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Found 14 papers, 5 papers with code
Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese
In this work, we develop a pipeline for historical-psychological text analysis in classical Chinese.
Groot: Adversarial Testing for Generative Text-to-Image Models with Tree-based Semantic Transformation
With the prevalence of text-to-image generative models, their safety becomes a critical concern.
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
A wide range of experiments on both synthetic and real-world datasets have illustrated the superior modeling capacities and prediction performance of ContiFormer on irregular time series data.
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model
Self-supervised learning has emerged as a highly effective approach in the fields of natural language processing and computer vision.
Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptation
Unsupervised domain adaptation uses source data from different distributions to solve the problem of classifying data from unlabeled target domains.
RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer
However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means for increasing the sample rate of low sample trajectories.
A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task that aims to extract triplets of aspect terms, sentiments, and opinion terms from review sentences.
Ranked #1 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Code Integrity Attestation for PLCs using Black Box Neural Network Predictions
Cyber-physical systems (CPSs) are widespread in critical domains, and significant damage can be caused if an attacker is able to modify the code of their programmable logic controllers (PLCs).
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models.
Plane Spiral OAM Mode-Group Based MIMO Communications: An Experimental Study
The mode-group (MG) superposed by specific single mode plane spiral OAM (PSOAM) waves has been proved to be a flexible beamforming method to achieve the azimuthal pattern diversity, which inherits the spiral phase distribution of conventional OAM wave.
Active Fuzzing for Testing and Securing Cyber-Physical Systems
Cyber-physical systems (CPSs) in critical infrastructure face a pervasive threat from attackers, motivating research into a variety of countermeasures for securing them.
Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage.
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS).
Towards Learning and Verifying Invariants of Cyber-Physical Systems by Code Mutation
Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network.
