Author
Contributions by role
Author 2
Xinlong Pan
Institute of Information Fusion, Naval Aviation University, Yantai 264001, China
Summary
Edited Journals
IECE Contributions

Open Access | Research Article | 26 April 2025
Using Psycholinguistic Clues to Index Deep Semantic Evidences: Personality Detection in Social Media Texts
Chinese Journal of Information Fusion | Volume 2, Issue 2: 112-126, 2025 | DOI: 10.62762/CJIF.2025.820998
Abstract
Detecting personalities in social media content is an important application of personality psychology. Most early studies apply a coherent piece of writing to personality detection, but today, the challenge is to identify dominant personality traits from a series of short, noisy social media posts. To this end, recent studies have attempted to individually encode the deep semantics of posts, often using attention-based methods, and then relate them, or directly assemble them into graph structures. However, due to the inherently disjointed and noisy nature of social media content, constructing meaningful connections remains challenging. While such methods rely on well-defined relationships be... More >

Graphical Abstract
Using Psycholinguistic Clues to Index Deep Semantic Evidences: Personality Detection in Social Media Texts

Open Access | Research Article | 17 March 2025
Quantitative Evaluation Method for Anomaly Levels of Complex Flight Maneuver Based on Multi-sensor Data
Chinese Journal of Information Fusion | Volume 2, Issue 1: 14-26, 2025 | DOI: 10.62762/CJIF.2024.344084
Abstract
The methods that identify complex flight maneuvers from multi-sensor flight parameter data and conduct automated quantitative evaluations of anomaly levels could play an important role in enhancing flight safety and pilot training. However, existing methods focus on anomaly detection at individual flight parameter data points, making it challenging to accurately quantify the overall abnormality of a flight maneuver. To address this issue, this paper proposes a novel method for the quantitative evaluation of anomaly levels in complex flight maneuvers by fusing multi-sensor data. The proposed method comprises two stages: complex flight maneuver recognition and anomaly level quantification. In... More >

Graphical Abstract
Quantitative Evaluation Method for Anomaly Levels of Complex Flight Maneuver Based on Multi-sensor Data