Mahsa Salehi

Orcid: 0000-0002-2991-1612

According to our database1, Mahsa Salehi authored at least 50 papers between 2014 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
CARLA: Self-supervised contrastive representation learning for time series anomaly detection.
Pattern Recognit., 2025

2024
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey.
ACM Comput. Surv., September, 2024

Series2vec: similarity-based self-supervised representation learning for time series classification.
Data Min. Knowl. Discov., July, 2024

Improving position encoding of transformers for multivariate time series classification.
Data Min. Knowl. Discov., January, 2024

DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series.
CoRR, 2024

Human Brain Exhibits Distinct Patterns When Listening to Fake Versus Real Audio: Preliminary Evidence.
CoRR, 2024

Resilient Automated Distributed Device Scheduling in Smart Grids under False Data Injection Attacks.
Proceedings of the IEEE International Conference on Communications, 2024

Measuring Affective and Motivational States as Conditions for Cognitive and Metacognitive Processing in Self-Regulated Learning.
Proceedings of the 14th Learning Analytics and Knowledge Conference, 2024

EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

MTP: A Dataset for Multi-Modal Turning Points in Casual Conversations.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, 2024

2023
Guarding the Grid: Enhancing Resilience in Automated Residential Demand Response Against False Data Injection Attacks.
CoRR, 2023

Open-Set Graph Anomaly Detection via Normal Structure Regularisation.
CoRR, 2023

CARLA: A Self-supervised Contrastive Representation Learning Approach for Time Series Anomaly Detection.
CoRR, 2023

Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series.
CoRR, 2023

Cross-Domain Graph Anomaly Detection via Anomaly-Aware Contrastive Alignment.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Information resources estimation for accurate distribution-based concept drift detection.
Inf. Process. Manag., 2022

An eager splitting strategy for online decision trees in ensembles.
Data Min. Knowl. Discov., 2022

Deep Learning for Time Series Anomaly Detection: A Survey.
CoRR, 2022

ENDASh: Embedding Neighbourhood Dissimilarity with Attribute Shuffling for Graph Anomaly Detection.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022

Extremely Fast Hoeffding Adaptive Tree.
Proceedings of the IEEE International Conference on Data Mining, 2022

False Data Injection Attack Detection for Secure Distributed Demand Response in Smart Grids.
Proceedings of the 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2022

2021
Detecting singleton spams in reviews via learning deep anomalous temporal aspect-sentiment patterns.
Data Min. Knowl. Discov., 2021

A Fully Unsupervised and Efficient Anomaly Detection Approach with Drift Detection Capability.
Proceedings of the 2021 International Conference on Data Mining, 2021

Disjoint-CNN for Multivariate Time Series Classification.
Proceedings of the 2021 International Conference on Data Mining, 2021

We Can Pay Less: Coordinated False Data Injection Attack Against Residential Demand Response in Smart Grids.
Proceedings of the CODASPY '21: Eleventh ACM Conference on Data and Application Security and Privacy, 2021

2020
Enabling Efficient Privacy-Assured Outlier Detection Over Encrypted Incremental Data Sets.
IEEE Internet Things J., 2020

An Eager Splitting Strategy for Online Decision Trees.
CoRR, 2020

Emergent and Unspecified Behaviors in Streaming Decision Trees.
CoRR, 2020

Detecting Driver's Distraction using Long-term Recurrent Convolutional Network.
CoRR, 2020

Inherent Vulnerability of Demand Response Optimisation against False Data Injection Attacks in Smart Grids.
Proceedings of the NOMS 2020, 2020

Robust Demand Response for Device Scheduling under False Data Injection Attacks in Smart Grids.
Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe, 2020

MIR_MAD: An Efficient and On-line Approach for Anomaly Detection in Dynamic Data Stream.
Proceedings of the 20th International Conference on Data Mining Workshops, 2020

2019
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets.
CoRR, 2019

Online Semi-Supervised Concept Drift Detection with Density Estimation.
CoRR, 2019

High Impact False Data Injection Attack against Real-time Pricing in Smart Grids.
Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Europe, 2019

2018
A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction].
SIGKDD Explor., 2018

Density Biased Sampling with Locality Sensitive Hashing for Outlier Detection.
Proceedings of the Web Information Systems Engineering - WISE 2018, 2018

Online Clustering for Evolving Data Streams with Online Anomaly Detection.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Extremely Fast Decision Tree.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017
A large-scale spatio-temporal data analytics system for wildfire risk management.
Proceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data, 2017

Fast Memory Efficient Local Outlier Detection in Data Streams (Extended Abstract).
Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

An Efficient Method for Anomaly Detection in Non-Stationary Data Streams.
Proceedings of the 2017 IEEE Global Communications Conference, 2017

2016
Fast Memory Efficient Local Outlier Detection in Data Streams.
IEEE Trans. Knowl. Data Eng., 2016

Dynamic and Robust Wildfire Risk Prediction System: An Unsupervised Approach.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Smart Sampling: A Novel Unsupervised Boosting Approach for Outlier Detection.
Proceedings of the AI 2016: Advances in Artificial Intelligence, 2016

Data-Driven Prediction and Visualisation of Dynamic Bushfire Risks.
Proceedings of the Databases Theory and Applications, 2016

2015
Anomaly detection in data streams: challenges and techniques.
PhD thesis, 2015

Local outlier detection for data streams in sensor networks: Revisiting the utility problem invited paper.
Proceedings of the Tenth IEEE International Conference on Intelligent Sensors, 2015

Profiling Pedestrian Distribution and Anomaly Detection in a Dynamic Environment.
Proceedings of the 24th ACM International Conference on Information and Knowledge Management, 2015

2014
A Relevance Weighted Ensemble Model for Anomaly Detection in Switching Data Streams.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2014


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