Search Results for author:
Found 134 papers, 42 papers with code
YNU-HPCC at SemEval-2022 Task 2: Representing Multilingual Idiomaticity based on Contrastive Learning
However, for subtask A of idiomaticity detection, we simply did a few explorations and experiments based on the xlm-RoBERTa model.
YNU-HPCC at ROCLING 2022 Shared Task: A Transformer-based Model with Focal Loss and Regularization Dropout for Chinese Healthcare Named Entity Recognition
This paper adopts a transformer-based model with focal Loss and regularization dropout.
YNU-HPCC at SemEval-2022 Task 8: Transformer-based Ensemble Model for Multilingual News Article Similarity
This paper describes the system submitted by our team (YNU-HPCC) to SemEval-2022 Task 8: Multilingual news article similarity.
YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic
As participants in Task 6 (titled “iSarcasmEval: Intended Sarcasm Detection In English and Arabic”), we implement the sentiment system for all three subtasks in English and Arabic.
YNU-HPCC at SemEval-2022 Task 5: Multi-Modal and Multi-label Emotion Classification Based on LXMERT
We use Faster-RCNN to extract visual representation and utilize LXMERT’s cross-attention for multi-modal alignment.
YNU-HPCC at SemEval-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language Detection
For the multi-label classification, we use the fine-tuned BERT model to extract the sentiment score of the text and a fully connected layer to classify the text into the PCL categories.
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model Compression
The teacher can continuously meta-learn the student’s learning objective to adjust its parameters for maximizing the student’s performance throughout the distillation process.
ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts
This paper presents the ROCLING 2021 shared task on dimensional sentiment analysis for educational texts which seeks to identify a real-value sentiment score of self-evaluation comments written by Chinese students in the both valence and arousal dimensions.
Accelerating Inference for Pretrained Language Models by Unified Multi-Perspective Early Exiting
To address this limitation, a unified horizontal and vertical multi-perspective early exiting (MPEE) framework is proposed in this study to accelerate the inference of transformer-based models.
RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learning
RALAD features three primary designs, including (1) domain adaptation via an enhanced Optimal Transport (OT) method that accounts for both individual and grouped image distances, (2) a simple and unified framework that can be applied to various models, and (3) efficient fine-tuning techniques that freeze the computationally expensive layers while maintaining robustness.
Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D Car Reconstruction
3D car modeling is crucial for applications in autonomous driving systems, virtual and augmented reality, and gaming.
A Geometric Analysis-Based Safety Assessment Framework for MASS Route Decision-Making in Restricted Waters
To enhance the safety of Maritime Autonomous Surface Ships (MASS) navigating in restricted waters, this paper aims to develop a geometric analysis-based route safety assessment (GARSA) framework, specifically designed for their route decision-making in irregularly shaped waterways.
Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations
The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training.
Learning to Reason via Self-Iterative Process Feedback for Small Language Models
Small language models (SLMs) are more efficient, cost-effective, and customizable than large language models (LLMs), though they often underperform in specific areas like reasoning.
Energy Consumption Optimization, Response Time Differences and Indicators in Cortical Working Memory Revealed by Nonequilibrium
Experimental validation is challenging due to evolutionary variability, making our theoretical approach valuable for quantifying complex dynamics.
WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing
Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images.
SADDE: Semi-supervised Anomaly Detection with Dependable Explanations
In this research paper, we introduce SADDE, a general framework designed to accomplish two primary objectives: (1) to render the anomaly detection process interpretable and enhance the credibility of interpretation outcomes, and (2) to assign high-confidence pseudo labels to unlabeled samples, thereby boosting the performance of anomaly detection systems when supervised data is scarce.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
A Novel Deep Learning Tractography Fiber Clustering Framework for Functionally Consistent White Matter Parcellation Using Multimodal Diffusion MRI and Functional MRI
Tractography fiber clustering using diffusion MRI (dMRI) is a crucial strategy for white matter (WM) parcellation.
Dynamic Threshold-based Two-layer Online Unsupervised Anomaly Detector
The proliferation of the Internet of Things (IoT) has heightened the vulnerability to cyber threats, making it imperative to develop Anomaly Detection Systems (ADSs) capable of adapting to emerging or novel attacks.
Holistic structure of neural pathways underlies brain perceptual rivalry: A physical perspective of auditory stream segregation
Our results highlight that the neural basis of a perceptual and cognitive behavior is not limited to a localized brain neural region, but has a holistic and systematic origin.
Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information
To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong Information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes.
Grounding Language Models in Autonomous Loco-manipulation Tasks
Humanoid robots with behavioral autonomy have consistently been regarded as ideal collaborators in our daily lives and promising representations of embodied intelligence.
Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning
With the rapid growth of global e-commerce, the demand for automation in the logistics industry is increasing.
LLM-assisted Labeling Function Generation for Semantic Type Detection
One challenge in this process is the difficulty of manually writing labeling functions due to the large volume and low quality of the data lake table datasets.
MegActor-$Σ$: Unlocking Flexible Mixed-Modal Control in Portrait Animation with Diffusion Transformer
To address this issue, we introduce MegActor-$\Sigma$: a mixed-modal conditional diffusion transformer (DiT), which can flexibly inject audio and visual modality control signals into portrait animation.
A proof of contribution in blockchain using game theoretical deep learning model
A single edge device cannot undertake the various intelligent computations in a smart city, and the large-scale deployment of edge devices from different service providers to build an edge resource platform has become a necessity.
AEMLO: AutoEncoder-Guided Multi-Label Oversampling
Existing oversampling methods generate feature vectors of synthetic samples through replication or linear interpolation and assign labels through neighborhood information.
Chinese Metaphor Recognition Using a Multi-stage Prompting Large Language Model
Metaphors are common in everyday language, and the identification and understanding of metaphors are facilitated by models to achieve a better understanding of the text.
Autonomous Behavior Planning For Humanoid Loco-manipulation Through Grounded Language Model
This involves robots being able to plan their actions and behaviors in long-horizon tasks while using multi-modality to perceive deviations between task execution and high-level planning.
MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
To fill this gap, we introduce the Multimodal Multi-image Understanding (MMIU) benchmark, a comprehensive evaluation suite designed to assess LVLMs across a wide range of multi-image tasks.
Zero-Shot Cross-Domain Dialogue State Tracking via Dual Low-Rank Adaptation
Common methodologies include integrating prompts at the input layer or introducing learnable variables at each transformer layer.
PORCA: Root Cause Analysis with Partially Observed Data
In this paper, we unveil the issues of unobserved confounders and heterogeneity in partial observation and come up with a new problem of root cause analysis with partially observed data.
Nonequilibrium dynamics and thermodynamics provide the underlying physical mechanism of the perceptual rivalry
Perceptual rivalry, where conflicting sensory information leads to alternating perceptions crucial for associated cognitive function, has attracted researcher's attention for long.
HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation
We apply the planning and reasoning features of the large language models (LLMs) to complex loco-manipulation tasks, constructing a hierarchical task graph that comprises a series of primitive behaviors to bridge lower-level execution with higher-level planning.
A Blueprint Architecture of Compound AI Systems for Enterprise
Large Language Models (LLMs) have showcased remarkable capabilities surpassing conventional NLP challenges, creating opportunities for use in production use cases.
Towards RGB-NIR Cross-modality Image Registration and Beyond
This paper focuses on the area of RGB(visible)-NIR(near-infrared) cross-modality image registration, which is crucial for many downstream vision tasks to fully leverage the complementary information present in visible and infrared images.
Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) identifies sentiment information related to specific aspects and provides deeper market insights to businesses and organizations.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Diagnosing the Compositional Knowledge of Vision Language Models from a Game-Theoretic View
Compositional reasoning capabilities are usually considered as fundamental skills to characterize human perception.
Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling
Five methods were applied to generate soft negative samples that shared highly similar text but had different semantics from the original.
DuetSim: Building User Simulator with Dual Large Language Models for Task-Oriented Dialogues
User Simulators play a pivotal role in training and evaluating task-oriented dialogue systems.
Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation
Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes.
SoftMCL: Soft Momentum Contrastive Learning for Fine-grained Sentiment-aware Pre-training
The proposed SoftMCL is conducted on both the word- and sentence-level to enhance the model's ability to learn affective information.
MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation.
Research on emotionally intelligent dialogue generation based on automatic dialogue system
The model can detect and understand a wide range of emotions and specific pain signals in real time, enabling the system to provide empathetic interaction.
The application of Augmented Reality (AR) in Remote Work and Education
With the rapid advancement of technology, Augmented Reality (AR) technology, known for its ability to deeply integrate virtual information with the real world, is gradually transforming traditional work modes and teaching methods.
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
Specifically, HiGraphDTI learns hierarchical drug representations from triple-level molecular graphs to thoroughly exploit chemical information embedded in atoms, motifs, and molecules.
Event-assisted Low-Light Video Object Segmentation
In the realm of video object segmentation (VOS), the challenge of operating under low-light conditions persists, resulting in notably degraded image quality and compromised accuracy when comparing query and memory frames for similarity computation.
Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples
However, the intrinsic class imbalance in multi-label data may bias the model towards majority labels, since samples relevant to minority labels may be underrepresented in each mini-batch.
MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning
As a new distributed computing framework that can protect data privacy, federated learning (FL) has attracted more and more attention in recent years.
AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learning
Yet, FL users are susceptible to gradient inversion attacks (GIAs) which can reconstruct ground-truth training data such as images based on model gradients.
Personalized LoRA for Human-Centered Text Understanding
Effectively and efficiently adapting a pre-trained language model (PLM) for human-centered text understanding (HCTU) is challenging since user tokens are million-level in most personalized applications and do not have concrete explicit semantics.
Brain-inspired and Self-based Artificial Intelligence
In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm.
Understanding Cellular Noise with Optical Perturbation and Deep Learning
This approach, when combined with the analytical capabilities of a sophisticated deep neural network, enables the accurate estimation of rate constants from observational data in a broad range of biochemical reaction networks.
Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation
Relational Web tables provide valuable resources for numerous downstream applications, making table understanding, especially column annotation that identifies semantic types and relations of columns, a hot topic in the field of data management.
Ranked #1 on
Columns Property Annotation
on WikiTables-TURL-CPA
( Macro-F1 metric)
UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving
Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities.
Quantum learning and essential cognition under the traction of meta-characteristics in an open world
This is because things in the new and old worlds have different units and dimensions for their features.
EGVD: Event-Guided Video Deraining
In this paper, we approach video deraining by employing an event camera.
Multi-level feature fusion network combining attention mechanisms for polyp segmentation
Firstly, features extracted by encoders are not adequately filtered and utilized.
Language models as master equation solvers
In this study, we propose repurposing language models as a machine learning approach to solve master equations.
Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision
Moreover, the rapid expansion of precision livestock farming is creating a growing need to educate and train animal science students in CV.
Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms
Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data, to compare the performance of the thresholding and Mask R-CNN deep learning approaches, to evaluate the predictive ability of body weight regression models, and to promote open science in the animal science community by releasing the source code for video-based body weight prediction.
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications
In particular, we focus on the practical scenario of CCMCKP, where the probability distributions of random weights are unknown but only sample data is available.
Hedonic Prices and Quality Adjusted Price Indices Powered by AI
To accomplish this, we generate abstract product attributes, or ``features,'' from text descriptions and images using deep neural networks, and then use these attributes to estimate the hedonic price function.
Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues
Both memories collaborate to obtain entailment and discourse representation for the generation, allowing a deeper understanding of both consistency and coherence.
Ranked #1 on
Dialogue Generation
on Persona-Chat
(using extra training data)
Unsupervised Video Deraining with An Event Camera
Current unsupervised video deraining methods are inefficient in modeling the intricate spatio-temporal properties of rain, which leads to unsatisfactory results.
Towards Understanding the Generalization of Deepfake Detectors from a Game-Theoretical View
Deepfake detectors encode multi-order interactions among visual concepts, in which the low-order interactions usually have substantially negative contributions to deepfake detection.
Text-Guided Mask-free Local Image Retouching
In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning.
Fine-Grained Selective Similarity Integration for Drug-Target Interaction Prediction
Recently, with the availability of abundant heterogeneous biological information from diverse data sources, computational methods have been able to leverage multiple drug and target similarities to boost the performance of DTI prediction.
Joint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging
This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction task (JMERE).
An Improved Time Feedforward Connections Recurrent Neural Networks
This cell structure can reduce the number of parameters for RNNs cell, consequently reducing the computational complexity.
Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection.
LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge
This paper describes LeVoice automatic speech recognition systems to track2 of intelligent cockpit speech recognition challenge 2022.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Deep Baseline Network for Time Series Modeling and Anomaly Detection
By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result.
Explaining Deepfake Detection by Analysing Image Matching
Besides the supervision of binary labels, deepfake detection models implicitly learn artifact-relevant visual concepts through the FST-Matching (i. e. the matching fake, source, target images) in the training set.
Locally Aggregated Feature Attribution on Natural Language Model Understanding
Some feature attribution methods have shown promising results in computer vision, especially the gradient-based methods where effectively smoothing the gradients with reference data is key to a robust and faithful result.
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding
To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner.
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly.
Supporting GNSS Baseband Using Smartphone IMU and Ultra-Tight Integration
A Doppler value is predicted based on an integrated extended Kalman filter (EKF) navigator where the pseudorange-state-based measurements of GNSS and INS are fused.
Metastable dynamics of neural circuits and networks
In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits.
Federated Ensemble Model-based Reinforcement Learning in Edge Computing
Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices without gathering their data.
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data
Temporal relational data, perhaps the most commonly used data type in industrial machine learning applications, needs labor-intensive feature engineering and data analyzing for giving precise model predictions.
The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge
This report describes the submission of the DKU-DukeECE-Lenovo team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2021 track 4.
Accelerating Federated Learning with a Global Biased Optimiser
To address this challenge, we propose a novel, generalised approach for incorporating adaptive optimisation into FL with the Federated Global Biased Optimiser (FedGBO) algorithm.
YNU-HPCC at SemEval-2021 Task 5: Using a Transformer-based Model with Auxiliary Information for Toxic Span Detection
In this paper, a transformer-based model with auxiliary information is proposed for SemEval-2021 Task 5.
YNU-HPCC at SemEval-2021 Task 11: Using a BERT Model to Extract Contributions from NLP Scholarly Articles
This paper describes the system we built as the YNU-HPCC team in the SemEval-2021 Task 11: NLPContributionGraph.
YNU-HPCC at SemEval-2021 Task 10: Using a Transformer-based Source-Free Domain Adaptation Model for Semantic Processing
To address the issue, the organizers provided the models that fine-tuned a large number of source domain data on pre-trained models and the dev data for participants.
YNU-HPCC at SemEval-2021 Task 6: Combining ALBERT and Text-CNN for Persuasion Detection in Texts and Images
In recent years, memes combining image and text have been widely used in social media, and memes are one of the most popular types of content used in online disinformation campaigns.
PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
To enhance the generalization ability of PanGu-$\alpha$, we collect 1. 1TB high-quality Chinese data from a wide range of domains to pretrain the model.
Ranked #1 on
Reading Comprehension (One-Shot)
on DuReader
Cloze (multi-choices) (Few-Shot)
Cloze (multi-choices) (One-Shot)
+19
Unifying deterministic and stochastic ecological dynamics via a landscape-flux approach
We develop a landscape-flux framework to investigate observed frequency distributions of vegetation and the stability of these ecological systems under fluctuations.
Absolute quantification of real-time PCR data with stage signal difference analysis
Based on the calculated efficiency, we illustrate a new absolute qPCR analysis method for accurately determining nucleic acid amount.
Parallel implementations of random time algorithm for chemical network stochastic simulations
The discussion of computational complexity suggested a factor of $M$ (which is the connection number of the network) folds time consuming reduction for random time simulation algorithm as compared to other exact stochastic simulation algorithms, such as the Gillespie algorithm.
Probing black hole microstructure with the kinetic turnover of phase transition
By treating black hole as the macroscopic stable state on the free energy landscape, we propose that the stochastic dynamics of the black hole phase transition can be effectively described by the Langevin equation or equivalently by the Fokker-Planck equation in phase space.
General Relativity and Quantum Cosmology
Hawking radiation, local temperatures, and nonequilibrium thermodynamics of the black holes with non-killing horizon
Therefore, the black hole with non-killing horizon can be overall in non-equilibrium steady state while the Hawking temperature of the black funnel can be viewed as the local temperature and the corresponding Hawking radiation can be regarded as being in the local equilibrium with the horizon of the black funnel.
General Relativity and Quantum Cosmology
Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach
To address these challenges, we propose a novel learning-driven method, which is user-centric and can make effective online migration decisions by utilizing incomplete system-level information.
Nonequilibrium Physics in Biology
Life is characterized by a myriad of complex dynamic processes allowing organisms to grow, reproduce, and evolve.
Biological Physics
Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis
Recent studies used attention-based methods that can effectively improve the performance of aspect-level sentiment analysis.
YNU-HPCC at SemEval-2020 Task 7: Using an Ensemble BiGRU Model to Evaluate the Humor of Edited News Titles
This paper describes an ensemble model designed for Semeval-2020 Task 7.
YNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection
In this study, we propose a multi-granularity ordinal classification method to address the problem of emphasis selection.
No Perfect Outdoors: Towards A Deep Profiling of GNSS-based Location Contexts
On one hand, we offer a more fine-grained semantic classification than binary indoor-outdoor detection.
Networking and Internet Architecture
HPCC-YNU at SemEval-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed Text
In this paper, we (my codalab username is kongjun) present a system that uses a bilingual vector gating mechanism for bilingual resources to complete the task.
YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles
This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques.
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts.
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis
In recent years, the growing ubiquity of Internet memes on social media platforms, such as Facebook, Instagram, and Twitter, has become a topic of immense interest.
The Role of Energy Cost on Accuracy, Sensitivity, Specificity, Speed and Adaptation of T Cell Foreign and Self Recognition
The critical role of energy consumption in biological systems including T cell discrimination process has been investigated in various ways.
Stochastic epigenetic dynamics of gene switching
Epigenetic modifications of histones crucially affect the eukaryotic gene activity, while the epigenetic histone state is largely determined by the binding of specific factors such as the transcription factors (TFs) to DNA.
Explaining Away Attacks Against Neural Networks
We investigate the problem of identifying adversarial attacks on image-based neural networks.
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis
Deep neural network models such as long short-term memory (LSTM) and tree-LSTM have been proven to be effective for sentiment analysis.
Prediction stability as a criterion in active learning
Recent breakthroughs made by deep learning rely heavily on large number of annotated samples.
BigData Applications from Graph Analytics to Machine Learning by Aggregates in Recursion
In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented their use in the recursive statements of logic programs and deductive databases.
YNU-HPCC at SemEval-2019 Task 9: Using a BERT and CNN-BiLSTM-GRU Model for Suggestion Mining
Consumer opinions towards commercial entities are generally expressed through online reviews, blogs, and discussion forums.
YNU-HPCC at SemEval-2019 Task 6: Identifying and Categorising Offensive Language on Twitter
This document describes the submission of team YNU-HPCC to SemEval-2019 for three Sub-tasks of Task 6: Sub-task A, Sub-task B, and Sub-task C. We have submitted four systems to identify and categorise offensive language.
YNU-HPCC at SemEval-2019 Task 8: Using A LSTM-Attention Model for Fact-Checking in Community Forums
The LSTM-Attention model uses two LSTM to extract the features of the question and answer pair.
YUN-HPCC at SemEval-2019 Task 3: Multi-Step Ensemble Neural Network for Sentiment Analysis in Textual Conversation
This paper describes our approach to the sentiment analysis of Twitter textual conversations based on deep learning.
Improving Distributed Similarity Join in Metric Space with Error-bounded Sampling
To address the limitation, we propose SP-Join, an end-to-end framework to support distributed similarity join in metric space based on the MapReduce paradigm, which (i) employs an estimation-based stratified sampling method to produce pivots with quality guarantees for any sample size, and (ii) devises an effective cost model as the guideline to split the whole datasets into partition in map and reduce phases according to the sampled pivots.
Databases
Fast communication-efficient spectral clustering over distributed data
However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead.
K-nearest Neighbor Search by Random Projection Forests
Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.
A New Approach for Resource Scheduling with Deep Reinforcement Learning
Based on the previous research on DRL in the literature, we introduce online resource scheduling algorithm DeepRM2 and the offline resource scheduling algorithm DeepRM_Off.
YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in Tweets
The useful BiLSTM (Bidirectional Long-Short Term Memory) model with attention mechanism was mainly applied for our system.
YNU-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on Twitter
This paper describe the system we proposed to participate the first year of Irony detection in English tweets competition.
YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense Knowledge
Typically, there are several questions for each text that correspond to it.
YNU-HPCC at SemEval-2018 Task 12: The Argument Reasoning Comprehension Task Using a Bi-directional LSTM with Attention Model
This paper introduces a bi-directional long short term memory (Bi-LSTM) with an attention model to select a correct warrant from two to explain an argument.
YNU-HPCC at SemEval-2018 Task 2: Multi-ensemble Bi-GRU Model with Attention Mechanism for Multilingual Emoji Prediction
This paper describes our approach to SemEval-2018 Task 2, which aims to predict the most likely associated emoji, given a tweet in English or Spanish.
YNU-HPCC at IJCNLP-2017 Task 5: Multi-choice Question Answering in Exams Using an Attention-based LSTM Model
A shared task is a typical question answering task that aims to test how accurately the participants can answer the questions in exams.
YNU-HPCC at IJCNLP-2017 Task 4: Attention-based Bi-directional GRU Model for Customer Feedback Analysis Task of English
This paper describes our submission to IJCNLP 2017 shared task 4, for predicting the tags of unseen customer feedback sentences, such as comments, complaints, bugs, requests, and meaningless and undetermined statements.
YNU-HPCC at IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis Using a Bi-directional LSTM-CRF Model
Building a system to detect Chinese grammatical errors is a challenge for natural-language processing researchers.
IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases
This paper presents the IJCNLP 2017 shared task on Dimensional Sentiment Analysis for Chinese Phrases (DSAP) which seeks to identify a real-value sentiment score of Chinese single words and multi-word phrases in the both valence and arousal dimensions.
YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction
In this paper, we present a system that uses a convolutional neural network with long short-term memory (CNN-LSTM) model to complete the task.
Refining Word Embeddings for Sentiment Analysis
Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks.
YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classification
In this paper, we propose a multi-channel convolutional neural network-long short-term memory (CNN-LSTM) model that consists of two parts: multi-channel CNN and LSTM to analyze the sentiments of short English messages from Twitter.
Chinese Grammatical Error Diagnosis Using Single Word Embedding
A computer-assisted learning tool which can automatically detect and correct Chinese grammatical errors is necessary for those foreign students.
