citizens’ quality of life amidst rapid urbanization. Integration of the Internet of Things (IoT) is a key component that allows for gathering real-time data to inform decision-making and drive innovation in urban planning and management. However, managing the amount of data generated and the IoT devices rapid growth poses a challenge that leads to network management, interoperability, security, and scalability issues in smart cities. To overcome such problems, integrating Software Define Networking (SDN) in IoT provides a flexible, scalable, and efficient network architecture that can better support the unique demands of IoT devices and applications. Motivated by the extensive research efforts in the Software Defined Internet of Things (SDIoT), this paper aims to review SDIoT implementation in smart cities. It first introduces the underlying technology along with various practical applications of SDIoT. The comprehension of SDIoT in smart cities focus on IoT application requirements, including interoperability, scalability, low latency requirement, handling of big data, security, and privacy, energy consumption, Quality of Service (QoS), and task offloading. The paper concludes by discussing the future research directions that need to be examined in greater depth