Annals of Data Science最新文献

This paper introduces the concept of system of systems (SoS) and the challenges ahead to extend systems engineering (SE) to system of systems engineering. The birth of a new engineering field may be on the horizon - System of Systems Engineering (SoSE). A SoS is a collection of individual, possibly heterogeneous, but functional systems integrated together to enhance the overall robustness, lower the cost of operation, and increase reliability of the overall complex (SoS) system. Having said that the field has large vacuum from basic definition, to theory, to management and implementation. Many key issues like architecture, modeling, simulation, identification, emergence, standards, net-centricity, control, etc. are all begging for attention. In this review paper, we will be going through all these issues briefly and bring out the challenges to the attention of interested readers. This paper is based on a keynote presented at the ITQM International Conference, Bucharest, Romania, August 23-24, 2024

Dataset lightweighting is the process of constructing a small dataset from a large original dataset to reduce the pressure of data storage, distribution, and model training. Dataset distillation(DD) is an emerging dataset lightweight method in recent years. This paper mainly focuses on DD problem. The mainstream algorithms of DD can be divided into several categories: meta learning framework, parameter matching framework, distribution matching framework and generative model-based DD. In this paper, we provide a comprehensive review of the distillation methods of datasets since 2018 in the above order. At the end of the article, we present some discussions on the current state of development in this field and suggest some potential future research directions.

Traditional feasibility evaluation of power grid engineering projects primarily focuses on either individual or overall evaluations. The existing approach overlooks the identification and assessment of cluster projects spanning the feasibility and implementation phases, resulting in resource wastage and schedule stagnation throughout the project management lifecycle. To address this issue, this study proposes a methodology based on knowledge graphs to align the physical elements of completed projects and those under feasibility study. This paper (i) defines the Power Grid Engineering Knowledge Graph from the perspective of physical elements (PGKG), (ii) extracts information on physical elements from feasibility study reports and constructs the initial topology of physical element nodes, and (iii) designs two physical element alignment algorithms for node - structure and node - line alignment. Finally, the two algorithms are tested on the constructed PGKG, and ablation experiments are conducted to verify their effectiveness and rationality. This paper offers a reliable method for constructing the power grid engineering knowledge graph and a physical element alignment method to tackle the problems of information deficiency and misalignment of spatio - temporal information in power grid engineering knowledge graphs.

In retrospect and over the past eighty years, it appears that the author has tracked a somewhat consistent pattern in his educational, employment and investment pursuits - all centered on the theme of decision sciences and engineering systems (DSES). Indeed, the author’s papers, lectures, speeches, and range of academic and work activities have likewise tracked such a DSES theme. Since an early age, the author came to realize that his interest in mathematics served to help him make consistent sense of his decisions about life and his various pursuits; he further understood that life itself is intertwined in a number of different engineered systems, connected with and in support of each other. Certainly, each person adopts a somewhat different personal journey in their DSES quest; this paper, of course, documents the author’s own unique journey.

This article discusses the interconnectivity of financial management abilities, managerial abilities, and funding methods in propelling company growth and performance. This study mimics different financing options (debt, equity, internal capital, trade credit, and others) using Multi-Agent-Based Modeling (MABM) to examine their effects under dynamic market scenarios. The study shows that long-term growth and competitive resilience are facilitated by a balanced finance strategy supported by efficient financial management and effective leadership. The simulation shows how firms adjust to economic transformation and balance between risk and potential growth through various strategies. The results offer the efficacy with which MABM illustrates the complexity of financial strategy selection in uncertain markets and demonstrates emergent patterns in systemic risk, firm survival, and aggregate growth, yielding new insight into robust financing strategies.

The paper addresses three concepts namely enlightenment, humanism and collaboration in the context of a human being’s quest for new knowledge and perennial values. Established relationships among people and their evolution are impacted by Artificial Intelligence associated with other main technology drivers of the digital transformation such as the Internet and Big Data analytics. The basic aspects that characterize the three above concepts are reviewed and a brief and up-to-date technical account of the Artificial Intelligence domain is provided together with the steps envisaged toward a trustworthy and anthropocentric schema. The current beneficial as well as detrimental impacts of the current usage of the Internet, Big Data and Artificial Intelligence are reviewed together with various opinions of influential scientists and industry leaders and the updated concepts of the enlightenment, humanism, and collaboration in the digital context are eventually presented.

With the intensification of global population aging, the incidence of cognitive impairment such as dementia continues to rise. The Activity of Daily Living (ADL) scale can help to assess daily living functions and early screen for dementia, which is crucial for delaying disease progression and improving the quality of life in older adults. As advanced ADL assessment tools continue to be developed and improved, how to evaluate their effectiveness is particularly important. However, most studies have assessed these tools from a single perspective and have often failed to examine the contribution of individual items within the scales. Therefore, we propose the Statistical and Machine Learning-based Advanced ADL scale Effectiveness Assessment Method (SML-AAEA) to evaluate the psychometric properties and early dementia screening ability of ADL assessment tool, including: (1) scale design and data collection based on traditional scales and advanced items; (2) analysis of scale validity and reliability; and (3) analysis of scale dementia diagnostic ability and item importance using machine learning. We then apply SML-AAEA to investigate the effectiveness of our proposed Advanced ADL scale for Early Dementia Screening (AADLs-EDS), which introduces three new advanced items, namely “Going far away”, “Online shopping” and “Using smartphone”. The results show that AADLs-EDS has excellent construct validity, measurement invariance, and scale reliability. The total score of AADLs-EDS can explain the changes in elderly cognitive functions to some extent. The study also finds that AADLs-EDS outperforms the traditional ADL scale in classifying dementia, with the three new items showing the strongest predictive contributions. The findings confirm that AADLs-EDS is a reliable and valid tool for early dementia screening.

In the current era of information abundance, data mining and strategy generation have become crucial. Although decision trees are widely used across various domains, their direct application to obtain optimal solutions often lacks the flexibility and precision needed to adapt update themselves under specific conditions. This paper introduces an enhanced data mining algorithm, the Transformation Tree, which addresses these limitations by systematically comparing parameter variables under diverse conditions. The transformation tree algorithm builds on the traditional decision tree methodology to identify optimal transformation schemes, delivering precise and adaptable solutions. The algorithm employs basic-element theory to extract data features, evaluates them using information gain ratio and the comprehensive gain ratio metrics, and constructs a transformation tree structure. This structure facilitates the generation and refinement of transformation schemes and strategies through iterative testing. To validate its effectiveness, we applied the algorithm to a hypertension case study. The results indicate that while the transformation tree algorithm exhibits slightly lower efficiency in classification tasks, it excels in discovering multiple transformation strategies, enabling intelligent scheme generation, and providing flexible and accurate decision support.

With the social economy growing at a quick pace and the stock market seeing constant developments, more and more people are voicing concerns about investing in stocks. The importance of forecasting stock values has increased in the domain of engineering's use of cognitive computing. Utilizing data-driven tactics for forecasting stock prices, investors can effectively mitigate risks and enhance profits. Investors can use projections based on historical values and textual data to make well-informed judgments about future patterns in stock prices. Stock price anticipation is a pivotal undertaking in the financial sector that has substantial consequences for traders and investors. This article presents an in-depth comparison analysis of machine learning tactics for forecasting price fluctuations in stocks. The research deploys historical stock data and diverse technical indicators. This paper presents the Gated Recurrent Unit (GRU) model for Nasdaq stock index anticipation, which is optimized by Particle swarm optimization (PSO), Biogeography-based optimization (BBO), and Moth flame optimization (MFO). Among these optimizers, MFO has the best outcomes. Compared to the GRU scheme the optimized PSO-GRU, BBO-GRU, and MFO-optimized GRU for stock forecasting has the outcomes of 0.9807, 0.9824, and 0.9904 in coefficient of determination (({R}^{2})) which shows the improvement of the presented scheme as a result of its development. The criteria used to evaluate this model are mean absolute error, root mean absolute error, and ({R}^{2}).