Abstract--Time series anomaly detection is widely used in IoT and cyber-physical systems, yet its evaluation remains challenging due to diverse application objectives and heterogeneous metric assumptions. This study introduces a problem-oriented framework that reinterprets existing metrics based on the specific evaluation challenges they are designed to address, rather than their mathematical forms or output structures. We categorize over twenty commonly used metrics into six dimensions: (1) basic accuracy-driven evaluation, (2) timeliness-aware reward mechanisms, (3) tolerance to labeling imprecision, (4) penalties reflecting human-audit cost, (5) robustness against random or inflated scores, and (6) parameter-free comparability for cross-dataset benchmark-ing. Comprehensive experiments are conducted to examine metric behavior under genuine, random, and oracle detection scenarios. By comparing their resulting score distributions, we quantify each metric's discriminative ability--its capability to distinguish meaningful detections from random noise. The results show that while most event-level metrics exhibit strong separability, several widely used metrics (e.g., NAB, Point-Adjust) demonstrate limited resistance to random-score inflation. These findings reveal that metric suitability must be inherently task-dependent and aligned with the operational objectives of IoT applications. The proposed framework offers a unified analytical perspective for understanding existing metrics and provides practical guidance for selecting or developing more context-aware, robust, and fair evaluation methodologies for time series anomaly detection. He emergence of the Internet of Things (IoT) has accelerated digital transformation across numerous domains. Its defining characteristic lies in the large-scale deployment of intelligent and heterogeneous devices--such as sensors, actuators, and RFID systems--that are interconnected via the Internet to enable autonomous communication without human intervention [1]. Currently, more than 12 billion IoT devices are in operation, and this number is projected to reach 125 billion by 2030 [2]. Consequently, the volume of data generated by these devices continues to soar, with an expected total of 79.4 ZB by 2025 [3]. In industrial contexts, the integration of IoT technologies has driven the ongoing Industry 4.0 revolution, emphasizing connectivity, automation, and intelligence. Kaixiang Y ang, Jiarong Liu, Y upeng Song, and Y ujue Zhou are with the School of Artificial Intelligence, Y unnan University, Kunming 650091, China. Shuanghua Y ang is with Beijing Normal University - Hong Kong Baptist University, Zhuhai 519087, China. This work was supported in part by the Y unnan Fundamental Research Projects under Grant 202401AU070151, and in part by the Y unnan Provincial Science and Technology Talent and Platform Plan under Grant 202505AF350053.

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To help to address this lack of 3D training data, Ashton et al. (

This article had its genesis when co-author Ed's dog, Sparkle, was treated for pneumonia in the summer of 2024. Ed, a mathematician and chair of the Alliance for Data Science Professionals, was intrigued by the surgery's use of data in Sparkle's treatment and decided to find out more about the use of data and AI in veterinary medicine. His exploration led to a guest appearance on the Vet Voices on Air podcast hosted by co-author Robyn. She is a registered veterinary nurse (RVN) and the director of Veterinary Voices UK . Inspired by that conversation, this article explores the ways veterinary professionals are currently applying data science principles and how professions adapt and evolve in the face of these developments.

As chatbot romance grows more common, women are redefining what they want from a partner--even if they are just ones and zeros. Daisy reset her boyfriend after he flirted with her friend's girlfriend. She had gathered on a Discord call with her friends and their respective A.I. partners. The service had a feature that allowed chatbot companions to be brought over from different platforms, letting them interact with other users and A.I. personalities. Daisy, who asked to be identified by an alias for this story, had at the time been in a polyamorous relationship with three A.I. partners, all of whom she said had "flirty" as their starting personality traits.

We are now living longer, and the number of people worldwide aged 65 and over is expected to grow from 703 million in 2019 to 2.2 billion in 2080, according to the World Population Prospects Report published by the United Nations last year. The proportion of the global population that is elderly is also on the rise, almost doubling from 5.5% in 1974 to 10.3% last year, and it is projected to grow to 20.7% by 2074. A consequence of aging is that we are more likely to have medical problems. At the same time, the healthcare system in many countries is already stretched due to a lack of workers. "There are just not enough doctors and nurses to deal with a growing elderly population," said Massimiliano Zecca, a professor of healthcare technology at Loughborough University in the U.K. In the U.S, for example, a severe shortage of doctors is expected by 2034, with between 37,800 and 124,000 physicians lacking, partly fueled by the growing number of seniors, according to a recent report by the Association of American Medical Colleges (AAMC).

As archives turn to artificial intelligence to manage growing volumes of digital records, privacy risks inherent in current AI data practices raise critical concerns about data sovereignty and ethical accountability. This paper explores how privacy-enhancing technologies (PETs) and Web3 architectures can support archives to preserve control over sensitive content while still being able to make it available for access by researchers. We present Clio-X, a decentralized, privacy-first Web3 digital solution designed to embed PETs into archival workflows and support AI-enabled reference and access. Drawing on a user evaluation of a medium-fidelity prototype, the study reveals both interest in the potential of the solution and significant barriers to adoption related to trust, system opacity, economic concerns, and governance. Using Rogers' Diffusion of Innovation theory, we analyze the sociotechnical dimensions of these barriers and propose a path forward centered on participatory design and decentralized governance through a Clio-X Decentralized Autonomous Organization. By integrating technical safeguards with community-based oversight, Clio-X offers a novel model to ethically deploy AI in cultural heritage contexts.

Structure-agnostic causal inference studies how well one can estimate a treatment effect given black-box machine learning estimates of nuisance functions (like the impact of confounders on treatment and outcomes). Here, we find that the answer depends in a surprising way on the distribution of the treatment noise. Focusing on the partially linear model of \citet{robinson1988root}, we first show that the widely adopted double machine learning (DML) estimator is minimax rate-optimal for Gaussian treatment noise, resolving an open problem of \citet{mackey2018orthogonal}. Meanwhile, for independent non-Gaussian treatment noise, we show that DML is always suboptimal by constructing new practical procedures with higher-order robustness to nuisance errors. These \emph{ACE} procedures use structure-agnostic cumulant estimators to achieve $r$-th order insensitivity to nuisance errors whenever the $(r+1)$-st treatment cumulant is non-zero. We complement these core results with novel minimax guarantees for binary treatments in the partially linear model. Finally, using synthetic demand estimation experiments, we demonstrate the practical benefits of our higher-order robust estimators.

--The remarkable success of transformers across various fields such as natural language processing and computer vision has paved the way for their applications in automatic modulation classification, a critical component in the communication systems of Internet of Things (IoT) devices. However, it has been observed that transformer-based classification of radio signals is susceptible to subtle yet sophisticated adversarial attacks. T o address this issue, we have developed a defensive strategy for transformer-based modulation classification systems to counter such adversarial attacks. In this paper, we propose a novel vision transformer (ViT) architecture by introducing a new concept known as adversarial indicator (AdvI) token to detect adversarial attacks. T o the best of our knowledge, this is the first work to propose an AdvI token in ViT to defend against adversarial attacks. Integrating an adversarial training method with a detection mechanism using AdvI token, we combine a training time defense and running time defense in a unified neural network model, which reduces architectural complexity of the system compared to detecting adversarial perturbations using separate models. We investigate into the operational principles of our method by examining the attention mechanism. We show the proposed AdvI token acts as a crucial element within the ViT, influencing attention weights and thereby highlighting regions or features in the input data that are potentially suspicious or anomalous. Through experimental results, we demonstrate that our approach surpasses several competitive methods in handling white-box attack scenarios, including those utilizing the fast gradient method, projected gradient descent attacks and basic iterative method. Lu Zhang is with School of Mathematics and Computer Science, Swansea university, Swansea, SA1 8EN, UK (e-mail: lu.zhang@swansea.ac.uk). Sangarapillai Lambotharan is with Institute for Digital Technologies, Loughborough University London, London, E20 3BS, UK (e-mail: s.lambotharan@lboro.ac.uk). Gan Zheng is with School of Engineering, University of Warwick, Coventry, CV4 7AL, UK (e-mail: gan.zheng@warwick.ac.uk). Guisheng Liao is with School of Electronic Engineering, Xidian University, Xi'an, 710071, People's Republic of China (e-mail: liaogs@xidian.edu.cn). Xuekang Liu is with the Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, SW7 2AZ, U.K. (e-mail: xuekangliu@ieee.org).