COMP4337/9337: Securing Fixed and Wireless Networks Operational Security: Firewalls and IDS Lecturer: • Firewall Copyright By PowCoder代写 加微信 powcoder – Stateless – Stateful – Application level gateways • IDS – Host Based – Network based • Snort Secure System The only system which is truly secure is the one which is switched off and unplugged, […]
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COMPSCI 753 Algorithms for Massive Data Semester 2, 2020 Tutorial – Recommender System Kaiqi Zhao 1 Collaborative filtering Given the following user-item interaction matrix in a recommender system. Rows denote users and columns denote items. p1 p2 p3 p4 p5 p6 u1 ? 5 ? 4 ? 3 u2 ? 2 4 2 4 ?
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Recommender Systems COMPSCI 753 Kaiqi Zhao Recommendation algorithms § Content-based § Recommend based on the attributes of items, e.g., functionality, topics, types, etc. § Collaborative Filtering (CF) § User-based: recommend items that similar users liked § Item-based: recommend similar items to the items the user liked 10 Content-based recommendation § Content-based methods look for the
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Mining Social Networks COMPSCI 753 Kaiqi Zhao Overview § Social network analysis § Social network introduction § Community detection § Influence maximization 14 How to define good communities? 1 2456 3 5 6 5 6 Actual § Edge betweenness 1 2 3 Cut § Best cuts § Modularity 2 3 4 1 1 -2 3
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COMPSCI 753 Algorithms for Massive Data Semester 2, 2020 Tutorial 2: Data stream algorithms Ninh Pham 1 Uniformly sampling Suppose we have a stream of tuples with the schema Score(university, courseID, studentID, score) Assume that universities are unique, but courseID is unique only within a university and likewise, studentID is only unique within a university.
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Algorithms for Large Graphs COMPSCI 753 Kaiqi Zhao Slides adapted from “Mining of Massive Datasets” http://mmds.org Problems with PageRank § Measures generic importance of a page § In different topics, the importance of a web page could be different! § The results are not personalized § All the users will get the same results §
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1 Spam farm COMPSCI 753 Algorithms for Massive Data Semester 2, 2020 Tutorial – Graph Kaiqi Zhao Assume that two spam farmers, each having p = 1 support page and 1 target page, agree to link their spam farms. Assume that each target page has page rank x + 1−β without any n spam farm.
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Approximate Near Neighbor Search: Locality-sensitive Hashing COMPCSI 753: Algorithms for Massive Data Ninh Pham University of Auckland Auckland, Aug 10, 2020 1 Outline Popular similarity/distance measure Definitions Applications Locality-sensitive hashing framework for approximate near neighbor search LSH definition LSH framework for near neighbor search 2 A common metaphor
Recommender Systems COMPSCI 753 Kaiqi Zhao Overview ¡ì What is recommender system (RS)? ¡ì Recommendation algorithms ¡ì Content-based methods ¡ì Collaborative filtering ¡ì Latent factor model ¡ì Advanced topics in recommendations ¡ì Context-aware recommendations ¡ì Session-based recommendations 1 What is recommender system? ¡ì Recommender systems are a way of suggesting like or similar items for
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Data Stream Processing COMPSCI 753: Algorithms for Massive Data Ninh Pham University of Auckland Slides are collected and edited from https://vasia.github.io/dspa20/index.html and https://homepages.cwi.nl/~boncz/bads/stream.shtml Auckland, Aug 17, 2020 1 Outline Introduction Definition, applications, challenges Data stream processing Data stream modelling Data stream processing 2 What is data stream? Large data