Search Results for author:
Found 39 papers, 5 papers with code
A New Intelligent Reflecting Surface-Aided Electromagnetic Stealth Strategy
Electromagnetic wave absorbing material (EWAM) plays an essential role in manufacturing stealth aircraft, which can achieve the electromagnetic stealth (ES) by reducing the strength of the signal reflected back to the radar system.
Adaptive Split Learning over Energy-Constrained Wireless Edge Networks
Then, a two-layer optimization method is proposed to solve the MIP problem.
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
To address this, we propose the Information Bottleneck-based Gradient (\texttt{IBG}) explanation framework for ABSA.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation
The evidential uncertainty measure is extended to quantify the uncertainty in emotion distribution estimation.
Parameter Efficient Finetuning for Speech Emotion Recognition and Domain Adaptation
Foundation models have shown superior performance for speech emotion recognition (SER).
Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement
Few-shot prompting elicits the remarkable abilities of large language models by equipping them with a few demonstration examples in the input.
Transferring speech-generic and depression-specific knowledge for Alzheimer's disease detection
Apart from the knowledge from speech-generic representations, this paper also proposes to simultaneously transfer the knowledge from a speech depression detection task based on the high comorbidity rates of depression and AD.
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation
Human annotator simulation (HAS) serves as a cost-effective substitute for human evaluation such as data annotation and system assessment.
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition
In this paper, we explored how to boost speech emotion recognition (SER) with the state-of-the-art speech pre-trained model (PTM), data2vec, text generation technique, GPT-4, and speech synthesis technique, Azure TTS.
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints.
Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for Conversations
Two metrics are proposed to evaluate AER performance with automatic segmentation based on time-weighted emotion and speaker classification errors.
Is ChatGPT a Good Personality Recognizer? A Preliminary Study
Concretely, we employ a variety of prompting strategies to explore ChatGPT's ability in recognizing personality from given text, especially the level-oriented prompting strategy we designed for guiding ChatGPT in analyzing given text at a specified level.
A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling
Motivated by this, we propose a positive-augmentation and positive-mixup positive-unlabeled metric learning framework (P3M).
Document-level RE with incomplete labeling
Metric Learning
+1
Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression
In automatic emotion recognition (AER), labels assigned by different human annotators to the same utterance are often inconsistent due to the inherent complexity of emotion and the subjectivity of perception.
Digital Twin-Assisted Resource Demand Prediction for Multicast Short Video Streaming
In this paper, we propose a digital twin (DT)-assisted resource demand prediction scheme to enhance prediction accuracy for multicast short video streaming.
Self-supervised representations in speech-based depression detection
This paper proposes handling training data sparsity in speech-based automatic depression detection (SDD) using foundation models pre-trained with self-supervised learning (SSL).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
PEGA: Personality-Guided Preference Aggregator for Ephemeral Group Recommendation
The role of personality in our approach is twofold: (1) To estimate individual users' importance in a group and provide explainability; (2) to alleviate the data sparsity issue that occurred in ephemeral groups.
Tell Model Where to Attend: Improving Interpretability of Aspect-Based Sentiment Classification via Small Explanation Annotations
Gradient-based explanation methods play an important role in the field of interpreting complex deep neural networks for NLP models.
Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications
Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient.
Holistic Network Virtualization and Pervasive Network Intelligence for 6G
The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively.
Performance Analysis and Enhancement of Beamforming Training in 802.11ad
Since the derived BF training efficiency is an implicit function, to reveal the relationship between system parameters and BF training performance, we also derive an approximate expression of BF training efficiency.
Beef up mmWave Dense Cellular Networks with D2D-Assisted Cooperative Edge Caching
In this paper, we propose a device-to-device (D2D) assisted cooperative edge caching (DCEC) policy for millimeter (mmWave) dense networks, which cooperatively utilizes the cache resource of users and SBSs in proximity.
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks
Since the problem is NP-hard due to coupled network planning and network operation stages, we develop a Two timescAle netWork Slicing (TAWS) algorithm by collaboratively integrating reinforcement learning (RL) and optimization methods, which can jointly make network planning and operation decisions.
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning
Specifically, sampling rate adaption, inference task offloading and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services.
Digital Twin-Assisted Collaborative Transcoding for Better User Satisfaction in Live Streaming
Particularly, two DTs are constructed for emulating the cloud-edge collaborative transcoding process by analyzing spatial-temporal information of individual videos and transcoding configurations of transcoding queues, respectively.
Distribution-based Emotion Recognition in Conversation
Automatic emotion recognition in conversation (ERC) is crucial for emotion-aware conversational artificial intelligence.
Self-Attentive Sequential Recommendation with Cheap Causal Convolutions
We evaluate the effectiveness of the proposed model in terms of both accurate and calibrated sequential recommendation.
Personalized QoE Enhancement for Adaptive Video Streaming: A Digital Twin-Assisted Scheme
In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE).
Climate and Weather: Inspecting Depression Detection via Emotion Recognition
Automatic depression detection has attracted increasing amount of attention but remains a challenging task.
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity.
Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors
In this paper, a novel Bayesian training loss based on per-utterance Dirichlet prior distributions is proposed for verbal emotion recognition, which models the uncertainty in one-hot labels created when human annotators assign the same utterance to different emotion classes.
Multi-channel Attentive Graph Convolutional Network With Sentiment Fusion For Multimodal Sentiment Analysis
This paper proposes a Multi-channel Attentive Graph Convolutional Network (MAGCN), consisting of two main components: cross-modality interactive learning and sentimental feature fusion.
Long-Tail Session-based Recommendation from Calibration
Accurate predictions in session-based recommendations have progressed, but a few studies have focused on skewed recommendation lists caused by popularity bias.
AI-Native Network Slicing for 6G Networks
Then, network slicing solutions are studied to support emerging AI services by constructing AI instances and performing efficient resource management, i. e., slicing for AI.
Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning
To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost.
Emotion recognition by fusing time synchronous and time asynchronous representations
In this paper, a novel two-branch neural network model structure is proposed for multimodal emotion recognition, which consists of a time synchronous branch (TSB) and a time asynchronous branch (TAB).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in Space-Air-Ground Integrated Network
Our objective is to design a task scheduling policy that minimizes offloading and computing delay of all tasks given the UAV energy capacity constraint.
Pyramid Focusing Network for mutation prediction and classification in CT images
Predicting the mutation status of genes in tumors is of great clinical significance.
Fast mmwave Beam Alignment via Correlated Bandit Learning
Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems.
