Search Results for author:
Found 39 papers, 7 papers with code
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.
Extracting COVID-19 Events from Twitter
Language Modeling
+5
A Unified Attack Detection Strategy for Multi-Agent Systems over Transient and Steady Stages
This paper proposes a unified detection strategy against three kinds of attacks for multi-agent systems (MASs) which is applicable to both transient and steady stages.
Political-LLM: Large Language Models in Political Science
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection.
Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph
The former retrieves question-relevant domain knowledge from DKG and uses it to prompt LLM to enhance the reasoning capability for domain-specific tasks; the latter leverages LLM to generate new domain knowledge from processed tasks and use it to evolve DKG.
Adaptive Distributed Observer-based Model Predictive Control for Multi-agent Formation with Resilience to Communication Link Faults
In order to address the nonlinear multi-agent formation tracking control problem with input constraints and unknown communication faults, a novel adaptive distributed observer-based distributed model predictive control method is developed in this paper.
Price-guided user attention in large-scale E-commerce group recommendation
We analyzed user attention scores from a widely-used group recommendation model on a real-world E-commerce dataset and found that item price and user interaction history significantly influence the selection of critical users.
Open-/Closed-loop Active Learning for Data-driven Predictive Control
On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage.
A survey on secure decentralized optimization and learning
Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data.
A Real-Time Adaptive Multi-Stream GPU System for Online Approximate Nearest Neighborhood Search
In this paper, we introduce a novel Real-Time Adaptive Multi-Stream GPU ANNS System (RTAMS-GANNS).
An Approach to Detect Abnormal Submissions for CodeWorkout Dataset
However, anomalies in the students log data, such as cheating to solve programming problems, could introduce a hidden bias in the log data.
Innovation-triggered Learning with Application to Data-driven Predictive Control
The closed-loop stability of the data-driven predictive controller equipped with the innovation-triggered learning protocol is discussed within a high probability framework.
Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming
This study demonstrated that ChatGPT could generate Java programming assignment feedback that students perceived as formative.
Resilient and constrained consensus against adversarial attacks: A distributed MPC framework
In this work, we propose a distributed resilient consensus framework, consisting of a pre-designed consensus protocol and distributed model predictive control (DMPC) optimization, which can help significantly reduce the requirement on the network robustness and effectively handle the general linear constrained MAS under adversarial attacks.
Replay Attack Detection Based on Parity Space Method for Cyber-Physical Systems
The replay attack detection problem is studied from a new perspective based on parity space method in this paper.
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management
Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents.
Meta-Shop: Improving Item Advertisement For Small Businesses
Training samples in RS can be highly biased toward popular businesses with sufficient sales and can decrease advertising performance for small businesses.
A Robust Distributed Model Predictive Control Framework for Consensus of Multi-Agent Systems with Input Constraints and Varying Delays
This paper studies the consensus problem of general linear discrete-time multi-agent systems (MAS) with input constraints and bounded time-varying communication delays.
Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts.
Learning Similarity Preserving Binary Codes for Recommender Systems
We conducted experiments on MovieLens1M, Amazon product review, Ichiba purchase dataset and confirmed CCSR outperformed the existing matrix factorization-based methods.
Efficient Cross-Modal Retrieval via Deep Binary Hashing and Quantization
We simultaneously learn binary hash codes and quantization codes to preserve semantic information in multiple modalities by an end-to-end deep learning architecture.
Achieving Model Fairness in Vertical Federated Learning
To solve it in a federated and privacy-preserving manner, we consider the equivalent dual form of the problem and develop an asynchronous gradient coordinate-descent ascent algorithm, where some active data parties perform multiple parallelized local updates per communication round to effectively reduce the number of communication rounds.
Composite Adaptive Control for Anti-Unwinding Attitude Maneuvers: An Exponential Stability Result Without Persistent Excitation
This paper provides an exponential stability result for the adaptive anti-unwinding attitude tracking control problem of a rigid body with uncertain but constant inertia parameters, without requiring the satisfaction of persistent excitation (PE) condition.
Self-triggered min-max DMPC for asynchronous multi-agent systems with communication delays
At triggering instants, each agent solves a local min-max optimization problem based on local system states and predicted system states of neighbors, determines its next triggering instant and broadcasts its predicted state trajectory to its neighbors.
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4. 0 on the edge computing level.
Toward Semi-Automatic Misconception Discovery Using Code Embeddings
Understanding students' misconceptions is important for effective teaching and assessment.
Chinese Lexical Simplification
Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning.
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets.
Extracting COVID-19 Events from Twitter
Language Modeling
+6
Task-Oriented Optimal Sequencing of Visualization Charts
A chart sequence is used to describe a series of visualization charts generated in the exploratory analysis by data analysts.
EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data
A suite of visualizations is designed to illustrate the dynamics of city segmentation and the corresponding interactions are added to support the exploration of the segmentation patterns over time.
Visual Analytics of Anomalous User Behaviors: A Survey
The increasing accessibility of data provides substantial opportunities for understanding user behaviors.
Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations
Count sketch is a simple popular sketch which uses a randomized hash function to achieve compression.
Deep neural network based i-vector mapping for speaker verification using short utterances
Experimental results using the NIST SRE 2010 dataset show that both methods provide significant improvement and result in a max of 28. 43% relative improvement in Equal Error Rates from a baseline system, when using deep encoder with residual blocks and adding an additional phoneme vector.
Question Type Guided Attention in Visual Question Answering
Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions.
Accurate and Efficient Estimation of Small P-values with the Cross-Entropy Method: Applications in Genomic Data Analysis
$\textbf{Results:}$ We evaluate the performance of the proposed algorithm through simulations and demonstrate its application to three real-world examples in genomic studies.
Applications
PoTrojan: powerful neural-level trojan designs in deep learning models
With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life.
Compact Tensor Pooling for Visual Question Answering
Performing high level cognitive tasks requires the integration of feature maps with drastically different structure.
Leak Event Identification in Water Systems Using High Order CRF
Today, detection of anomalous events in civil infrastructures (e. g. water pipe breaks and leaks) is time consuming and often takes hours or days.
Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Robust tensor CP decomposition involves decomposing a tensor into low rank and sparse components.
