FDSE 2021 - Keynote Program
Link to Microsoft Teams will be available on Conference days
Webnesday, Nov 24, 2021 - 08:30 am - 11:30 am (GMT+7)
◦ 8:30 am - Opening Ceremony
◦ 9:00 am - Keynote Speech 1 by Prof. Tai M. Chung (Sungkyunkwan University, Korea)
Topic: Federated Learning - Issues in Medical Application
◦ 10:30 am - Keynote Speech 2 by Dr. Thanh Thi Nguyen (Deakin University, Autralia)
Topic: Applications of Artificial Intelligence in the Battle against COVID-19
Thursday, Nov 25, 2021 - 02:30 pm - 05:00 pm (GMT+7)
◦ 2:30 pm - Keynote Speech 3 by Prof. Dr. Artur Andrzejak (Ruprecht - Karls - University of Heidelberg, Germany)
Topic: Machine Learning on Source Code with Applications in Software Engineering
◦ 4:00 pm - Keynote Speech 4 by Prof. Johann Eder (University of Klagenfurt, Austria)
Topic: Time in Data Models
FDSE 2021 - Q & A Session
This session will be available during Conference days
LNCS 13706
Invited Keynote: Federated Learning: Issues in Medical Application
Joo Hun Yoo, Hyejun Jeong, Jaehyeok Lee and Tai-Myoung Chung
Live Talk at 9:00 am (GMT+7) on Webnesday, Nov 24, 2021
Invited Keynote: Time in Data Models
Johann Eder, Marco Franceschetti and Josef Lubas
Live Talk at 4:00 pm (GMT+7) on Thursday, Nov 25, 2021
#5. Potential Threat of Face Swapping to eKYC with Face Registration and Augmented Solution with DeepFake Detection
#6. Feature Learning and Data Generative Models for Facial Expression Recognition
#9. Detecting the Intrusion in the Software Defined Networks
#18. Attendance Monitoring using Adjustable Power UHF RFID and Web-based Real-time Automated Information System
#26. Clustering Analyses of Two-Dimensional Space-Filling Curves
#27. Multi-class Bagged Proximal Support Vector Machines for the ImageNet Challenging Problem
#41. Pesticide Label Detection Using Bounding Prediction-based Deep Convolutional Networks
#55. Motorbike Counting in Heavily Crowded Scenes
#87. Comprehensive Analysis of Privacy in Black-box and White-box Inference Attacks against Generative Adversarial Networks
#88. Selective Combination and Management of Distributed Machine Learning Models
#91. Efficient Brain Hemorrhage Detection on 3D CT Scans with Deep Neural Network
#92. A Consensus-based Load-Balancing Algorithm for Sharded Blockchains
#99. Application Based Cigarette Detection on Social Media Platforms Using Machine Learning Algorithms
#101. Spliced Image Forgery Detection based on the Combination of Image Pre-Processing and Inception V3
#105. Improving ADABoost Algorithm with Weighted SVM for Imbalanced Data Classification
#106. Neighboring Information Exploitation for Anomaly Detection in Intelligent IoT
#107. Integrating Deep Learning Architecture into Matrix Factorization for Student Performance Prediction
#109. Authorization Strategies and Classification of Access Control Models
#110. A Data Union Method Using Hierarchical Clustering and Set Unionability
#123. Face Recognition in the Wild for Secure Authentication with Open Set Approach
#124. Feature Representation of AutoEncoders for Unsupervised IoT Malware Detection
#144. Distributed Scalable Association Rule Mining over Covid-19 Data
#148. Threshold Benefit for Groups Influence in Online Social Networks
#150. Intelligent Urban Transportation System to Control Road Traffic with Air Pollution Orientation
CCIS 1500
#11. Personalized Student Performance Prediction Using Multivariate Long Short-Term Memory
#13. Improving ModSecurity WAF using a Structured-Language Classifier
#17. Building a Vietnamese Dataset for Natural Language Inference Models
#45. Air Pollution Forecasting Using Regression Models and LSTM Deep Learning Models for Vietnam
#51. Estimating the Traffic Density from Traffic Cameras
#58. On Using Cryptographic Technologies in Privacy Protection of Online Conferencing Systems
#63. While Blood Cell Segmentation and Classification Using Deep Learning Couple with Image Processing Technique
#65. Hospital Revenue Forecast using Multivariate and Univariate Long Short-Term Memories
#76. Proposing Recommender System Using Bag of Word and Multi-Label Support Vector Machine Classification
#78. Using Some Machine Methods to the Problem of Gold Price Prediction by Time Series
#83. Security Issues in Android Application Development and Plug-in for Android Studio to Support Secure Programming
#96. Modeling Transmission Rate of Covid-19 in Regional Countries to Forecast Newly Infected Cases in a Nation by the Deep Learning Method
#104. A Survey of Machine Learning Techniques for IoT Security
#108. Forecasting and Analyzing the Risk of Dropping Out of High School Students in Ca Mau Province
#111. IU-SmartCert: a Blockchain-based System for Academic Credentials with Selective Disclosure
#119. Entropy-based Discretization Approach on Metagenomic Data for Disease Prediction
#121. Human Mobility Prediction using k-Latest Check-ins
#122. An Approach for Project Management System based on Blockchain
#125. Features Selection in Microscopic Printing Analysis for Source Printer Identification with Machine Learning
#131. A Consortium Blockchain-Based Platform for Academic Certificate Verification
#134. Forecasting Covid-19 Infections in Ho Chi Minh City using Recurrent Neural Networks
To be updated ...#142. Privacy-preserving Attribute-based Access Control in Education Information Systems
#146. A Prediction-Based Cache Replacement Policy for Flash Storage
#153. Document Representation with Representative Sets and Document Similarity at Sentence Level Using Maximum Matching in Bipartite Graph
To be updated ...#154. Innovative Way of Detecting Atrial Fibrillation Based on HRV Features Using AI-Techniques
#158. A Hybrid Approach using Decision Tree and Multiple Linear Regression for Predicting Students’ Performance
#159. A Deep Learning-based Method for Image Tampering Detection
#167. One-Class Classification with Noise-based Data Augmentation for Industrial Anomaly Detection
To be updated ...SHORT PAPERS
#16. Use some Machine Learning Algorithms Combined with Deep Learning in Speech Recognition
#60. Relation Classification based on Vietnamese Covid-19 Information using BERT Model with Typed Entity Markers
#77. Using Artificial Intelligence and IoT for Constructing a Smart Trash Bin
#79. The System for Detecting Vietnamese Mispronunciation
#81. Pixel-wise Information in Fake Image Detection
#95. Speaker Diarization in Vietnamese Voice
#132. Evading Security Products for Credential Dumping through Exploiting Vulnerable Driver in Windows Operating Systems.
#162. Anomaly Detection by Learners' Behaviors in Fog-Based E-Assessment Systems
To be updated ...
Bio: Artur Andrzejak has received a PhD degree in computer science from ETH Zurich in 2000 and a habilitation degree from FU Berlin in 2009. He was a postdoctoral researcher at the HP Labs Palo Alto from 2001 to 2002 and a researcher at ZIB Berlin from 2003 to 2009. He was leading the CoreGRID Institute on System Architecture (2004 to 2006) and acted as a Deputy Head of Data Mining Department at I2R Singapore in 2010. Since 2010 he is a professor at Ruprecht-Karls-University of Heidelberg and leads there the Parallel and Distributed Systems group. To find out more about his research group, visit http://pvs.ifi.uni-heidelberg.de/
Country: Germany
Affiliation: Ruprecht-Karls-University of Heidelberg, Germany
DBLP: Link
Research interests: Artificial Intelligence for Programming, Tools for Data Analysis, and Reliability of Complex Software Systems.
Keynote Topic: Machine Learning on Source Code with Applications in Software Engineering
The emergence of "Big Code", i.e. availability of very large repositories of programs, e.g. on GitHub or GitLab enabled a new class of software engineering applications and tools based on machine learning models of code. Such applications include code recommendation, automated source code summarization, comment generation and updates, bug detection, program translation, clone detection, program induction, and more.
In this presentation we will first refresh some concepts from Deep Learning (in particular Transformer models), and then explain common building blocks for source code modelling: embeddings of source code as text, embeddings and representation of Abstract Syntax Trees (ASTs), closed vocabulary vs. open vocabulary models, and pre-training. We will then illustrate these concepts on some recent state-of-the-art approaches: code predictions via modifying the attention mechanism of the Transformer, and unsupervised translations of programs between Java, C++, and Python.
Country: Korea
Affiliation: Sungkyunkwan University, Korea
DBLP: Link
Keynote Topic: Research Issues for Federate Learning in Smart Medical Systems
Since the Federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in smart medical area. In fact, the idea of learning in AI system without collecting data from local systems is very attractive because data remain in local sites. However, federated learning techniques still have various open problems because of the characteristics of federated learning such as distribution, participating clients and vulnerable environments.
In this presentation, the current issues to make federated learning flawlessly useful in real world will be briefly overviewed. They are related to malicious client detection, asynchrony, data/system heterogeneity, and integrating learning models. Also, we introduce the framework, we currently develop, to experiment various techniques and protocols to find solutions for the issues. The framework will be open to public after development.
Bio: Johann Eder is full professor for Information and Communication Systems in the Department of Informatics-Systems of the Alpen-Adria Universität Klagenfurt, Austria. From 2005-2013 he served as Vice President of the Austrian Science Funds (FWF). He held positions at the Universities of Linz, Hamburg and Vienna and was visiting scholar at AT&T Shannon Labs, NJ, USA, the University of California Santa Barbara, CA, USA, and the New Jersey Institute of Technology, NJ, USA.
Johann Eder published more than 190 papers in peer reviewed international journals, conference proceedings, and edited books. He chaired resp. served in numerous program committees for international conferences and as editor and referee for international journals.
Country: Austria
Affiliation: University of Klagenfurt, Austria
DBLP: Link
Research interests: The research interests of Johann Eder are information systems engineering, business process management, and data management for medical research. A particular focus of his work is the evolution of information systems and the modelling and management of temporal information and temporal constraints. Another focus is the application of information technology for medical research in particular information systems for biobanking. He successfully directed numerous competitively funded research projects on workflow management systems, temporal data warehousing, process modelling with temporal constraints, application interoperability and evolution, information systems modelling, information systems for medical research, etc.
Keynote Topic: Checking Conflicts in Temporal Constraints of Data Models
Bio: Dr Thanh Thi Nguyen has been recognized as a leading researcher in Australia in the field of Artificial Intelligence by The Australian Newspaper in a report published in 2018. Dr Nguyen was a Visiting Scholar with the Computer Science Department at Stanford University, California, USA in 2015 and the Edge Computing Lab, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Massachusetts, USA in 2019. He received an Alfred Deakin Postdoctoral Research Fellowship in 2016, a European-Pacific Partnership for ICT Expert Exchange Program Award from European Commission in 2018, and an Australia–India Strategic Research Fund Early- and Mid-Career Fellowship awarded by the Australian Academy of Science in 2020. Dr Nguyen obtained a PhD in Mathematics and Statistics from Monash University, Australia and currently is a Senior Lecturer in the School of Information Technology, Deakin University, Australia. He has expertise in artificial intelligence, deep learning, deep reinforcement learning, soft computing, data science, big data, econometrics, time series, signal processing, image processing, multi-agent systems, and operational research. Dr Nguyen has domain knowledge in various areas, including robotics, autonomous vehicles, defence technologies, cyber security, IoT, neuroscience, bioinformatics, health informatics, geoinformatics, remote sensing, business statistics, and financial technologies.
Affiliation: Deakin University, Autralia
DBLP: Link
Keynote Topic: Applications of Artificial Intelligence in the Battle against COVID-19
Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This talk will present an overview of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. The talk will touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. I will highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI and machine learning methods and tools that can be used to solve those problems. It is envisaged that this talk will provide AI and machine learning researchers and the wider community an overview of the current status of AI and machine learning applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.