Author name cluster

Jing Qiu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Vector Database Watermarking

Zhiwen Ren
Wei Fan
Qiyi Yao
Jing Qiu
Weiming Zhang
Nenghai Yu

Vector databases support machine learning tasks using Approximate Nearest Neighbour (ANN) query functionality, making them highly valuable digital assets. However, they also face security threats like unauthorized replication. By embedding stealth information, watermarking technology can be used for ownership authentication. This paper introduces a watermarking scheme specifically designed for vector databases. The scheme consists of four steps: generating identifiers, grouping, cryptographic mapping, and modification. Since watermark embedding requires modification of certain vectors, it may negatively affect the ANN query results. Further investigation reveals that in the widely used Hierarchical Navigable Small World (HNSW) indexing structure for vector databases, heuristic edge selection and pruning strategies result in some vectors having fewer edges or even none at all. These vectors exhibit significantly lower query frequencies than others, which means that modifying these vectors incurs less impact on query results. Based on this observation, we propose the Transparent Vector Priority (TVP) watermarking scheme, which prioritizes embedding the watermark in these low-query-frequency “transparent” vectors to minimize the impact of watermark embedding on query results. Experimental results show that compared to the current most effective and relevant watermarking schemes, the TVP scheme can significantly reduce the number of missed and false queries by approximately 75\%.

PDF Details

NeurIPS Conference 2024 Conference Paper

From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection

Xinlei Wang
Maike Feng
Jing Qiu
Jinjin Gu
Junhua Zhao

This paper introduces a novel approach that leverages Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by reasoning across both text and time series data. With language as a medium, our method adaptively integrates social events into forecasting models, aligning news content with time series fluctuations to provide richer insights. Specifically, we utilize LLM-based agents to iteratively filter out irrelevant news and employ human-like reasoning to evaluate predictions. This enables the model to analyze complex events, such as unexpected incidents and shifts in social behavior, and continuously refine the selection logic of news and the robustness of the agent's output. By integrating selected news events with time series data, we fine-tune a pre-trained LLM to predict sequences of digits in time series. The results demonstrate significant improvements in forecasting accuracy, suggesting a potential paradigm shift in time series forecasting through the effective utilization of unstructured news data.

PDF Details DOI

EAAI Journal 2024 Journal Article

Real-time industrial carbon emission estimation with deep learning-based device recognition and incomplete smart meter data

Jinjie Liu
Guolong Liu
Huan Zhao
Junhua Zhao
Jing Qiu
Zhao Yang Dong

Real-time industrial carbon emission estimation aims to estimate emissions more accurately to promote carbon reduction and mitigate climate change. Compared with input-output-based (IOA) analysis methods, the process-based analysis (PA) methods provide more specific information to decision-makers based on extensive detailed data. However, the required data is hard to obtain and normally contains missing data. To address these challenges, this paper proposes a novel deep learning-based carbon emission estimation framework to track the emissions of industrial customers in smart grids with smart meter data. The proposed framework encompasses three pivotal stages: data imputation, device recognition, and emission estimation—collectively referred to as DI-DR-EE. Specifically, the Data Imputation Network (DINet) based on super-resolution perception (SRP) is first introduced to recover the missing smart meter data. Then the recovered data is used to recognize the device states through the Device Recognition Network (DRNet), which thrives upon subspace blueprint separable convolutions (BSConv-S) to elevate the accuracy of device recognition with low-frequency data, all the while optimizing computational efficiency. Finally, the direct emission estimation is conducted based on the device states, and the indirect emission is estimated based on the power consumption. Case studies with five factories connected to the IEEE 57-bus system have verified the effectiveness of the proposed framework. The model training process was executed using Python with PyTorch version 1. 8. 1, coupled with Cuda 11. 1 for accelerated computations. Results underscore that DINet and DRNet outperform established benchmarks, while DI-DR-EE remarkably maintains its capacity to attain estimations within a 10% margin of error, even when grappling with up to 90% missing meter data.

Details DOI