Current AI system development processes often follow a fragmented approach, where non-ML and ML components are typically developed in isolation. This separation poses significant challenges, particularly in managing changes effectively. Our study delves into these challenges, highlighting how the lack of co-development can obscure and exacerbate issues within the change management process. Through an in-depth exploratory case study, we meticulously model, execute, and monitor the current (As-Is) development process. Our findings lay the groundwork for an integrated future (To-Be) process, aimed at enhancing change management practices
Today, many software applications have been developed based on a microservices architecture composed of multiple components that can be updated individually without affecting the entire application. Such applications can be deployed on Kubernetes clusters which provide flexibility, scalability, and ease of management. State-of-the-art strategies for deploying microservices in Kubernetes clusters, such as ramped, bluegreen, canary, A/B, or shadow strategy are designed with high availability, stability, and easy rollback. However, these strategies are not energy-efficient because they require keeping both the original and the update version of the application running simultaneously. In this paper, we address this issue by proposing a solution to reduce the update time of microservices applications, thereby decreasing the interval of time in which two versions of the application on the infrastructure. We design an optimized scheduling mechanism for updating microservices components as a Mixed Integer Linear Programming (MILP) model. We mathematically formulate an energy consumption model for the update process and then propose a Greedy algorithm to find the solution in real-time. Simulation results show that our Greedy algorithm approximates the optimal solution provided by a mathematical solver in only a fraction of time.
In recent years, Legal Judgment Prediction (LJP) has attracted a lot of interest from both academic and legal practitioners. Generally, LJP research has three subtasks, i.e., applicable law article prediction, charge/judgment prediction, and term of penalty prediction. Among those three subtasks, the majority of LJP research focuses on charge prediction as one of three tasks of LJP. Charge prediction itself is a single label prediction problem. Unlike the majority, this research proposes a multi-label classification on LJP that predicts violations of relevant law articles in a legal case with the help of Natural Language Processing (NLP). Data used in this research is collected from the publicly accessible legal document in the European Court of Human Rights (ECHR). In detail, the data includes “facts” of each legal case that describes how the events have occurred. Legal BERT (LBERT) embedding is utilized to generate word embedding and Label Powerset is used for problem transformation. Lastly, we apply conventional Machine Learning (ML) classifiers such as Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) to predict which relevant law articles have been violated. In total, there are 13 law articles in the ECHR dataset. The proposed model achieves over 86.51% precision, 81.77% recall, and 82.89% F1score on ECHR dataset.
The parameterized knowledge within large language models (LLMs), like ChatGPT, offers a significant opportunity for modelling domain knowledge base from text. However, LLMs' context sensitivity can hinder obtaining precise and taskaligned outcomes, thus requiring a suitable design for leveraging prompt engineering. This study explores the efficacy of different prompting methods for RDF knowledge graph construction from medical documents as our preliminary investigation, aiming to develop an efficient pipeline for a large-scale automatic knowledge graph construction according to semantic web standards and technologies. The results show that leveraging in-context learning within LLMs is capable of extracting an array of precise RDF triples from text. We perform a qualitative analysis of the extracted triples with different prompt templates, giving insights that could guide potential development in the research field.
Happiness relates to life satisfaction and can impact an individual's activities in daily life. For students, it impacted their learning activities in and outside the school environment. The report by OECD in 2019 has indicated some East Asian countries and economies did not achieve the average life-satisfaction. Despite not achieving the average lifesatisfaction score, East Asian students has been top performers in Programme for International Student Assessment (PISA). This paper focuses on studying aspects related to happiness with respect to the academic achievement of East Asian students in PISA. The data from PISA 2018 is extracted, integrated and analyzed to identify factors that may have impact towards happiness among East Asian students. Secondly is to ascertain the relationship between level of happiness and academic achievements across Reading, Math and Science domains in PISA. The main findings reveal that the top three correlated factors are frequency of being happy, subjective well-being and frequency of being joyful with each of the three factors having a correlation value of above 0.4 which indicate positive correlation with happiness. From this study it is suggested that activities in and outside the school environment that led to the top 3 correlated factors can be planned to inculcate a sense of happiness among students.
