Blockchain Technology and Applications

This lecture overviews Blockchain Technology that has many applications in Cryptocurrencies, Identity Management and Biometrics. It covers the following topics in detail: Introduction to Cryptography, Blockchain and Blockchain Consensus Algorithms. Applications in Cryptocurrencies, Distributed Identity Management (uPost, Sovrin, ShoCard systems) and Biometrics.

Cryptocurrencies

This lecture overviews Cryptocurrencies that has many applications in Finances, Network Theory and Web Science. It covers the following topics in detail: Introduction to Cryptography, Blockchain and Blockchain Consensus Algorithms. Blockchain and Cryptocurrency: Bitcoin and Ethereum.

Blockchain Algorithms

This lecture overviews Blockchain technology that has many applications in cryptocurrencies, e-commerce and identity management.  It covers the following topics in detail: Introduction to Blockchain. Peer-to-Peer (P2P) networks and their use in trusted distributed decision making are detailed.  Blockchain structure and technology, namely hash pointers, time-stamping and mining are thoroughly explained. The Byzantine Fault or Byzantine Generals’ Problem and its various solutions are presented as well together with Byzantine Fault Tolerance and Practical BFT. Distributed System Consensus: Blockchain Consensus, Nakamoto Consensus, Proof of Work Consensus, Proof of Stake Consensus and the attack are detailed.

Web Search based on Ranking

This lecture overviews Web Search based on Ranking that has many applications in Web Science and Social Media Analytics. It covers the following topics in detail: Architecture of Web Search Engine: Crawler, Indexer, Query Processor. Timeline of Ranking at Indexed Pages. Ranking Algorithms: Based on Frequency (TF-IDF), Based on Graph-Link Analysis (PageRank, Hits, Salsa, UsersRank, SimRank). Neural Networks-based ranking algorithms: Pointwise (Ranking with Large Margin Principles-SVM), Pairwise (SortNet), Listwise (ListNet). Current Neural Networks for Ranking: GEPS, MatchPyramid, C-DSSM, NeuBase. Deep Learning Metrics: Angular Loss, Nearest Neighbors Gaussian Kernels.

Recommendation Systems

This lecture overviews Recommendation Systems that has many applications in Web Science, Marketing and Social Media Analytics. It covers the following topics in detail: Content Based Filtering. Collaborative Filtering: Memory Based Techniques, Model Based Techniques, Hybrid Techniques. ΚΝΝ algorithm. ALS algorithm. Learning from Implicit Datasets. Matrix Factorization: Funk MF, SVD++, Asymmetric SVD. Hybridization techniques. Deep Learning in Recommender Systems: MLP, Deep Factorization Machine, Restricted Boltzman Machines, Neural Autoregressive Density Estimators (NADE). Evaluation of Recommender Systems.  Netflix Challenge.

Information Diffusion

This lecture overviews Information Diffusion that has many applications in Network Theory, Web Science, Political Science, Marketing and Social Media Analytics. It covers the following topics in detail: Basics of Information Diffusion. Social Network Diffusion Models: Ising Model, Epidemic Diffusion Models, Cascade Models, Threshold Models, Game Theory Models, Nature-Inspired Model Influence Models, Influence Models.

Applications of Information Diffusion: Rumor-diffusion, Cross-Media Information Diffusion, Viral marketing. Emergency situation management, Collaborative filtering, Citation networks.