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Found 45 papers, 11 papers with code
Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense
Large Language Models (LLMs) have showcased remarkable capabilities across various domains.
LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance
Dataset license compliance is a critical yet complex aspect of developing commercial AI products, particularly with the increasing use of publicly available datasets.
FD-LLM: Large Language Model for Fault Diagnosis of Machines
We assess the fault diagnosis capabilities of four open-sourced LLMs based on the FD-LLM framework, and evaluate the models' adaptability and generalizability under various operational conditions and machine components, namely for traditional fault diagnosis, cross-operational conditions, and cross-machine component settings.
Personalized Continual EEG Decoding: Retaining and Transferring Knowledge
To address these issues, we propose the Personalized Continual EEG Decoding (PCED) framework for continual EEG decoding.
EEG-based Multimodal Representation Learning for Emotion Recognition
Multimodal learning has been a popular area of research, yet integrating electroencephalogram (EEG) data poses unique challenges due to its inherent variability and limited availability.
GLA-DA: Global-Local Alignment Domain Adaptation for Multivariate Time Series
Unsupervised and Semi-supervised Domain Adaptation (UDA and SSDA) have demonstrated efficiency in addressing this issue by utilizing pre-labeled source data to train on unlabeled or partially labeled target data.
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback
To address this task, we propose Pistis-RAG, a new RAG framework designed with a content-centric approach to better align LLMs with human preferences.
LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices
To address this issue, we propose Knowledge Pruning (KP), a novel paradigm for time series learning in this paper.
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition
The general pipeline consists of a span detector to identify entity spans in text and an entity-type classifier to assign types to entities.
Performance Evaluation of Damping Systems in Civil Engineering Structures Via Minimal Sensor
To control structural responses under various actions, the growing use of supplementary damping systems in modern civil engineering structures necessitates inspecting and evaluating their operational performance postinstallation.
Blockchain and Artificial Intelligence: Synergies and Conflicts
Blockchain technology and Artificial Intelligence (AI) have emerged as transformative forces in their respective domains.
EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representation
In the domain of 3D scene representation, 3D Gaussian Splatting (3DGS) has emerged as a pivotal technology.
Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs
Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause configuration properties based on logs.
A Variational Autoencoder Framework for Robust, Physics-Informed Cyberattack Recognition in Industrial Cyber-Physical Systems
This data-driven framework considers the temporal behavior of a generic physical system that extracts features from the time series of the sensor measurements that can be used for detecting covert attacks, distinguishing them from equipment faults, as well as localize the attack/fault.
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things
The Internet of Federated Things (IoFT) represents a network of interconnected systems with federated learning as the backbone, facilitating collaborative knowledge acquisition while ensuring data privacy for individual systems.
Corporate Credit Rating: A Survey
Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development.
An Empirical Study of NetOps Capability of Pre-Trained Large Language Models
Nowadays, the versatile capabilities of Pre-trained Large Language Models (LLMs) have attracted much attention from the industry.
Delete: Deep Lead Optimization Enveloped in Protein Pocket through Unified Deleting Strategies and a Structure-aware Network
Drug discovery is a highly complicated process, and it is unfeasible to fully commit it to the recently developed molecular generation methods.
Mapping effective connectivity by virtually perturbing a surrogate brain
Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain.
Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach
This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections.
Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review
This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning.
A Novel Splitting Criterion Inspired by Geometric Mean Metric Learning for Decision Tree
Decision tree (DT) attracts persistent research attention due to its impressive empirical performance and interpretability in numerous applications.
Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions
Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives.
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20. 68x on average.
VIRT: Improving Representation-based Models for Text Matching through Virtual Interaction
With the booming of pre-trained transformers, representation-based models based on Siamese transformer encoders have become mainstream techniques for efficient text matching.
Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems
We create abenchmark for Paint4Poem: we train two representative text-to-image generation models: AttnGAN and MirrorGAN, and evaluate theirperformance regarding painting pictorial quality, painting stylistic relevance, and semantic relevance between poems and paintings. The results indicate that the models are able to generate paintings that have good pictorial quality and mimic Feng Zikai's style, but thereflection of poem semantics is limited.
Missingness Augmentation: A General Approach for Improving Generative Imputation Models
However, data augmentation, as a simple yet effective method, has not received enough attention in this area.
Reward-Based 1-bit Compressed Federated Distillation on Blockchain
The recent advent of various forms of Federated Knowledge Distillation (FD) paves the way for a new generation of robust and communication-efficient Federated Learning (FL), where mere soft-labels are aggregated, rather than whole gradients of Deep Neural Networks (DNN) as done in previous FL schemes.
Online Optimization and Ambiguity-based Learning of Distributionally Uncertain Dynamic Systems
A novelty of this work is that it extends DRO to online optimization problems subject to a distributionally uncertain dynamical system constraint, handled via a control-dependent ambiguity set that leads to online-tractable optimization with probabilistic guarantees on regret bounds.
Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG
We trained our model using the publicly available dataset from 2017 PhysioNet Computing in Cardiology(CinC) Challenge containing 8528 single-lead ECG recordings of short-term heart rhythms (9-61s).
AUL is a better optimization metric in PU learning
Recent works try to recover the unbiased result by estimating the proportion of positive samples with mixture proportion estimation (MPE) algorithms, but the model performance is still limited and heavy computational cost is introduced (particularly for big datasets).
Adversarial Momentum-Contrastive Pre-Training
Recently proposed adversarial self-supervised learning methods usually require big batches and long training epochs to extract robust features, which will bring heavy computational overhead on platforms with limited resources.
PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data
Further, this classifier is incorporated into the generative adversarial framework to help the generator to yield higher quality imputation results.
Improve Adversarial Robustness via Weight Penalization on Classification Layer
It is well-known that deep neural networks are vulnerable to adversarial attacks.
Deep Learning based Covert Attack Identification for Industrial Control Systems
This data-driven framework considers the temporal behavior of a generic physical system that extracts features from the time series of the sensor measurements that can be used for detecting covert attacks, distinguishing them from equipment faults, as well as localize the attack/fault.
Query Resolution for Conversational Search with Limited Supervision
Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution.
Improving Positive Unlabeled Learning: Practical AUL Estimation and New Training Method for Extremely Imbalanced Data Sets
Secondly, we propose ProbTagging, a new training method for extremely imbalanced data sets, where the number of unlabeled samples is hundreds or thousands of times that of positive samples.
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.
Nanoconfined, dynamic electrolyte gating and memory effects in multilayered graphene-based membranes
Multilayered graphene-based nanoporous membranes with electrolyte incorporated between individual sheets is a unique nano-heterostructure system in which nanoconfined electrons in graphene and ions confined in between sheets are intimately coupled throughout the entire membrane.
Mesoscale and Nanoscale Physics Materials Science Soft Condensed Matter Applied Physics Chemical Physics
ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment
To our best knowledge, this is the first work that aims at building a real and multilingual ASR corpus for the air traffic related research.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status.
Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks
Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings.
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems.
BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training
In distributed machine learning (DML), the network performance between machines significantly impacts the speed of iterative training.
Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series
We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS.
