102 citations · 227 across the 22 of their papers we have counts for
14 papers · 1 filter
BEACON: Bridging Multimodal Semantics and Asynchronous Propagation for Cloud System Anomaly Detection
Zhe Li, Tengxue Zhang, Yang Shu +4
Modern large-scale cloud systems continuously generate massive and multimodal observability data, such as time-series metrics, log records, and system dependency graphs, which refl…
Contrastive Hypergraph Source-free Domain Adaptive Object Detection in Adverse Weather Conditions--Extended Version
Jicheng Yuan, Tung Kieu, Manh Nguyen-Duc +2
Adverse weather conditions such as fog, rain, snow, and low illumination introduce structured visibility degradations that severely impair object detectors trained on clear-weather…
Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis
Nam V. Nguyen, Hoang Van Truong, Hieu D. D. +3
We study industrial fault diagnosis as a classification task that assigns sensor time-series windows to predefined health states or fault types. Scarce labels and changing operatin…
AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version
Kien Le Trung, Tung Kieu, Dinh Duc Nha Nguyen +3
Accurate long-term air quality forecasting is challenging due to long-range transport, meteorological variability, and nonlinear chemical reactions underlying secondary pollutant f…
SemReuse: Certified Reuse of Cached Natural-Language Views for Semantic Operators
Phat T. Tran-Truong, Tung Kieu, Xuan-Bach Le +4
Semantic operators allow users to filter unstructured data using conditions expressed in natural language, but existing systems typically invoke an LLM for every input tuple. Analy…
When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning
Sean Bin Yang, Ying Sun, Zongyi Xu +6
Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use information, to support tasks including…
EPIC: Explicit Posterior Item Conditioning for Semantic ID Diffusion Recommendation
Tuan-Binh Tran, Thanh Tam Nguyen, Quoc Viet Hung Nguyen +3
Semantic ID (SID) generative recommendation predicts the next item by generating a short tuple of discrete tokens. Recent masked-diffusion methods improve this process through bidi…
SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version
Tuan-Binh Tran, Dat Nguyen Cong, Duc-Trong Le +2
Textual context such as news, reports, and logs can provide valuable signals for time series forecasting, especially when future dynamics are driven by external events that are not…
When Denoising Hurts: Rethinking the Terminal Step of Diffusion Time Series Forecasters -- Extended Version
Dat Nguyen-Cong, Luong Tran, Tung Kieu
Diffusion models offer a natural way to model uncertainty in time series forecasting, yet their iterative sampling process is often treated as a uniformly beneficial refinement pro…
ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation
Xuan-Thong Truong, Trung-Kien Le, Tung Kieu +2
Deep learning has significantly advanced time series imputation, yet most existing architectures primarily rely on localized temporal context within the corrupted input sequence. T…
Scenario-based Probing and Steering Cultural Values in Large Language Models--Extended Version
Trung Duc Anh Dang, Tung Kieu, Sarah Masud
Large Language Models (LLMs) are deployed across cultural contexts but often reflect homogenized values inherited from training data. Evaluations of cultural alignment typically re…
TimeBlocks: Foundational and Continual Time-Series Blockbase -- Extended Version
David Campos, Bin Yang, Tung Kieu +3
The ongoing digitization has led to a proliferation of time-series data streams that monitor a variety of processes, from which valuable insights may be obtained. Further, the emer…
Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version
Hong-Phuc Phan, Tuan-Anh Vu, Tung Kieu +3
Unsupervised outlier detection is attractive because it eliminates the need for labeled data. Moreover, forming multi-model ensembles can improve detection robustness. However, com…
FastDiSS: Few-step Match Many-step Diffusion Language Model on Sequence-to-Sequence Generation--Full Version
Dat Nguyen-Cong, Tung Kieu, Hoang Thanh-Tung
Self-conditioning has been central to the success of continuous diffusion language models, as it allows models to correct previous errors. Yet its ability degrades precisely in the…