Qi Liu

A load-balancing approach based on modified K-ELM and NSGA-II in a heterogeneous cloud environment

2016

MapReduce is a popular programming model widely used in distributed systems. With regard to large-scale applications, e.g. home energy management in a city, online social community etc., load-balancing becomes critical affecting the performance of distributed …

A speculative execution strategy based on node classification and hierarchy index mechanism for heterogeneous Hadoop systems

2017

MapReduce (MR) has been widely used to process distributed large data sets. MRV2 working on Yarn, as a more advanced programing model, has gained lots of concerns. Meanwhile, speculative execution is known as an approach …

Data quality screening for high-resolution satellite imagery via spectral clustering

2017

High-resolution satellite imagery data have been widely used in geoscience and remote sensing research. Dealing with data quality issue is the first and most important step before truly making use of these high-resolution images. Scientific …

Confidence-Aware Matrix Factorization for Recommender Systems

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been widely used in recommender systems. The literature has reported that matrix factorization methods often produce superior accuracy of rating prediction in recommender systems. However, …

Interactive Attention Transfer Network for Cross-Domain Sentiment Classification

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Cross-domain sentiment classification refers to utilizing useful knowledge in the source domain to help sentiment classification in the target domain which has few or no labeled data. Most existing methods mainly concentrate on extracting common …

Homomorphic Consortium Blockchain for Smart Home System Sensitive Data Privacy Preserving

2019 · IEEE Access

The relative low level of smart home system (SHS) device information security may threaten the privacy of users. In this paper, we propose a homomorphic consortium blockchain for SHS sensitive data privacy preserving (HCB-SDPP), which …

Sentence-State LSTM for Text Representation

2018

Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, …

Alpha-Beta Sampling for Pairwise Ranking in One-Class Collaborative Filtering

2019

This paper introduces Alpha-Beta Sampling (ABS) strategy, which is particularly intended for the sampling problem of pairwise ranking in one-class collaborative filtering (PROCCF). Specifically, ABS strategy places more emphasis on such training examples, including positive …

Smart Contract Vulnerability Detection using Graph Neural Network

2020

The security problems of smart contracts have drawn extensive attention due to the enormous financial losses caused by vulnerabilities. Existing methods on smart contract vulnerability detection heavily rely on fixed expert rules, leading to low …

A Reinforcement Learning Based System for Minimizing Cloud Storage Service Cost

2020

Currently, many web applications are deployed on cloud storage service provided by cloud service providers (CSPs). A CSP offers different types of storage including hot, cold and archive storage and sets unit prices for these …

A 3D Simulation Environment and Navigation Approach for Robot Navigation via Deep Reinforcement Learning in Dense Pedestrian Environment

2020

With the rapid development of mobile robot technology, robots are playing an increasingly important role in people's daily lives. As one of the key technologies of the basic functions of mobile robots, navigation also needs …

Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation

2022 · Proceedings of the ACM Web Conference 2022

Recent years have witnessed great success in deep learning-based sequential recommendation (SR), which can provide more timely and accurate recommendations. One of the most effective deep SR architectures is to stack high-performance residual blocks, e.g., …

A smart speculative execution strategy based on node classification for heterogeneous Hadoop systems

2016 · 2016 18th International Conference on Advanced Communication Technology (ICACT)

MapReduce (MR) has been widely used to process distributed large data sets. Meanwhile, speculative execution is known as an approach for dealing with same problems by backing up those tasks running on a low performance …

A Novel Approach for Auto-Formulation of Optimization Problems

2023 · arXiv (Cornell University)

In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the …

A Novel Covert Timing Channel Based on Bitcoin Messages

2023 · IEEE Transactions on Computers

Covert channels serve the construction of cyberspace security. By realizing the secure transmission of data, it is widely used in political and financial fields. Blockchain covert channels have higher reliability and concealment compared to traditional …

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

2023 · arXiv (Cornell University)

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific …

Chat2Query: A Zero-Shot Automatic Exploratory Data Analysis System with Large Language Models

2024

Data analysts often encounter two primary challenges while conducting exploratory data analysis by SQL: (1) the need to skillfully craft SQL queries, and (2) the requirement to generate suitable visualizations that enhance the interpretation of …

Modeling and Simulation of Market Opportunities in Digital Economy Based on Artificial Bee Colony Algorithm

2024

This study aims to model and simulate the opportunities in the digital economy market using the artificial bee colony algorithm. Firstly, the specific application scheme of the artificial bee colony algorithm in the mining of …

Promoting Machine Abilities of Discovering and Utilizing Knowledge in a Unified Zero-Shot Learning Paradigm

2024 · ACM Transactions on Knowledge Discovery from Data

Knowledge discovery and utilization are two essential cognitive processes that enable humans to understand the world and extract new insights from their surroundings. These processes have motivated machine learning studies, particularly zero-shot (ZS) learning, which …

Towards More Relevant Product Search Ranking Via Large Language Models: An Empirical Study

2024 · arXiv (Cornell University)

Training Learning-to-Rank models for e-commerce product search ranking can be challenging due to the lack of a gold standard of ranking relevance. In this paper, we decompose ranking relevance into content-based and engagement-based aspects, and …

Stepwise Reasoning Disruption Attack of LLMs

2025

Jingyu Peng, Maolin Wang, Xiangyu Zhao, Kai Zhang, Wanyu Wang, Pengyue Jia, Qidong Liu, Ruocheng Guo, Qi Liu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

SimCDR: Preserving Intra-Domain Similarities of Users for Cross-Domain Recommendation

2025 · ACM Transactions on Information Systems

Cross-Domain Recommendation (CDR) can effectively alleviate the data sparsity issue in the recommendation system by transferring the source domain knowledge to the target domain. Many CDR methods try to find a mapping of latent embeddings …

Channel-masked Asymmetric Distribution Matching for Cross-Domain Generalized Dataset Distillation

2026 · Proceedings of the AAAI Conference on Artificial Intelligence

Dataset distillation has achieved remarkable progress as an effective approach for data compression. However, real-world data often comes from diverse domains, leading to potential mismatches between the domains of synthesized images and those of the …

Some advances of tracing techniques and methods for studying metallogeny at the State Key Laboratory of Ore Deposit Geochemistry over the past decade

2026 · Bulletin of Mineralogy Petrology and Geochemistry

成矿示踪技术方法是矿床学研究的基础，在深入揭示成矿物质来源、活化、迁移和富集成矿等方面发挥着至关重要的作用。随着关键矿产成为研究的重点，由于此类矿产通常具有“稀”、“细”、“伴”的特殊地质属性，已有方法和理论已难以满足研究的需要。为解决上述问题，矿床地球化学国家重点实验室近十年来开展了系列元素-同位素（包括年代学）高精度、高空间分辨率分析方法以及元素分配-同位素分馏实验模拟和理论计算研究，逐步形成了能够强力支撑关键矿产研究的技术方法体系。这些技术方法被广泛应用于钨、锡、锑、金、铜、钼、锂、稀土和稀散等关键金属研究，极大地促进了成矿理论创新和找矿突破。本文对实验室近十年来相关技术方法的发展与应用进行了梳理，以期为关键矿产成矿示踪和找矿勘查提供方法学参考。

Learning from History and Present

2018

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendationsare becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works …

Insertion-based Decoding with Automatically Inferred Generation Order

2019 · Transactions of the Association for Computational Linguistics

Conventional neural autoregressive decoding commonly assumes a fixed left-to-right generation order, which may be sub-optimal. In this work, we propose a novel decoding algorithm— InDIGO—which supports flexible sequence generation in arbitrary orders through insertion operations. …

Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction

2021

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature. However, despite being extensively studied, these sequential methods still suffer from three limitations. …

Contrastive Cross-domain Recommendation in Matching

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-world systems. However, CDR in the matching (i.e., …