Search Results for author:
Found 43 papers, 10 papers with code
An Unsupervised Approach to Discover Media Frames
We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.
OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data
We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research.
BU-NEmo: an Affective Dataset of Gun Violence News
As such, we introduce a U. S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings.
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage
Such perspectives are called “frames” in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article.
BERT Enhanced Neural Machine Translation and Sequence Tagging Model for Chinese Grammatical Error Diagnosis
This paper presents the UNIPUS-Flaubert team’s hybrid system for the NLPTEA 2020 shared task of Chinese Grammatical Error Diagnosis (CGED).
Asymptotically Efficient Online Learning for Censored Regression Models Under Non-I.I.D Data
The asymptotically efficient online learning problem is investigated for stochastic censored regression models, which arise from various fields of learning and statistics but up to now still lacks comprehensive theoretical studies on the efficiency of the learning algorithms.
Fault Separation Based on An Excitation Operator with Application to a Quadrotor UAV
This paper presents an excitation operator based fault separation architecture for a quadrotor unmanned aerial vehicle (UAV) subject to loss of effectiveness (LoE) faults, actuator aging, and load uncertainty.
Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances
State estimation has long been a fundamental problem in signal processing and control areas.
Joint Device-Edge Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization
Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques.
Lightweight Self-Knowledge Distillation with Multi-source Information Fusion
Specifically, we introduce a Distillation with Reverse Guidance (DRG) method that considers different levels of information extracted by the model, including edge, shape, and detail of the input data, to construct a more informative teacher.
ImpressionGPT: An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT
The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians, and it is typically written by radiologists based on the 'Findings' section.
Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation
Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information.
Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network
Cross-domain Sequential Recommendation (CSR) is an emerging yet challenging task that depicts the evolution of behavior patterns for overlapped users by modeling their interactions from multiple domains.
Deep Forest with Hashing Screening and Window Screening
To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening.
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model
Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks.
Adaptive Identification with Guaranteed Performance Under Saturated-Observation and Non-Persistent Excitation
This paper investigates the adaptive identification and prediction problems for stochastic dynamical systems with saturated observations, which arise from various fields in engineering and social systems, but up to now still lack comprehensive theoretical studies including performance guarantees needed in practical applications.
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential recommendation.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains.
Functional2Structural: Cross-Modality Brain Networks Representation Learning
Since brain networks derived from functional and structural MRI describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks is non-trivial.
OPP-Miner: Order-preserving sequential pattern mining
To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values.
DBC-Forest: Deep forest with binning confidence screening
As a deep learning model, deep confidence screening forest (gcForestcs) has achieved great success in various applications.
Model-free Nearly Optimal Control of Constrained-Input Nonlinear Systems Based on Synchronous Reinforcement Learning
In this paper a novel model-free algorithm is proposed.
A Nearly Optimal Chattering Reduction Method of Sliding Mode Control With an Application to a Two-wheeled Mobile Robot
First, the deficiency of chattering in traditional SMC and the quasi-SMC method are analyzed in this paper.
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation
Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups.
Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic Performance Prediction
Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, such as personalized teaching and academic early warning.
Identification and Adaptation with Binary-Valued Observations under Non-Persistent Excitation Condition
By using both the stochastic Lyapunov function and martingale estimation methods, we establish the strong consistency of the estimation algorithm and provide the convergence rate, under a signal condition which is considerably weaker than the traditional PE condition and coincides with the weakest possible excitation known for the classical least square algorithm of stochastic regression models.
A Multi-task Deep Feature Selection Method for Brain Imaging Genetics
Though deep learning models can extract the nonlinear relationship, they could not select relevant genetic factors.
Online Adaptive Optimal Control Algorithm Based on Synchronous Integral Reinforcement Learning With Explorations
In this paper, we present a novel algorithm named synchronous integral Q-learning, which is based on synchronous policy iteration, to solve the continuous-time infinite horizon optimal control problems of input-affine system dynamics.
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains.
Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation
However, the representation learning for a group is most complex beyond the fusion of group member representation, as the personal preferences and group preferences may be in different spaces.
Unsupervised Image Segmentation using Mutual Mean-Teaching
Unsupervised image segmentation aims at assigning the pixels with similar feature into a same cluster without annotation, which is an important task in computer vision.
OpenFraming: We brought the ML; you bring the data. Interact with your data and discover its frames
The degree of user involvement is flexible: they can run models that have been pre-trained on select issues; submit labeled documents and train a new model for frame classification; or submit unlabeled documents and obtain potential frames of the documents.
Secrecy Rate Maximization for Intelligent Reflecting Surface Aided SWIPT Systems
Simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) are two promising techniques for providing enhanced wireless communication capability and sustainable energy supply to energy-constrained wireless devices.
Multi-Label and Multilingual News Framing Analysis
News framing refers to the practice in which aspects of specific issues are highlighted in the news to promote a particular interpretation.
Adversarial Attack on Hierarchical Graph Pooling Neural Networks
However, most of the existing work in this area focus on the GNNs for node-level tasks, while little work has been done to study the robustness of the GNNs for the graph classification task.
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities
Considering the rapid evolution of this field, this paper provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers.
Performance Comparison of Crowdworkers and NLP Tools on Named-Entity Recognition and Sentiment Analysis of Political Tweets
We report results of a comparison of the accuracy of crowdworkers and seven Natural Language Processing (NLP) toolkits in solving two important NLP tasks, named-entity recognition (NER) and entity-level sentiment (ELS) analysis.
Convergence of a Distributed Least Squares
In this paper, we consider a least-squares (LS)-based distributed algorithm build on a sensor network to estimate an unknown parameter vector of a dynamical system, where each sensor in the network has partial information only but is allowed to communicate with its neighbors.
Detecting Frames in News Headlines and Its Application to Analyzing News Framing Trends Surrounding U.S. Gun Violence
We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U. S. between 2016 and 2018.
BUOCA: Budget-Optimized Crowd Worker Allocation
We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling.
Dynamic Allocation of Crowd Contributions for Sentiment Analysis during the 2016 U.S. Presidential Election
We explore two dynamic-allocation methods: (1) The number of workers queried to label a tweet is computed offline based on the predicted difficulty of discerning the sentiment of a particular tweet.
Learning Coarse-to-Fine Sparselets for Efficient Object Detection and Scene Classification
Part model-based methods have been successfully applied to object detection and scene classification and have achieved state-of-the-art results.
Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles
Our strategy is to formulate the individual ROI optimization as a group variance minimization problem, in which group-wise functional and structural connectivity patterns, and anatomic profiles are defined as optimization constraints.
