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Found 48 papers, 12 papers with code
Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning Network for Semi-supervised 3D Medical Image Segmentation
Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data.
Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels
In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks.
Separate Motion from Appearance: Customizing Motion via Customizing Text-to-Video Diffusion Models
Extensive experiments demonstrate that compared to previous works, our method can generate videos with appearance more aligned with the text descriptions and motion more consistent with the reference videos.
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
TAPFed uses a proposed threshold functional encryption scheme and allows for a certain number of malicious aggregators while maintaining security and privacy.
LLMs are Also Effective Embedding Models: An In-depth Overview
This survey serves as a valuable resource for researchers and practitioners by synthesizing current advancements, highlighting key challenges, and offering a comprehensive framework for future work aimed at enhancing the effectiveness and efficiency of LLMs as embedding models.
Semantic Feature Division Multiple Access for Multi-user Digital Interference Networks
To address these challenges, in this paper, we propose a novel discrete semantic feature division multiple access (SFDMA) paradigm for multi-user digital interference networks.
Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making
In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole.
Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making
Artificial Intelligence (AI) is increasingly employed in various decision-making tasks, typically as a Recommender, providing recommendations that the AI deems correct.
Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students
The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes in project-based learning (PBL).
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges
In the rapidly evolving domain of Natural Language Generation (NLG) evaluation, introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e. g., coherence, creativity, and context relevance.
Voila-A: Aligning Vision-Language Models with User's Gaze Attention
In this paper, we introduce gaze information, feasibly collected by AR or VR devices, as a proxy for human attention to guide VLMs and propose a novel approach, Voila-A, for gaze alignment to enhance the interpretability and effectiveness of these models in real-world applications.
Feasibility Conditions for Mobile LiFi
Then, we derive the expression of LiFi transmission rate based on the m-pulse-amplitude-modulation (M-PAM).
MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation
Graph Edit Distance (GED) is a general and domain-agnostic metric to measure graph similarity, widely used in graph search or retrieving tasks.
Re-Reading Improves Reasoning in Large Language Models
To enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs), we introduce a simple, yet general and effective prompting method, Re2, i. e., \textbf{Re}-\textbf{Re}ading the question as input.
Joint Beamforming Design for RIS-enabled Integrated Positioning and Communication in Millimeter Wave Systems
Integrated positioning and communication (IPAC) system and reconfigurable intelligent surface (RIS) are both considered to be key technologies for future wireless networks.
Robust Power Allocation for UAV-aided ISAC Systems with Uncertain Location Sensing Errors
Unmanned aerial vehicle (UAV) holds immense potential in integrated sensing and communication (ISAC) systems for the Internet of Things (IoT).
Mathematical Characterization of Signal Semantics and Rethinking of the Mathematical Theory of Information
Shannon information theory is established based on probability and bits, and the communication technology based on this theory realizes the information age.
Features Disentangled Semantic Broadcast Communication Networks
Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users' intended semantic features are selected for broadcasting, which can further improve the transmission efficiency.
Task-oriented Explainable Semantic Communications
Furthermore, based on the $\beta $-variational autoencoder ($\beta $-VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise.
Global Algorithms for Mean-Variance Optimization in Markov Decision Processes
In this paper, we propose a new approach to find the globally optimal policy for combined metrics of steady-state mean and variance in an infinite-horizon undiscounted MDP.
Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making
Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers.
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making
To mitigate this gap, we proposed to promote humans' appropriate trust based on the CL of both sides at a task-instance level.
Joint Beamforming and PD Orientation Design for Mobile Visible Light Communications
To address this challenge, we propose an alternating optimization algorithm to obtain the transmit beamforming and the PD orientation.
HORIZON: High-Resolution Semantically Controlled Panorama Synthesis
Panorama synthesis endeavors to craft captivating 360-degree visual landscapes, immersing users in the heart of virtual worlds.
Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation
Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver.
Self-supervised Learning for Heterogeneous Graph via Structure Information based on Metapath
Empirical results validate the performance of SESIM method and demonstrate that this method can improve the representation ability of traditional neural networks on link prediction task and node classification task.
Ered: Enhanced Text Representations with Entities and Descriptions
On the one hand, it is implicit and only model weights are paid attention to, the pre-trained entity embeddings are ignored.
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning
Keeping risk under control is often more crucial than maximizing expected rewards in real-world decision-making situations, such as finance, robotics, autonomous driving, etc.
TraceNet: Tracing and Locating the Key Elements in Sentiment Analysis
In this paper, we study sentiment analysis task where the outcomes are mainly contributed by a few key elements of the inputs.
KESA: A Knowledge Enhanced Approach For Sentiment Analysis
On the contrary, the second task predicts the overall sentiment polarity given the sentiment polarity of the word as prior knowledge.
A unified algorithm framework for mean-variance optimization in discounted Markov decision processes
To deal with this unorthodox problem, we introduce a pseudo mean to transform the untreatable MDP to a standard one with a redefined reward function in standard form and derive a discounted mean-variance performance difference formula.
SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation
While pre-training techniques are working very well in natural language processing, how to pre-train a decoder and effectively use it for neural machine translation (NMT) still remains a tricky issue.
Control Image Captioning Spatially and Temporally
Moreover, the controllability and explainability of LoopCAG are validated by analyzing spatial and temporal sensitivity during the generation process.
Ranked #1 on
Image Captioning
on Localized Narratives
SceneRec: Scene-Based Graph Neural Networks for Recommender Systems
Collaborative filtering has been largely used to advance modern recommender systems to predict user preference.
CASS: Towards Building a Social-Support Chatbot for Online Health Community
Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community.
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models.
A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation
The commonly used framework for unsupervised machine translation builds initial translation models of both translation directions, and then performs iterative back-translation to jointly boost their translation performance.
A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction
To deal with those issues, in this paper, we propose a novel graph-based paradigm to induce bilingual lexicons in a coarse-to-fine way.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+2
Easy First Relation Extraction with Information Redundancy
Many existing relation extraction (RE) models make decisions globally using integer linear programming (ILP).
Explicit Cross-lingual Pre-training for Unsupervised Machine Translation
Pre-training has proven to be effective in unsupervised machine translation due to its ability to model deep context information in cross-lingual scenarios.
Variance-Based Risk Estimations in Markov Processes via Transformation with State Lumping
In the numerical experiment, we illustrate state lumping in the SAT, errors from a naive reward simplification, and the validity of the SAT for the two risk estimations.
A Scheme for Dynamic Risk-Sensitive Sequential Decision Making
For a given risk-sensitive problem, in which the objective and constraints are, or can be estimated by, functions of the mean and variance of return, we generate a synthetic dataset as training data.
Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms
The dataset is available online, which contains eight types of modulated signals.
Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics
In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems.
Unsupervised Neural Machine Translation with SMT as Posterior Regularization
To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process.
Triangular Architecture for Rare Language Translation
Neural Machine Translation (NMT) performs poor on the low-resource language pair $(X, Z)$, especially when $Z$ is a rare language.
State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning
In the framework of MDP, although the general reward function takes three arguments-current state, action, and successor state; it is often simplified to a function of two arguments-current state and action.
Transition-based versus State-based Reward Functions for MDPs with Value-at-Risk
Thirdly, since the estimation method is for a Markov reward process with the reward function on current state only, we present a transformation algorithm for the Markov reward process with the reward function on current and next states, in order to estimate the VaR function with an intact total reward distribution.
