Evaluation of Machine Learning Algorithm on Drinking Water Quality for Better Sustainability

Water has become intricately linked to the United Nations\u27 sixteen sustainable development goals. Access to clean drinking water is crucial for health, a fundamental human right, and a component of successful health protection policies. Clean water is a significant health and development issue on a national, regional, and local level. Investments in water supply and sanitation have been shown to produce a net economic advantage in some areas because they reduce adverse health effects and medical expenses more than they cost to implement. However, numerous pollutants are affecting the quality of drinking water. This study evaluates the efficiency of using machine learning (ML) techniques in order to predict the quality of water. Thus, in this paper, a machine learning classifier model is built to predict the quality of water using a real dataset. First, significant features are selected. In the case of the used dataset, all measured characteristics are chosen. Data are split into training and testing subsets. A set of existing ML algorithms is applied, and the results are compared in terms of precision, recall, F1 score, and ROC curve. The results show that support vector machine and k-nearest neighbor are better according to F1-score and ROC AUC values. However, The LASSO LARS and stochastic gradient descent are better based on recall values

If it ain’t broke, don’t fix it : An Abductive and Contextual Exploration of Maintenance Deferral

Objective: To create academic insights into how organisations approach and manage the maintenance of vendor-supplied information systems software. Approach: Three iterations of the Peircean Abduction methodology lead to the identification, conceptualisation, and application of new knowledge in vendor-supplied Information Systems (IS) maintenance deferral by means of undertaking a qualitative multiple-case study. The research goals are achieved through the appropriation and application of theories from Peircean Abduction and Systemic Functional Linguistics. Research questions: The following abductive statement is created through the application of the Peircean Abduction methodology: The surprising observation, “some organisations, having invested in a vendor-supplied IS software solution, defer the implementation of vendor-supplied maintenance”, is made; However, if “the existence of deterrents to maintenance, requiring a trigger event before the implementation of maintenance” were true, then “maintenance deferral” would be a matter of course. Hence there is a reason to suspect that “the existence of both deterrents, and of triggers” is true. From this abductive statement, three research questions are deduced. The first research question investigates the existence, characteristics and influence of deterrents; the second question investigates the existence, characteristics and influence of triggers. As a consequence of this approach, the final question provides a general understanding of IS maintenance deferral. Methodology: Following the implementation of a systematic literature review methodology, six themes are identified: 1. an acknowledgement that problems exist when considering vendor-supplied software maintenance; 2. deterrents as a driver in behaviour; 3. the occurrence of tipping-points which require vendor-supplied maintenance to be undertaken; 4. the consequences of deferral; 5. the value of maintenance; and 6. the formalisation of a maintenance lifecycle. Taking the insights arising from the systematic literature review, a multiple-case study following the pragmatic framework is constructed from data collected interviewing twelve participants across a diverse set of ten organisations. An abductive approach to this research topic creates opportunities for a comprehensive, well-grounded exploratory contribution to a scarcely investigated research domain. Major findings: The translation of Peircean abduction to an interpretative context generates a rich and substantive contribution to theory and practice. The existence of both deterrents and triggers are strongly supported, leading to the conclusion that maintenance deferral is a matter of course. The development of a new abductive and Systemic Functional Linguistic model enhances the knowledge of maintenance deferral and allows refinement of historical IS maintenance models. Finally, the application of Systems Thinking situates insights from the application of their mode within their respective organisational environments

MAPPING BPEL PROCESSES TO DIAGNOSTIC MODELS

Web services are loosely-coupled, self-contained, and self-describing software modules that perform a predetermined task. These services can be linked together to develop an appli­ cation that spans multiple organizations. This linking is referred to as a composition of web services. These compositions potentially can help businesses respond more quickly and more cost-effectively to changing market conditions. Compositions can be specified using a high- level workflow process language. A fault or problem is a defect in a software or software component. A system is said to have a failure if the service it delivers to the user deviates from compliance with the system specification for a specified period of time. A problem causes a failure. Failures are often referred to as symptoms of a problem. A problem can occur on one component but a failure is detected on another component. This suggests a need to be able to determine a problem based on failures. This is referred to as fault diagnosis. This thesis focuses on the design, implementation and evaluation of a diagnostic module that performs automated mapping of a high-level specification of a web services composition to a diagnostics model. A diagnosis model expresses the relationship between problems and potential symptoms. This mapping can be done by a third party service that is not part of the application resulting from the composition of the web services. Automation will allow a third party to do diagnosis for a large number of compositions and should be less error-prone

Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real-world applications. For instance, OpenAI devoted six months to iteratively aligning GPT-4 before its release [3]. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. This obstacle hinders systematic iteration and deployment of LLMs. To address this issue, this paper presents a comprehensive survey of key dimensions that are crucial to consider when assessing LLM trustworthiness. The survey covers seven major categories of LLM trustworthiness: reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness. Each major category is further divided into several sub-categories, resulting in a total of 29 sub-categories. Additionally, a subset of 8 sub-categories is selected for further investigation, where corresponding measurement studies are designed and conducted on several widely-used LLMs. The measurement results indicate that, in general, more aligned models tend to perform better in terms of overall trustworthiness. However, the effectiveness of alignment varies across the different trustworthiness categories considered. This highlights the importance of conducting more fine-grained analyses, testing, and making continuous improvements on LLM alignment. By shedding light on these key dimensions of LLM trustworthiness, this paper aims to provide valuable insights and guidance to practitioners in the field. Understanding and addressing these concerns will be crucial in achieving reliable and ethically sound deployment of LLMs in various applications