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Title of the Talk: ICICLE: Intelligent Cyberinfrastructure with Computational Learning in the Environment
Abstract: As a national infrastructure that enables artificial intelligence at the flick of a switch, ICICLE (Intelligent Cyberinfrastructure with Computational Learning in the Environment) will transform today’s AI landscape from a narrow set of privileged disciplines to one where democratized AI empowers domains broadly through integrated plug-and-play AI. Converging under one virtual roof, ICICLE will foster interdisciplinary communities, advance foundational AI and CI, and transform application domains. Through its innovative approach to training and technology transfer, ICICLE will grow an AI-enabled workforce and incubate innovative companies with sustained diversity and inclusion at all levels. Ultimately, ICICLE will enable a transparent and trustworthy national infrastructure for an AI-enabled future to address pressing societal problems and enable decision-making for national priorities.
Title of the Talk: AIoT for Achieving Sustainable Development Goals
Abstract: Artificial Intelligence revived in the last decade. The need for progress, the growing processing capacity and the low cost of the Cloud have facilitated the development of new, powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep Learning and Convolutional Networks is transforming the way we work and is opening new horizons. Thanks to them, we can now analyse data and obtain unimaginable solutions to today’s problems. Nevertheless, our success is not entirely based on algorithms, it also comes from our ability to follow our “gut” when choosing the best combination of algorithms for an intelligent artefact. It's about approaching engineering with a lot of knowledge and tact. This involves the use of both connectionist and symbolic systems, and of having a full understanding of the algorithms used. Moreover, to address today’s problems we must work with both historical and real-time data. We must fully comprehend the problem, its time evolution, as well as the relevance and implications of each piece of data, etc. It is also important to consider development time, costs and the ability to create systems that will interact with their environment, will connect with the objects that surround them and will manage the data they obtain in a reliable manner.
In this keynote, the evolution of intelligent computer systems will be examined. The need for human capital will be emphasised, as well as the need to follow one’s “gut instinct” in problem-solving. We will look at the benefits of combining information and knowledge to solve complex problems and will examine how knowledge engineering facilitates the integration of different algorithms. Furthermore, we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems. It will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities.
The use of edge platforms or fog computing helps increase efficiency, reduce network latency, improve security and bring intelligence to the edge of the network; close to the sensors, users and to the medium used.
This keynote will present success stories regarding specially smart cities. All these fields require the development of interactive, reliable and secure systems which we are capable of building thanks to current advances. Deepint.net, a tool developed by DCSc and BISITE will be presented. Several use cases of intelligent systems will be presented and it will be analysed how the different processes have been optimized by means of tools that facilitate decision-making.
Title of the Talk: Toward Effective Network Traffic Analytics of Mobile Apps via Deep Learning
Abstract: In recent years operators have experienced the tremendous growth of the traffic to be managed in their networks, whose heterogeneous composition (e.g. mobile/IoT devices, anonymity tools), dynamicity and increasing encryption is posing new challenges toward actionable network traffic analytics. In this talk, the topics of network traffic classification and prediction generated by mobile applications will be covered, due to their beneficial use in network management, user-tailored experience and privacy. First, the reasoned use of Deep Learning umbrella will be introduced and explained in such context. Hence, lessons learned and common pitfalls will be highlighted. Subsequently, the adoption of sophisticated multi-modal multi-task architectures will be put forward. The objective is to devise AI-based traffic analysis tools able to capitalize the structured nature of traffic and able to support different objectives. The talk will also cover the current research done in the area of AI-based network traffic analysis at TRAFFIC group of University of Naples Federico II, Italy.
Title of the Talk: Deep Learning for Medical Image Analysis
Abstract: In recent years, artificial intelligence and deep learning have made major breakthroughs not
only in theory but also in practical applications. The use of Convolutional Neural Network (CNN)
for image processing has especially made great strides. In this presentation, I will illustrate some
of the possible applications of deep learning in medical image analysis and share some current
research on clinical image analysis with you. Two research projects are in collaboration with
medical experts at NCKU Hospital and carried out in MOST Biomedical AI center. The first
project is to develop a growth parameter assessment and midsagittal plane (MSP) detection for 3D
fetal ultrasound image. The second project is to design and implement an automatic evaluation
system of bone mineral density from a premature infant radiograph. With the developed CNN
systems, image preprocessing is now more simplified and the test results yield higher accuracy. It
greatly improves the shortcomings of traditional methods. It is expected that the deep learning
methods will have better applicability and play more important roles in medical image analysis.