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Found 20 papers, 1 papers with code
Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems
While preference-based recommendation algorithms effectively enhance user engagement by recommending personalized content, they often result in the creation of ``filter bubbles''.
Detecting misinformation through Framing Theory: the Frame Element-based Model
In this paper, we delve into the rapidly evolving challenge of misinformation detection, with a specific focus on the nuanced manipulation of narrative frames - an under-explored area within the AI community.
BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations
This approach endeavors to transcend the constraints of the filter bubble, enrich recommendation diversity, and strike a belief balance among users while also catering to user preferences and system-specific business requirements.
AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization
In this paper, we propose an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews, which effectively captures the adaptive nature required for opinion summarization.
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis
Aspect term extraction is a fundamental task in fine-grained sentiment analysis, which aims at detecting customer's opinion targets from reviews on product or service.
DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems
In this paper, we propose a novel method to address the complex problem of news recommendation.
Rapid-Motion-Track: Markerless Tracking of Fast Human Motion with Deeper Learning
RMT and a range of DLC models were applied to the video data with tapping frequencies up to 8Hz to extract movement features.
A Light-weight, Effective and Efficient Model for Label Aggregation in Crowdsourcing
Due to the dynamic nature, the proposed algorithms can also estimate true labels online without re-visiting historical data.
Hybrid CNN -Interpreter: Interpret local and global contexts for CNN-based Models
Convolutional neural network (CNN) models have seen advanced improvements in performance in various domains, but lack of interpretability is a major barrier to assurance and regulation during operation for acceptance and deployment of AI-assisted applications.
Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation
Recently, diffusion models have been proven to perform remarkably well in text-to-image synthesis tasks in a number of studies, immediately presenting new study opportunities for image generation.
Ranked #1 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
A Comprehensive Review on Deep Supervision: Theories and Applications
Deep supervision, or known as 'intermediate supervision' or 'auxiliary supervision', is to add supervision at hidden layers of a neural network.
GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks
The experimental results indicate that GAC can learn and apply effective incentive allocation policies in unknown social networks and outperform existing incentive allocation approaches.
Parallel Multi-Scale Networks with Deep Supervision for Hand Keypoint Detection
Keypoint detection plays an important role in a wide range of applications.
Hand gesture detection in tests performed by older adults
We have implemented a hand gesture detector to detect the gestures in the hand movement tests and our detection mAP is 0. 782 which is better than the state-of-the-art.
Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction
To tackle the aforementioned challenges, in this paper, we propose a novel algorithm for exploring influential users in unknown networks, which can estimate the influential relationships among users based on their historical behaviors and without knowing the topology of the network.
Graph-based Joint Pandemic Concern and Relation Extraction on Twitter
However, detecting concerns in time from massive information in social media turns out to be a big challenge, especially when sufficient manually labeled data is in the absence of public health emergencies, e. g., COVID-19.
Applications of Artificial Intelligence to aid detection of dementia: a narrative review on current capabilities and future directions
With populations ageing, the number of people with dementia worldwide is expected to triple to 152 million by 2050.
ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks
A key step in influence maximization in online social networks is the identification of a small number of users, known as influencers, who are able to spread influence quickly and widely to other users.
Privacy Information Classification: A Hybrid Approach
A large amount of information has been published to online social networks every day.
An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification
In this study, we proposed a deep learning framework guided by the visual evoked potentials, called the Event-Related Potential (ERP)-Long short-term memory (LSTM) framework, extracted by EEG signals for visual classification.
