Singapore

The progress of Anomaly Detection (AD) in safety-critical 
domains, such as transportation, is severely constrained 
by the lack of large-scale, real-world benchmarks. 
To address this, we introduce EngineAD, a novel, 
multivariate dataset comprising high-resolution sensor 
telemetry collected from a fleet of 25 commercial vehicles 
over a six-month period. Unlike synthetic datasets, 
EngineAD features authentic operational data labeled 
with expert annotations, distinguishing normal states from 
subtle indicators of incipient engine faults. We preprocess 
the data into 300-timestep segments of 8 principal 
components and establish an initial benchmark using 
nine diverse one-class anomaly detection models. 
Our experiments reveal significant performance variability 
across the vehicle fleet, underscoring the challenge of 
cross-vehicle generalization. Furthermore, our findings 
corroborate recent literature, showing that simple 
classical methods (e.g., K-Means and One-Class SVM) 
are often highly competitive with, or superior to, deep 
learning approaches in this segment-based evaluation. 
By publicly releasing EngineAD, we aim to provide a 
realistic, challenging resource for developing robust 
and field-deployable anomaly detection and anomaly 
prediction solutions for the automotive industry.

AAAI 2026

EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset

automotive sensor data

real-world benchmark dataset

multivariate time series

anomaly detection

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Global climate change has increased the frequency of 
rainfall-induced disasters, such as landslides, even in regions 
previously considered safe, highlighting the need for accurate 
and region-generalizable prediction systems.
Landslide datasets are inherently imbalanced and regionally 
heterogeneous, as events are rare and local factors such as 
rainfall patterns, soil properties, and vegetation strongly 
influence occurrence.
Existing approaches either rely on physically grounded 
anomaly detection models, which ensure reasonable 
predictions in data-scarce regions but may not capture 
complex spatiotemporal patterns, or on deep learning 
models, which excel at representing complex patterns but 
often misinterpret rare yet safe conditions.
To leverage the complementary strengths of these 
approaches, we propose a hybrid framework that distills 
knowledge from physical anomaly detection models and 
incorporates region-conditioned supervised contrastive 
learning to learn latent representations that generalize across 
regions.The framework employs a three-layer tank model to 
simulate the Soil Water Index and quantify rainfall deviations, 
and jointly trains a prediction model with both physical outputs 
and actual disaster labels.
By integrating physically interpretable cues with deep 
representations, the system effectively addresses the scarcity 
and regional heterogeneity of landslide data.
We evaluated the framework on rainfall and terrain data from 
22 regions across Japan using a Leave-K-Region-Out scheme.
The proposed method achieved a 50\% higher PR-AUC in unseen 
regions compared to state-of-the-art baselines, demonstrating its 
potential for region-agnostic and physically grounded 
landslide prediction.

Leveraging Physics Anomaly 
Knowledge and Contrastive 
Learning for Region-Agnostic 
Landslide Prediction

Surveillance video captioning aims to generate natural language 
descriptions for security video and plays a crucial role in 
applications such as anomaly detection and behavior analysis. 
While vision-language models (VLMs) perform well on general 
video captioning, they often struggle in surveillance scenarios 
due to domain discrepancies in visual style and content 
emphasis. Moreover, reliable evaluation of caption quality in 
this domain also remains challenging. We propose a 
comprehensive framework that addresses both domain 
adaptation and evaluation. For domain adaptation, we enhance 
VLMs via reinforcement fine-tuning, exploring multiple reward 
designs to optimize relevance and accuracy while helping to
improve out-of-domain generalization. For evaluation, we 
develop an LLM-as-a-Judge process that assesses generated 
descriptions from two complementary perspectives: a local 
semantic score capturing fine-grained action fidelity and a global 
quality score evaluating linguistic fluency and temporal 
coherence. Experimental results show that our domain adaptation 
approach outperforms both zero-shot and supervised fine-tuning
baselines. Ablation studies further validate the effectiveness of 
our reward design, training strategy, and out-of-domain 
generalization. In addition, empirical analyses show that the 
proposed LLM-as-a-Judge framework aligns closely with human 
evaluations and exhibits stable and robust performance across 
different conditions.

Domain-Adaptive Video 
Captioning for Surveillance 
Videos

Learning additional reward signals---beyond the intrinsic rewards 
from the environment---can guide the agent more quickly toward 
optimal policies during training.
Recent work suggested training autoregressive models to 
maximize the likelihood of states observed in expert trajectories.
States that are likely then render a positive reward.
Such methods closely align with anomaly detection (AD), where 
the task is to find observations that deviate significantly from 
typical behavior.
We frame reward generation as an AD problem, generalizing 
existing techniques and proposing a novel methodology for 
learning reward signals. 
Specifically, we train state-of-the-art AD models capable of 
recognizing normal behavior from expert observations while 
actively learning to detect anomalous behavior from the agent's
sub-optimal observations, thus improving the reward learning 
process. We show that recent autoregressive reward-learning 
methods are, in fact, implementations of basic AD methods. 
Empirically, we show the effectiveness of the proposed reward
function across various tasks.


Anomaly-driven 
Reinforcement Learning

Anomaly detection (AD) for safety-critical IoT time series should be 
judged at the event level—by reliability and earliness under 
realistic perturbations. Yet many studies still emphasize point-level 
results on base form of curated public datasets, which limits their
value for model selection in practice. We introduce a unified 
evaluation protocol that adds event-level augmentation simulating 
real-world problems by (i) adds a calibrated sensor dropout stress, 
linear/log drift, additive noise, and window shifts, and (ii) 
performs sensor-level probing via mask-as-missing zeroing with 
per-channel influence estimation to support root-cause analysis.

We evaluate 14 representative models on five public anomaly 
datasets (SWaT, WADI, SMD, SKAB, TEP) and two industrial 
datasets (steam turbine, nuclear turbogenerator) under unified 
splits and event aggregation. There is no universal winner: 
graph-structured models transfer best under dropout and long 
events, for example on SWaT with additive noise a graph 
autoencoder falls from 0.804 to 0.677 (-16\%) while a 
graph-attention variant goes from 0.759 to 0.680 (-10\%) and 
a hybrid graph attention stays nearly flat at 0.762 to 0.756 
(-0.8\%); density or flow models work well on clean, stationary 
plants yet can be fragile to monotone drift, as log drift collapses 
a flow-based detector on SKAB and nuclear power plant (NPP) 
dataset while on SWaT the drop is mild at 0.795 to 0.783 
(-1.5\%); spectral CNNs lead when periodicity is strong; 
reconstruction autoencoders become competitive after basic 
sensor vetting; predictive or hybrid dynamics help when faults 
break temporal dependencies but remain window-sensitive. 
The same protocol also informs architecture choices, for example 
on SWaT under log perturbations replacing normalizing flows with 
a Gaussian density estimator reduces F1 to about 0.57 at high 
stress versus about 0.75 originally, and fixing the learned DAG 
yields a small clean-set gain of about 0.5 to 1.0 points but 
increases drift sensitivity by roughly eight times.


Benchmarking IoT 
Time-Series AD with Event-
Level Augmentations

Reliable anomaly detection (AD) in photovoltaic (PV) modules is 
critical for maintaining solar energy efficiency. However, the 
development of robust computer vision models for PV inspection is 
limited by the scarcity of large-scale, diverse, and balanced datasets.
In this study, we introduce a spatially conditioned denoising diffusion 
probabilistic model, PV-DDPM, designed to generate anomalous 
electroluminescence (EL) images across four PV cell types: 
multi-crystalline silicon (multi-c-Si), mono-crystalline silicon 
(mono-c-Si), half-cut multi-c-Si, and interdigitated back contact 
(IBC) with dogbone interconnect. 
PV-DDPM enables the controlled synthesis of both multi-defect and 
single-defect scenarios by conditioning on binary masks that 
represent structural features and defect positions.
To the best of our knowledge, this is the first framework that jointly 
models multiple PV cell types while allowing simultaneous 
generation of diverse anomaly types.
We also introduce E-SCDD dataset, an enhanced version of the 
SCDD dataset, comprising 1,000 pixel-wise annotated EL images 
spanning 30 semantic classes, and 1,768 unlabeled synthetic 
samples. Quantitative evaluation shows our generated images 
achieve a Fréchet Inception Distance (FID) of 4.10 and Kernel 
Inception Distance (KID) of 0.0023 ± 0.0007 across all categories. 
Furthermore, compared to training on the original SCDD dataset, 
training the vision–language anomaly detection model AA-CLIP 
on E-SCDD improves pixel-level AUC and average precision by 
1.70 and 8.34 points, respectively. Code and data are publicly 
released to ensure reproducibility.

Bridging the Data Gap: Spatially Conditioned Diffusion Model for Anomaly Generation in Photovoltaic Electroluminescence Images

This article introduces Attuned Design Practice (ADP), a design vocabulary and conceptual framework for producing embodied knowledge at the intersection of artistic and scientific inquiry, to inform the development of computational models that generate body-based interpretations and articulations. The convergence of Robotics, AI, HRI, and HCI in Industry 5.0 demand bold experimentation in artistic-scientific inquiry to interpret embodied knowledge beyond quantifiable data. In ADP, we propose the concept of ‘attunement’ to examine how alternative design vocabularies for embodied knowledge can be produced in reciprocity with the development of computational models that direct and facilitate expression and attunement over optimization or efficiency. Attunement is seen as a tuning fork to incrementally investigate the phenomena, relationships and patterns that inherently exist in tensions in-between tangible-intangible, or palpable-impalpable forms of materiality in human-computer connections, partnerships, and shared experiences. We propose Open Rehearsals, where-in alternative experiential understandings unfold in three areas of expertise: (1) preparing of tools and instruments, (2) performative practice, and (3) composition and choreography and notation methods. Our aim is to reinvent design innovation by cultivating integrative, reflective, and adaptive modes of collaborative design-making, from embodied discovery to embodied transformation.

Attuned Design Practice: towards Open Rehearsals for Embodied Knowledge and Computational Design.

This study investigates the intuitive relationship between manual gestures and vocal syllables, grounded in shared neurobiological and motor systems. Participants were tasked with producing "Synekinian pairs," consisting of vocal syllables paired with instinctive gestures. Through qualitative and quantitative analyses, the findings reveal consistent patterns in manual-vocal associations, offering insights into gesture-based sound design, vocal synthesis, gesture notation and human-computer interaction. Future work aims to refine the experimental methodology, expand datasets, and validate these observations across diverse cultural and linguistic contexts.

Exploring “Synekinian Pairs”: Manual-Vocal Gesture Integration in Experimental Contexts

This study investigates how audiences perceive humor in both Human-created and AI-generated Manzai scripts, a traditional Japanese form of scripted stand-up comedy performed by a pair of comedians. We analyze laughter responses using two complementary modalities: explicit laughter, captured through real-time reaction-button inputs, and implicit laughter, measured via mouth-corner motion tracking. Comparative experiments are conducted to evaluate differences between Human-created and AI-generated Manzai scripts. The results reveal that while AI-generated Manzai scripts can elicit explicit laughter to a certain extent, it is less effective in inducing implicit laughter compared to Human-created script. Implicit laughter is observed only when explicit laughter occurred, suggesting that unconscious emotional reactions may precede conscious recognition of humor. Moreover, Human-created Manzai scripts that are contextually relatable and easy to imagine produced larger and more sustained mouth-corner movements, indicating that empathy and contextual understanding play key roles in embodied humor perception. These findings demonstrate that mouth-corner motion is a reliable indicator of subtle amusement and provide new insights into Bodily Expressed Emotion Understanding (BEEU) for AI-generated humor.

Understanding Bodily Expressed Laughter Responses to Human and AI-Generated Japanese Manzai scripts

Predicting Valence-Arousal-Dominance (VAD) dimensions from bodily-expressed emotions in videos remains a fundamentally challenging task in affective computing, requiring models that capture subtle spatiotemporal patterns while balancing computational efficiency and interpretability. We present a comprehensive investigation of VAD prediction approaches on the newly introduced Annotated Bodily Expressed Emotion (ABEE) dataset, which contains approximately 3,200 video clips spanning 8 primary emotion categories and 20 subcategories. We explore two complementary methodologies: a feature-based gradient boosting approach using XGBoost with carefully engineered spatiotemporal features and dimensionality reduction, and deep learning architectures capable of learning hierarchical representations directly from raw video data. Our feature-based approach demonstrates exceptional computational efficiency, with sub-second training times and minimal resource requirements, while our deep models reveal the fundamental difficulty of capturing continuous VAD dimensions from bodily expressions. Through systematic evaluation on the ABEE dataset, we establish baseline performance for the VAD prediction task, achieving $R^2$ scores of -0.090, -0.014, and -0.058 for valence, arousal, and dominance, respectively, with our gradient boosting approach. These results highlight the substantial gap between current methodologies and the inherent complexity of bodily emotion signals, providing benchmarks for future research. We further discuss critical insights regarding feature engineering, temporal dynamics, and the intrinsic challenges of continuous emotion prediction from naturalistic video data, emphasizing the need for dedicated spatiotemporal modeling strategies tailored to bodily expressions.

Spatiotemporal Modeling of Bodily Emotional Expressions for Continuous Valence-Arousal-Dominance Prediction in Video

Despite remarkable advances in machine learning, real-time, online inference of agents’ intentions remains one of the enduring grand challenges in AI. Plan, activity, intent, and goal recognition involve making inferences about actors from observations of their behavior, whether they are software agents, robots, or humans. This tutorial targets AI students and researchers interested in exploring these recognition tasks to enhance their ongoing research or identify new research directions.
Attendees will leave with a clear understanding of core concepts, tasks, and relationships among plan, activity, intent, and goal recognition, as well as exposure to the state-of-the-art computational representations, algorithms, and empirical methodologies. The tutorial is innovative in its unification of fragmented research across multiple AI subfields and its use of live demonstrations to connect theory with practice. Building on the successful 2019 AAAI tutorial on the same topic (Keren, Mirsky, and Geib 2019), this session offers an updated and expanded perspective. It incorporates new material from the presenters’ co-authored book (Mirsky, Keren, and Geib 2021), along with the latest developments and advances in the field. Prerequisite knowledge: Familiarity with general AI concepts, probabilistic reasoning, and planning is recommended. Basic knowledge of Bayesian inference and logic-based reasoning will be helpful but not required.

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