3,989 Result(s)
within
Chapter
**u-**u Zhang
Computer Science
Information Storage and Retrieval
Information Systems Applications (incl. Internet)
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Chapter and Conference Paper
Evaluation of Deep Reinforcement Learning Based Stock Trading
Stock is one of the most important targets in investment. However, it is challenging to manually design a profitable strategy in the highly dynamic and complex stock market. Modern portfolio management usually...
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Chapter and Conference Paper
Syntax-Aware Transformer for Sentence Classification
Sentence classification is a significant task in natural language processing (NLP) and is applied in many fields. The syntactic and semantic properties of words and phrases often determine the success of sente...
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Chapter and Conference Paper
Enhance Performance of Ad-hoc Search via Prompt Learning
Recently, pre-trained language models (PTM) have achieved great success on ad hoc search. However, the performance decline in low-resource scenarios demonstrates the capability of PTM has not been inspired ful...
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Chapter and Conference Paper
Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations
Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural representations have shown great su...
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Chapter and Conference Paper
Contrastive Deep Knowledge Tracing
Knowledge tracing (KT) aims to predict student performance on the next question according to historical records. Recently deep learning-based models for KT task successfully modeling student responses receive ...
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Chapter and Conference Paper
Extreme Multi-label Classification with Hierarchical Multi-task for Product Attribute Identification
Identification of product attributes (product type, brand, color, gender, etc.) from a query is critically important for e-commerce search systems, especially the identification of brand intent. Recently, Name...
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Chapter and Conference Paper
Epidemic Modeling of the Spatiotemporal Spread of COVID-19 over an Intercity Population Mobility Network
Intercity traveling has been recognized as a leading cause for the continuation of the COVID-19 global pandemic. However, there lacks credible prediction of the spatiotemporal spread of COVID-19 with humans tr...
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Chapter and Conference Paper
DHA: Product Title Generation with Discriminative Hierarchical Attention for E-commerce
Product titles play an important role in E-Commerce sites. However, manually crafting product titles needs tremendous time and human effort. It is expected that product titles can be automatically generated, b...
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Chapter and Conference Paper
Anti-Clone: A Lightweight Approach for RFID Cloning Attacks Detection
Millions of radio frequency identification (RFID) tags are pervasively used all around the globe to identify a wide variety of objects inexpensively. However, the tag cannot use energy-hungry cryptography due ...
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Chapter and Conference Paper
Research on Depth-Adaptive Dual-Arm Collaborative Gras** Method
Among the existing dual-arm cooperative gras** methods, the dual-arm cooperative gras** method based on RGB camera is the mainstream intelligent method. However, these methods often require predefined dept...
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Chapter and Conference Paper
An Oriented Attention Model for Infectious Disease Cases Prediction
Effective infectious disease prediction supports the success of infection prevention and control. Several attention-based predictive models can be applied to undertake the prediction task. However, using a sin...
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Chapter and Conference Paper
ITAR: A Method for Indoor RFID Trajectory Automatic Recovery
With the increasing popularity of Radio Frequency Identification (RFID) technology, indoor applications based on RFID trajectory data analysis are becoming more and more extensive, such as personnel location, ...
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Chapter and Conference Paper
Semantic SLAM for Mobile Robot with Human-in-the-Loop
Mobile robots are an important participant in today’s modern life, and have huge commercial application prospects in the fields of unmanned security inspection, logistics, express delivery, cleaning and medica...
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Chapter and Conference Paper
Deep Reinforcement Learning for Multi-UAV Exploration Under Energy Constraints
Autonomous exploration is the essential task for various applications of unmanned aerial vehicles (UAVs), but there is currently a lack of available energy-constrained multi-UAV exploration methods. In this pa...
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Chapter and Conference Paper
A Longitudinal Measurement and Analysis of Pink, a Hybrid P2P IoT Botnet
With the ubiquitous deployment of Internet of Things (IoT) devices in many fields, more and more IoT botnets have taken a variety of penetration methods to infect vulnerable IoT devices. Nowadays, a substantia...
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Chapter and Conference Paper
S \(^2\) QL: Retrieval Augmented Zero-Shot Question Answering over Knowledge Graph
Knowledge Graph Question Answering (KGQA) is a challenging task that aims to obtain the entities from the given Knowledge Graph (KG) to answer the user’s natural language questions. Most existing studies are f...
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Chapter and Conference Paper
Rethinking Adjacent Dependency in Session-Based Recommendations
Session-based recommendations (SBRs) recommend the next item for an anonymous user by modeling the dependencies between items in a session. Benefiting from the superiority of graph neural networks (GNN) in learni...
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Chapter and Conference Paper
Ultra-short-Term Load Forecasting Model Based on VMD and TGCN-GRU
Load forecasting is to use historical load information to estimate the load demand for a period of time in the future. At present, mode decomposition algorithm is often used in the field to improve the forecas...
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Chapter and Conference Paper
Two-Stage Traffic Clustering Based on HNSW
Traffic flow clustering is a common task to analyze urban traffic using GPS data of urban vehicles. Existing density-based traffic flow clustering methods generally have two important problems, that is to not ...
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Chapter and Conference Paper
Evading Encrypted Traffic Classifiers by Transferable Adversarial Traffic
Machine learning algorithms have been widely leveraged in traffic classification tasks to overcome the challenges brought by the enormous encrypted traffic. On the contrary, ML-based classifiers introduce adve...
