593 Result(s)
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Chapter and Conference Paper
Lottery4CVR: Neuron-Connection Level Sharing for Multi-task Learning in Video Conversion Rate Prediction
As a fundamental task of industrial ranking systems, conversion rate (CVR) prediction is suffering from data sparsity problems. Most conventional CVR modeling leverages Click-through rate (CTR) &CVR multitask ...
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Chapter and Conference Paper
Neural HD Map Generation from Multiple Vectorized Tiles Locally Produced by Autonomous Vehicles
High-definition (HD) map is a fundamental component of autonomous driving systems, as it can provide precise environmental information about driving scenes. Recent work on vectorized map generation could produ...
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Chapter and Conference Paper
RDC-YOLOv5: Improved Safety Helmet Detection in Adverse Weather
Outdoor construction sites are frequently affected by fog and various adverse weather conditions, resulting in a decline in the quality of the captured images. This deterioration ultimately leads to a signific...
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Chapter and Conference Paper
Defeating the Non-stationary Opponent Using Deep Reinforcement Learning and Opponent Modeling
In the cyber attack and defense process, the opponent’s strategy is often dynamic, random, and uncertain. Especially in an advanced persistent threat scenario, it is not easy to capture its behavior strategy w...
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Chapter and Conference Paper
Detect Depression from Social Networks with Sentiment Knowledge Sharing
Social network plays an important role in propagating people’s viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered ...
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Chapter and Conference Paper
The Diffusion of Vaccine Hesitation: Media Visibility Versus Scientific Authority
[Purpose/Significance] This study quantifies media visibility and scientific authority of vaccine scientists and anti-vaxxers. We analyze differences and associations through media co-occurrence and scientific in...
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Chapter and Conference Paper
An Accuracy Evaluation Method for Multi-source Data Based on Hexagonal Global Discrete Grids
As a new form of data management, the global discrete grid can describe and exchange geographic information in a standardized way on a global scale, which can be used for efficient storage and application of l...
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Chapter and Conference Paper
DPIM: Dynamic Pricing Incentive Mechanism for Mobile Crowd Sensing
As an emerging paradigm for collecting sensory data, Mobile Crowd Sensing (MCS) technology has found widespread application. The successful application of MCS technology relies not only on the active participa...
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Chapter and Conference Paper
Role-Guided Contrastive Learning for Event Argument Extraction
Event argument extraction is a subtask of information extraction. Recent efforts have predominantly focused on mitigating the issue of error propagation associated with pipeline methods for extracting event ar...
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Chapter and Conference Paper
A Multi-span-Based Conditional Information Extraction Model
Conditional information extraction plays an important role in medical information extraction applications, such as medical information retrieval, medical knowledge graph construction, intelligent diagnosis and...
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Chapter and Conference Paper
Dynamic Weighted Neural Bellman-Ford Network for Knowledge Graph Reasoning
Recent studies have shown that subgraphs of the head entity, such as related relations and neighborhoods, are helpful for Knowledge Graph Reasoning (KGR). However, prior studies tend to focus solely on enhanci...
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Chapter and Conference Paper
Text Mining Task for “Gene-Disease” Association Semantics in CHIP 2022
Gene-disease association plays a crucial role in healthcare knowledge discovery, and a large amount of valuable information is hidden in the literature. To alleviate this problem, we designed and organized the...
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Chapter and Conference Paper
Decompilation Based Deep Binary-Source Function Matching
Binary and source matching is vital for vulnerability detection or program comprehension. Most existing works focus on library matching (coarse-grained) by utilizing some simple features. However, they are so ...
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Chapter and Conference Paper
Adaptively Secure Constrained Verifiable Random Function
Constrained Verifiable Random Function (CVRF) is a powerful variant of Pseudorandom Function (PRF). Simply put, CVRF asks the outputs of PRF to be verifiable and the secret key of PRF to be delegatable, thus simu...
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Chapter and Conference Paper
Intention-Aware Neural Networks for Question Paraphrase Identification
We tackle Question Paraphrasing Identification (QPI), a task of determining whether a pair of interrogative sentences (i.e., questions) are paraphrases of each other, which is widely applied in information ret...
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Chapter and Conference Paper
Feature Differentiation and Fusion for Semantic Text Matching
Semantic Text Matching (STM for short) stands for the task of automatically determining the semantic similarity for a pair of texts. It has been widely applied in a variety of downstream tasks, e.g., informati...
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Chapter
Recommender Systems
Recommender systems have achieved widespread success in real-life applications. Personalized recommendation can reduce customers’ effort in finding items they are interested in. It is also critical in some ind...
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Chapter and Conference Paper
How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation
Graph neural networks (GNNs), as a group of powerful tools for representation learning on irregular data, have manifested superiority in various downstream tasks. With unstructured texts represented as concept...
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Chapter
Deep Learning for Recommender Systems
Deep neural networks have been serving as the main driving force for the emergence of cutting-edge applications in many areas including computer vision, speech recognition, natural language processing, etc. In...
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Chapter and Conference Paper
Exploiting Spatial Attention and Contextual Information for Document Image Segmentation
We propose a new framework of combining an attention mechanism with a conditional random field to deal with a document image segmentation task. The framework aims to recognize homogeneous regions, e.g. text, f...
