The purpose of this article is to illustrate an investigation of methods that can be effectively used to predict the data incompleteness of a dataset. Here, the investigators have conceptualized data incompleteness as a random variable, with the overall goal behind experimentation providing a 360-degree view of this concept conceptualizing incompleteness of a dataset both as a continuous, discrete random variable depending on the aspect of the required analysis. During the course of the experiments, the investigators have identified Kolomogorov–Smirnov goodness of fit, Mielke distribution, and beta distributions as key methods to analyze the incompleteness of a dataset for the datasets used for experimentation. A comparison of these methods with a mixture density network was also performed. Overall, the investigators have provided key insights into the use of methods and algorithms that can be used to predict data incompleteness and have provided a pathway for further explorations and prediction of data incompleteness.

Let $\mathsf{T}[1,n]$ be a string of length n and $\mathsf{T}[i,j]$ be the substring of $\mathsf{T}$ starting at position i and ending at position j. A substring $\mathsf{T}[i,j]$ of $\mathsf{T}$ is a repeat if it occurs more than once in $\mathsf{T}$; otherwise, it is a unique substring of $\mathsf{T}$. Repeats and unique substrings are of great interest in computational biology and information retrieval. Given string $\mathsf{T}$ as input, the Shortest Unique Substring problem is to find a shortest substring of $\mathsf{T}$ that does not occur elsewhere in $\mathsf{T}$. In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over $\mathsf{T}$ answering the following type of online queries efficiently. Given a range $[\alpha ,\beta ]$, return a shortest substring $\mathsf{T}[i,j]$ of $\mathsf{T}$ with exactly one occurrence in $[\alpha ,\beta ]$. We present an $\mathcal{O}(nlogn)$-word data structure with $\mathcal{O}\left({log}_{w}n\right)$ query time, where $w=\Omega (logn)$ is the word size. Our construction is based on a non-trivial reduction allowing for us to apply a recently introduced optimal geometric data structure [Chan et al., ICALP 2018]. Additionally, we present an $\mathcal{O}\left(n\right)$-word data structure with $\mathcal{O}\left(\sqrt{n}{log}^{ϵ}n\right)$ query time, where $ϵ>0$ is an arbitrarily small constant. The latter data structure relies heavily on another geometric data structure [Nekrich and Navarro, SWAT 2012].

Machine learning operations, or MLOps, are a set of practices to deploy and maintain machine learning models in production reliably and efficiently. This paper evaluates and compares open-source MLOps tools as a whole through GitHub data. Unsupervised machine learning models are implemented to find topics from repository descriptions and uses these topics to create clusters of repositories. From this analysis, the authors found the MLOps space is dominated by educational and cloud-platform-related content and is growing quickly, especially in the Python language. This paper aims to understand better the available open-source tools at a high level, looking at the types of tools that exist and how they might impact the adoption of MLOps.

Leveraging Artificial Intelligence (AI) ethically in connected healthcare systems requires a quantifiable framework that measures not only outcome correctness, but also the clarity, auditability, and ethical acceptability of model explanations in high-stakes clinical and cybersecurity workflows. This manuscript first presents a narrative review of ethical risks and countermeasures in Healthcare Internet of Things (HIoT) and explains why existing performance metrics are insufficient for trustworthy deployment. We then formalize a quantitative metric called Ethical Explainability (E_e) as a composite index integrating (1) a Human Agreement Ratio (HAR), capturing concordance between AI recommendations (and their rationale) and a calibrated expert consensus, and (2) an Entropy Reduction Index (ERI), capturing the proportional reduction in expert uncertainty after receiving an explanation, operationalized via probability-elicitation questionnaires mapped to Shannon entropy. Designed for HIoT security monitoring, E_e links transparency with governance-ready evidence of trustworthiness for human–AI collaboration.

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing.