Jia Jia

Beijing University of Posts and Telecommunications

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Lianhong Cai

Sun Yat-sen University

Zhiyong Wu

University of Hong Kong

Helen Meng

Chinese University of Hong Kong

Xixin Wu

Chinese University of Hong Kong

Suping Zhou

Tsinghua University

Haozhe Wu

Center for Information Technology

Runnan Li

Soochow University

Yishuang Ning

Kingdee (China)

Fanbo Meng

Nanjing Drum Tower Hospital

Pan Zhou

Wuhan University of Science and Technology

Cooperative Institutions

Tsinghua University

112

Chinese Academy of Sciences

University of Hong Kong

Beijing University of Posts and Telecommunications

University of Chinese Academy of Sciences

Zhejiang University

University Town of Shenzhen

Chinese University of Hong Kong

National University of Defense Technology

Peking University

Author Statistics

Papers

Citation

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Knowledge Representation Framework Combining Case-Based Reasoning with Knowledge Graphs for Product Design

Computer-Aided Design and Applications (2019)

Yingzhong Zhang Xu Liu Jia Jia Xiaofang Luo

Knowledge graph

Representation

10.14733/cadaps.2020.763-782

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Citations (16)

MEC 2017: Multimodal Emotion Recognition Challenge

Ya Li Jianhua Tao Björn W. Schuller Shiguang Shan Dongmei Jiang

This paper introduces baselines for the Multimodal Emotion Recognition Challenge (MEC) 2017, which is a part of the first Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) 2018. The aim of MEC 2017 is to improve the performance of emotion recognition in real-world conditions. The Chinese Natural Audio-Visual Emotion Database (CHEAVD) 2.0 is utilized as the challenge database, which is an extension of CHEAVD as released in MEC 2016. MEC 2017 has three sub-challenges and 31 teams participate in either all or part of them. 27 teams, 16 teams and 17 teams participate in audio (only), video (only) and multimodal emotion recognition sub-challenges, respectively. Baseline scores of the audio (only) and the video (only) sub-challenges are generated from Support Vector Machines (SVM) where audio features and video features are considered separately. In the multimodal sub-challenge, feature-level fusion and decision-level fusion are both utilized. The baselines of the audio (only), the video (only) and the multimodal sub-challenges are 39.2%, 21.7% and 35.7% in macro average precision.

Affective Computing

Feature (linguistics)

Multimodal learning

Audio visual

10.1109/aciiasia.2018.8470342

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Citations (43)

SenseRun: Real-Time Running Routes Recommendation towards Providing Pleasant Running Experiences

Proceedings of the AAAI Conference on Artificial Intelligence (2017)

Jiayu Long Jia Jia Han Xu

In this demo, we develop a mobile running application, SenseRun, to involve landscape experiences for routes recommendation. We firstly define landscape experiences, perceived enjoyment from landscape as motivators for running, by public natural area and traffic density. Based on landscape experiences, we categorize locations into 3 types (natural, leisure, traffic space) and set them with different basic weight. Real-time context factors (weather, season and hour of the day) are involved to adjust the weight. We propose a multi-attributes method to recommend routes with weight based on MVT (The Marginal Value Theorem) k-shortest-paths algorithm. We also use a landscape-awareness sounds algorithm as supplementary of landscape experiences. Experimental results improve that SenseRun can enhance running experiences and is helpful to promote regular physical activities.

10.1609/aaai.v31i1.10535

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Citations (4)

PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

Proceedings of the AAAI Conference on Artificial Intelligence (2020)

Tiancheng Shen Jia Jia Yan Li Yihui Ma Yaohua Bu

With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotion-oriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.

Affect

Feature (linguistics)

10.1609/aaai.v34i01.5352

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Citations (46)

Using tilt for automatic emphasis detection with Bayesian networks

Interspeech 2022 (2015)

Yishuang Ning Zhiyong Wu Xiaoyan Lou Helen Meng Jia Jia

Emphasis (telecommunications)

10.21437/interspeech.2015-207

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Citations (7)

Generating emphasis from neutral speech using hierarchical perturbation model by decision tree and support vector machine

International Conference on Audio, Language and Image Processing (2012)

Fanbo Meng Zhiyong Wu Helen Meng Jia Jia Lianhong Cai

In a computer-aided pronunciation training (CAPT) system, corrective feedback is desired to provide contrastive comparisons between user's and canonical pronunciations. This paper presents a hierarchical perturbation model to generate emphasis for English by modifying acoustic features of neutral speech to highlight such important speech segments. Synthesis of emphasis needs to be realized hierarchically at word, syllable and phone layers. A two-pass decision tree is constructed to cluster acoustic variations between emphatic and neutral speeches. The questions for decision tree construction are designed according to the above layers. The questions related to word and syllable layers are used to construct the main tree and then the questions related to phone layer are used to expand the leaves of main tree (deriving a set of subtrees). Support vector machines (SVMs) are used to predict acoustic variations for all the leaves of main tree (at word and syllable layers) and sub-trees (at phone layer). Experiments indicate that the proposed hierarchical perturbation model can generate emphatic speech with high quality for both naturalness and emphasis.

Emphasis (telecommunications)

Tree (set theory)

Pronunciation

10.1109/icalip.2012.6376658

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Citations (1)

Efficient Dehazing with Recursive Gated Convolution in U-Net: A Novel Approach for Image Dehazing

Journal of Imaging (2023)

Zhibo Wang Jia Jia P.C. Lyu Jeong-Ik Min

Image dehazing, a fundamental problem in computer vision, involves the recovery of clear visual cues from images marred by haze. Over recent years, deploying deep learning paradigms has spurred significant strides in image dehazing tasks. However, many dehazing networks aim to enhance performance by adopting intricate network architectures, complicating training, inference, and deployment procedures. This study proposes an end-to-end U-Net dehazing network model with recursive gated convolution and attention mechanisms to improve performance while maintaining a lean network structure. In our approach, we leverage an improved recursive gated convolution mechanism to substitute the original U-Net's convolution blocks with residual blocks and apply the SK fusion module to revamp the skip connection method. We designate this novel U-Net variant as the Dehaze Recursive Gated U-Net (DRGNet). Comprehensive testing across public datasets demonstrates the DRGNet's superior performance in dehazing quality, detail retrieval, and objective evaluation metrics. Ablation studies further confirm the effectiveness of the key design elements.

Convolution (computer science)

Net (polyhedron)

10.3390/jimaging9090183

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Citations (4)

Fake Finger Detection Based on Time-Series Fingerprint Image Analysis

Springer eBooks (2007)

Jia Jia Lianhong Cai

10.1007/978-3-540-74171-8_116

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Citations (30)

An approach to capturing and reusing tacit design knowledge using relational learning for knowledge graphs

Advanced Engineering Informatics (2022)

Jia Jia Yingzhong Zhang Mohamed Saad

Design knowledge

Tacit Knowledge

10.1016/j.aei.2021.101505

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Citations (34)

Evaluation of Rough Sets Theory on Effect Factors in Highway Traffic Accidents

CICTP 2021 (2012)

Jia Jia

When traffic accidents are analyzed and recognized through the use of the multiple factors analysis method, the influence of uncertainty can be sorted and recognized. This method can better reveal the further reason of traffic accidents other than single statistical analysis methods. Rough sets can effectively analyze inaccuracies, inconsistent and incomplete data and discover underlying rules based on the data. This method has simple procedure of calculation and can find order of traffic accident causes, which are helpful in developing traffic safety policies. This paper introduces how to use rough sets for analyzing traffic accidents. Then the presented method is utilized to analyze the serious national traffic accidents data and western mountains traffic accidents data. The analysis results in drawing order of traffic accident reasons.

Traffic accident

Statistical Analysis

Road traffic accident

Traffic conflict

10.1061/9780784412442.214

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Citations (2)