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Some of the project highlights of the Department are listed below:

Computational Intelligence Databases and Information Management Networking and Systems Pattern Recognition and Machine Learning

Computational Intelligence

How Serendipity Improves User Satisfaction with Recommendations? A Large-Scale User Evaluation
Staff Dr. CHEN, Li
  • To identify the causal relationships from novelty, unexpectedness, relevance, and timeliness to serendipity, and from serendipity to user satisfaction and purchase intention.
  • To reveal the moderating effects of user curiosity on the relationships from novelty to serendipity and from serendipity to satisfaction.
  • To compare four algorithms for recommending e-commerce products in terms of user perceptions.

Macro environment and non-company news on stock market performance: A comparative study of media sentiment, public engagement, and prediction models
Staff Dr. CHEN, Li
  • To analyze the news coverage by Hong Kong media in past 20 years, and illustrate the longitudinal patterns of news sentiment and related public engagement on interactive media outlets.
  • To explore and discover several useful non-company specific and macro environment indicators from the news patterns that are significantly correlated with market (Hang Seng Index and sub-indices) and specific stock price fluctuations.More
  • To compare predictability and reliability of the found non-company specific news indicators and that of existing company specific tools in financial practices.
  • To develop accurate and efficient computational methods for market indicators identification and media text analysis.

User Evaluation on Sentiment-based Recommendation Explanations
Staff Dr. CHEN, Li
  • To investigate how review information could be exploited to generate explanations for multiple recommendations especially in high-investment product domains (i.e., digital camera and laptop).
  • To identify the exact effect of sentiment-based explanations on improving users’ decision effectiveness and system perceptions.More
  • To investigate how users view information and compare products on the sentiment-based explanation interface.

Detecting the Tipping Points from Inflammation to Tumorigenesis Using A Multi-level Network Modeling Approach
Staff Dr. CHEUNG, William Kwok Wai
  • To detect tipping points by identifying “dynamical network biomarker” at molecular network level by analysing the ‘omics’ data.
  • To identify potential networks associated with tipping points by constructing multi-level regulatory networks from heterogeneous data sources and performing structural analysis on evolving networksMore
  • To conduct biological experiments for validating our findings of potential networks associated with the tipping points.

Joint Embedding of Sequential Data and Knowledge Graphs with application to Predictive Analytics in Healthcare
Staff Dr. CHEUNG, William Kwok Wai
  • To develop methodologies that can learn representations simultaneously from heterogeneous clinical events in the EHR data and the medical knowledge graphs for patient outcome prediction with enhanced interpretability.
  • To develop an interpretable framework to jointly infer the correspondence among different modalities in the heterogeneous EHR data and generate high-throughput phenotype candidates.

Spatio-Temporal Data Analytics for Disease Transmission Modeling: Diffusion Networks, Underlying Factors, and Partial Observations
Staff Prof. LIU, Jiming
  • To infer the structures of latent diffusion networks for active surveillance
  • To reveal the impact of underlying factors on geographic variations for epidemic prevention
  • To derive the epidemic risk map based on partial observations for disease control

Towards Epidemiological Intelligence – Machine Learning and Data-Driven Modeling for Disease Surveillance and Control
Staff Prof. LIU, Jiming
Objectives To tackle the challenges in infectious disease surveillance and control, via developing and deploying novel AI methods and data-driven models
  • To infer the underlying diffusion networks of infectious diseases
  • To assess the transmission risks of infectious diseases in different regions
  • To learn and deploy optimal strategies for intervention under limited anti-disease resources

Databases and Information Management

An Exploratory Energy Study of the Smart Grid and Smart City Data from Australia
Staff Dr. CHOI, Byron Koon Kau
  • To identify climate variables related to the power consumption patterns in Australia
  • To undertake an exploratory study for the effectiveness of energy tariff programs for different households
  • To explore options of energy demand management for sustainable energy transitions

Exploratory Search on Graph Databases through Subgraph Query Feedback
Staff Dr. CHOI, Byron Koon Kau
  • To formally analyze the feedback (WIFSSQ and WNSSQ) and propose novel querying algorithms
  • To efficiently integrate the feedback as a generic module into a GUI for graph platforms
  • To formulate the optimal opportunities for delivering feedback
  • To conduct comprehensive performance and usability evaluations

Privacy-preserving Linear Algebra Framework for Graph Query Algorithms for Massive Networks
Staff Dr. CHOI, Byron Koon Kau
  • To study a set of linear algebra operators such as set intersection/union, scalar product, matrix multiplication/addition, and propose the encoding and encryption for graph queries
  • To apply privacy-preserving optimizations for the specific algebra operations
  • To unify the operations and develop a publicly available tool (API)

Efficient Graph Search Algorithms for Public-Private Social Networks
Staff Dr. HUANG, Xin
  • To design public-private graph models, and collect real-life public-private graph datasets;
  • To investigate efficient algorithms of community search and keyword search on public-private networks;
  • To develop a prototype system to demonstrate the feasibility of public-private social network analysis;

Tracking the viral spread of incivility online: An interdisciplinary approach to studying profanity use in Chinese-language online platforms
Staff Dr. HUANG, Xin
  • To draw upon the advances in machine learning and natural language processing to tackle online incivility.
  • To collect a large corpus of profane speech from a variety of Chinese-language online platforms.More
  • To identify the mechanisms and processes behind the spread of nasty talk in social media at both individual and group levels, and have implications for the development of cost-effective long-term solutions to online uncivil behavior.

Development and Evaluation of the Effectiveness of an Online Cognitive Behavioral Intervention Program for Hong Kong People with Social Anxiety Disorder
Staff Prof. XU, Jianliang
  • To develop an online Cognitive Behavioral Therapy (CBT) program (including both an online platform and mobile application) for Hong Kong people with Social Anxiety Disorder
  • To evaluate the effectiveness of this online CBT program in reducing anxiety symptoms and psychological distress, and improving quality of life for Hong Kong people with Social Anxiety Disorder at post-treatmentMore
  • To evaluate the maintenance effects of this online CBT program at 3- and 6-month follow-up tests
  • To test the predictive effects of sociodemographic factors (e.g. age, education, marital and economic status) in the treatment effects

ImageProof: Enabling Authentication for Large-Scale Image Retrieval
Staff Prof. XU, Jianliang
  • To design novel frameworks and query authentication algorithms for the verification of large-scale image retrieval.
  • To propose novel ADSs and several optimization techniques for robust and efficient authenticated top-k image queries.
  • To evaluate the proposed techniques by combining theoretical analysis and empirical experiments.

Towards Searchable and Verifiable Blockchain
Staff Prof. XU, Jianliang
  • To design a framework for blockchain to alleviate the storage and computing costs of the user and support verifiable Boolean range queries to guarantee the results’ integrity.
  • To design an efficient index structure that supports range queries with integrity assurance in a hybrid-storage blockchain framework.

Verifiable Attribute-Based Search over Shared Cloud Data
Staff Prof. XU, Jianliang
  • To design novel security primitives for supporting verifiable attribute-based access control over shared cloud data.
  • To protect data content and access policy of outsourced databases in a zero-knowledge manner.
  • To propose query authentication algorithms and optimization techniques for various query types.More
  • To develop a demonstration system to show the robustness and efficiency of our proposed techniques.

Networking and Systems

Energy-efficient Training of Multiple Deep Learning Models on GPU Clusters
Staff Prof. CHU, Xiaowen
  • To build up an open data set of GPU performance and power with DVFS;
  • To develop performance and power models for the training jobs of deep learning models;
  • To design online algorithms for resource allocation and task scheduling problem on GPU clusters;
  • To implement an open-source job management and scheduling system, and to evaluate the performance of the proposed solutions using real-world experiments.

Indoor Location Analytics System for Exhibition and Convention Industries (ILAS)
Staff Prof. NG, Joseph Kee Yin
  • The proposed system brings new opportunities to different stakeholders in the Exhibition and Convention Industry.
  • The buyers and exhibitors will have their matchmaking costs reduced and thus their levels of satisfaction increased.More
  • The exhibition organizers, with the collected movement data stored at the backend analytics server, are provided with decision support for better booth arrangement of exhibitors as well as supportive information for promoting exhibitors at sub-optimal booth areas.

Pattern Recognition and Machine Learning

Learning Discriminative Manifolds from Multiple Data Modalities for Multimedia Content Understanding
Staff Dr. LIU, Yang
Objectives To discover the intrinsic structure of multimedia data with multiple modalities for multimedia content understanding
  • For multi-label multimedia data, learn the correlation between different labels while preserving the discriminative information.
  • For multi-modal multimedia data, learn the consistency between different modalities while preserving the discriminative information.

Interpretable Machine Learning Aided Understanding of Complex Brain Structural and Functional Interaction Underlying Spectra of Individual Differences in Cognitive Behaviour
Staff Prof. CHEUNG, Yiu Ming
  • To develop a core set of machine and deep learning models and tools for big brain data
  • To gain new understanding of how the brain employ specific systems for selected social cognition tasks
  • To further develop and apply methods to assess autistic trait

On Developing a Lip-password Based Face Recognition System
Staff Prof. CHEUNG, Yiu Ming
  • To integrate lip-password into the face recognition system as a single learning paradigm;
  • To select and design the underlying models and algorithms of building up the lip-password-based face recognition system.

A Hybrid Residual Network and Long Short-Term Memory Method for Peptic Ulcer Bleeding Mortality Prediction
Staff Prof. YUEN, Pong Chi
Objectives To design a hybrid residual network and long short-term memory method for accurate peptic ulcer bleeding mortality prediction.

Face anti-spoofing to combat mask attacks: A remote photoplethysmography approach
Staff Prof. YUEN, Pong Chi
  • To effectively detect the 3D mask attack with different mask materials and qualities, a new liveness cue is proposed by analyzing heartbeat signal through remote photoplethysmography (rPPG).
  • To precisely identify the heartbeat vestige from the observed noisy rPPG signals under practical lighting conditions, A rPPG correspondence feature for 3D mask face anti-spoofing is proposed.

Multi-adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection
Staff Prof. YUEN, Pong Chi
  • This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of the domain generalization.
  • Learning a generalized feature space via a novel multi-adversarial discriminative deep domain generalization framework.

Towards Practical Object Trackers: From Feature Combination to Modality Fusion
Staff Prof. YUEN, Pong Chi
  • Developing robust visual tracking and object re-detection framework based on information fusion techniques to handle large appearance variations and tracking loss
  • Developing feature combination models which can adaptively combine appropriate visual features
  • Developing modality fusion models which can exploit the complementarity of other non-visual modalities (data sources) for appearance modeling

Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
Staff Prof. YUEN, Pong Chi
Objectives Learning a discriminative feature extraction Deep Neural Network (DNN) with large-scale unlabeled images, such that the visually similar samples are close to each other in the learned embedding space

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Department of Computer Science, Hong Kong Baptist University
Hong Kong Baptist University