431 Result(s)
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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 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...
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Chapter and Conference Paper
Bi-granularity Adversarial Training for Non-factoid Answer Retrieval
Answer Retrieval is a task of automatically retrieving relevant answers towards a specific question. The recent studies, in this field, have witnessed the vast success of non-factoid QA methods which leverage ...
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Chapter and Conference Paper
Unsafe Driving Behavior Prediction for Electric Vehicles
There is an increasing availability of electric vehicles in recent years. With the revolutionary motors and electric modules within the electric vehicles, the instant reactions bring up not only improved drivi...
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Chapter and Conference Paper
Analyze and Evaluate Database-Backed Web Applications with WTool
Web applications demand low latency. In database-backed web applications, the latency is sensitive to the efficiency of database access. Hence, previous works have proposed various techniques to optimize the d...
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Chapter and Conference Paper
\(\mathbb {PSG}\) : Local Privacy Preserving Synthetic Social Graph Generation
Social graph, as a representation of the network topology, contains users’ social relationship. In order to obtain a social graph, a server requires users to submit their relationships. As we know, using or pu...
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Chapter and Conference Paper
A Distribution-Aware Training Scheme for Learned Indexes
The recent proposal of the learned index leads us to a new direction to optimize indexes. With the help of learned models, it has demonstrated promising performance improvement compared with traditional indexe...
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Chapter and Conference Paper
Location Differential Privacy Protection in Task Allocation for Mobile Crowdsensing Over Road Networks
Mobile Crowdsensing (MCS) platforms often require workers to provide their locations for task allocation, which may cause privacy leakage
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Chapter and Conference Paper
Loopster++: Termination Analysis for Multi-path Linear Loop
Loop structure is widely adopted in many applications, e.g. collaborative applications, social network applications, and edge computing. And the termination of the loop is of great significance to the correctness...
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Chapter and Conference Paper
Multi-D3QN: A Multi-strategy Deep Reinforcement Learning for Service Composition in Cloud Manufacturing
Service composition is an indispensable technology in the cloud manufacturing process to ensure the smooth execution of tasks. To implement effective and accurate service composition strategies, many researche...
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Chapter and Conference Paper
Building Linked Spatio-Temporal Data from Vectorized Historical Maps
Historical maps provide a rich source of information for researchers in the social and natural sciences. These maps contain detailed documentation of a wide variety of natural and human-made features and their...
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Chapter and Conference Paper
The Attitudes of Chinese Online Users Towards Movie Piracy: A Content Analysis
Movies piracy has raised growing concerns in digital time. However, most studies on movie piracy focused on university students, paying less attention to the exploration of the comprehensive factors among onli...
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Chapter and Conference Paper
An Effective Dual-Fisheye Lens Stitching Method Based on Feature Points
Fisheye lens is a super-wide-angle lens which is very light. Usually two cameras can shoot 360-degree panoramic images. However, the limited overlap** field of views make it hard to stitch in the boundaries....
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Chapter and Conference Paper
Clustering Study of Crowdsourced Test Report with Multi-source Heterogeneous Information
Crowdsourced testing is an emerging testing method in the field of software testing and industrial practice. Crowdsourced testing can provide a more realistic user experience. But crowdsourced workers are inde...
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Chapter and Conference Paper
Whether the Evolution of iSchool Revolves Around “Information, Technology and People”?
As the research topics and specialties of the Information Schools (iSchool) have always been evolving, the new trends in research are emerging constantly. Whether the evolution of iSchool is still pursuing its...
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Chapter
A Data Services Composition Approach for Continuous Query on Social Media Streams
We witness a rapid increase in the number of social media streams due to development of Web2.0, IoT and Cloud Computing technology. These sources include both traditional relational databases and streaming dat...
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Chapter and Conference Paper
Learning to Fuse Multiple Semantic Aspects from Rich Texts for Stock Price Prediction
Stock price prediction is challenging due to the non-stationary fluctuation of stock price, which can be influenced by the stochastic trading behaviors in the market. In recent years, researchers have focused ...
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Chapter and Conference Paper
Classifying Quality Centrality for Source Localization in Social Networks
Source localization, the process of estimating the originator of an epidemic outbreak or rumor propagation in a network, is an important issue in epidemiology and sociology. With the graph topology of the unde...
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Chapter and Conference Paper
Model and Practice of Crowd-Based Education
Based on connectivism pedagogy crowd-based education provides a practical method to extensively exploit wisdoms of core learners in education organization and external crowds on Internet. However, when applyin...
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Chapter and Conference Paper
Personalized Item-of-Interest Recommendation on Storage Constrained Smartphone Based on Word Embedding Quantization
In recent years, word embedding models receive tremendous research attentions due to their capability of capturing textual semantics. This study investigates the issue of employing word embedding models into r...