Aoying Zhou
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View article: Land Deformation Prediction via Multi-modal Adaptive Association Learning
Land Deformation Prediction via Multi-modal Adaptive Association Learning Open
View article: TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting
TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting Open
View article: A Supply–Demand Balance Guided Hierarchical Reinforcement Learning Approach for Truck-Cargo Matching
A Supply–Demand Balance Guided Hierarchical Reinforcement Learning Approach for Truck-Cargo Matching Open
Truck-cargo matching is one of the core tasks of online freight platforms, where the primary objective is to optimally assign each cargo task to the most suitable truck. The existing matching strategies seek to maximize total transported w…
View article: EasyTime: Time Series Forecasting Made Easy
EasyTime: Time Series Forecasting Made Easy Open
Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-cli…
View article: Open Source Evaluatology: An evaluation framework and methodology for open source ecosystems based on evaluatology
Open Source Evaluatology: An evaluation framework and methodology for open source ecosystems based on evaluatology Open
The open-source ecosystem, as an important component of the modern software industry, has increasingly attracted attention from both academia and industry regarding its evaluation. However, current open-source evaluation methods face sever…
View article: Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems
Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems Open
Air pollution significantly threatens human health and ecosystems, necessitating effective air quality prediction to inform public policy. Traditional approaches are generally categorized into physics-based and data-driven models. Physics-…
View article: TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting
TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting Open
Time Series Forecasting (TSF) is key functionality in numerous fields, such as financial investment, weather services, and energy management. Although increasingly capable TSF methods occur, many of them require domain-specific data collec…
View article: TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods Open
Time series are generated in diverse domains such as economic, traffic, health, and energy, where forecasting of future values has numerous important applications. Not surprisingly, many forecasting methods are being proposed. To ensure pr…
View article: Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends
Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends Open
Natural Language Processing (NLP) aims to analyze text or speech via techniques in the computer science field. It serves applications in the domains of healthcare, commerce, education, and so on. Particularly, NLP has been widely applied t…
View article: Multi-View Context Awareness based Transport Stay Hotspot Recognization
Multi-View Context Awareness based Transport Stay Hotspot Recognization Open
During long distance transporting for bulk commodities, the trucks need to stop off at multiple places for resting, refueling, repairing or unloading, which are important in transport route planning, called as transport stay hotspots (or T…
View article: Fast Commitment for Geo-Distributed Transactions via Decentralized Co-coordinators
Fast Commitment for Geo-Distributed Transactions via Decentralized Co-coordinators Open
In a geo-distributed database, data shards and their respective replicas are deployed in distinct datacenters across multiple regions, enabling regional-level disaster recovery and the ability to serve global users locally. However, transa…
View article: TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification
TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification Open
Text classification is one of the most imperative tasks in natural language processing (NLP). Recent advances with pre-trained language models (PLMs) have shown remarkable success on this task. However, the satisfying results obtained by P…
View article: Uncertainty-Aware Self-Training for Low-Resource Neural Sequence Labeling
Uncertainty-Aware Self-Training for Low-Resource Neural Sequence Labeling Open
Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc. However, the satisfying results achieved by tradit…
View article: Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling
Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling Open
Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc. However, the satisfying results achieved by tradit…
View article: Meta-Learning Siamese Network for Few-Shot Text Classification
Meta-Learning Siamese Network for Few-Shot Text Classification Open
Few-shot learning has been used to tackle the problem of label scarcity in text classification, of which meta-learning based methods have shown to be effective, such as the prototypical networks (PROTO). Despite the success of PROTO, there…
View article: TransPrompt v2: Transferable Prompt-based Fine-tuning for Few-shot Text Classification
TransPrompt v2: Transferable Prompt-based Fine-tuning for Few-shot Text Classification Open
Recent studies have shown that prompt-based fine-tuning improves the performance of large Pre-trained Language Models (PLMs) for few-shot text classification. Specifically, this type of method transforms the text classification task into t…
View article: Understanding Long Programming Languages with Structure-Aware Sparse Attention
Understanding Long Programming Languages with Structure-Aware Sparse Attention Open
Programming-based Pre-trained Language Models (PPLMs) such as CodeBERT have achieved great success in many downstream code-related tasks. Since the memory and computational complexity of self-attention in the Transformer grow quadratically…
View article: GypSum
GypSum Open
Code summarization with deep learning has been widely studied in recent\nyears. Current deep learning models for code summarization generally follow the\nprinciple in neural machine translation and adopt the encoder-decoder\nframework, whe…
View article: Are current benchmarks adequate to evaluate distributed transactional databases?
Are current benchmarks adequate to evaluate distributed transactional databases? Open
With the rapid development of distributed transactional databases in recent years, there is an urgent need for fair performance evaluation and comparison. Though there are various open-source benchmarks built for databases, it is lack of a…
View article: Dynamic Feature Selection for Structural Image Content Recognition
Dynamic Feature Selection for Structural Image Content Recognition Open
View article: Programming Knowledge Tracing: A Comprehensive Dataset and A New Model
Programming Knowledge Tracing: A Comprehensive Dataset and A New Model Open
In this paper, we study knowledge tracing in the domain of programming education and make two important contributions. First, we harvest and publish so far the most comprehensive dataset, namely BePKT, which covers various online behaviors…
View article: Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion
Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion Open
We present InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sample is predictable with supportive data in the training set. …
View article: Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification Open
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the exploitation of lexical features and their distributional s…
View article: Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification Open
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance.However, existing solutions heavily rely on the exploitation of lexical features and their distributional si…
View article: On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner
On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner Open
Author disambiguation arises when different authors share the same name, which is a critical task in digital libraries, such as DBLP, CiteULike, CiteSeerX, etc. While the state-of-the-art methods have developed various paper embedding-base…
View article: Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction
Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction Open
Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts.However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, le…
View article: The impact of data flow computing thinking on the development of computer architecture
The impact of data flow computing thinking on the development of computer architecture Open
Throughout the entire history of computer architecture, the von Neumann model has been the most mainstream model for computer systems architecture.Data flow computer systems are undoubtedly the most well-studied type of non-von Neumann com…
View article: LinSBFT: Linear-Communication One-Step BFT Protocol for Public Blockchains
LinSBFT: Linear-Communication One-Step BFT Protocol for Public Blockchains Open
This paper presents LinSBFT, a Byzantine Fault Tolerance (BFT) protocol with the capacity of processing over 2000 smart contract transactions per second in production. LinSBFT applies to a permissionless, public blockchain system, in which…
View article: EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression Recognition
EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression Recognition Open
Printed Mathematical expression recognition (PMER) aims to transcribe a printed mathematical expression image into a structural expression, such as LaTeX expression. It is a crucial task for many applications, including automatic question …
View article: F-HMTC: Detecting Financial Events for Investment Decisions Based on Neural Hierarchical Multi-Label Text Classification
F-HMTC: Detecting Financial Events for Investment Decisions Based on Neural Hierarchical Multi-Label Text Classification Open
The share prices of listed companies in the stock trading market are prone to be influenced by various events. Performing event detection could help people to timely identify investment risks and opportunities accompanying these events. Th…