PART 1: The application level. The point of view of the applications. Network service QoS. Design of scalable architectures for Web services. Internet data center arn account chitectures. Content delivery networks. Peer-to-peer networks and distributed hash tables.
PART 2: The relationship between transport and application layers. The socket library and its use.
PART 3: Congestion control and the transport layer. Transport techniques. TCP and congestion control. Focus: TCP exercises and examples.
PART 4: Routing metodologies and technologies. Routing algorithm for the network infrastructure. Link-State-Packet algorithms. Routing protocols and the Internet network. Software Defined Networks. Spanning tree computation for switched networks.
PART 5: Interdomain routing. Border Gateway Protocol. Scalability of BGP. Internet architecture. Internet data analysis. Design of a transit AS. Stability of BGP protocol.
PART 6: Virtual networks. Virtual local networks (VLAN). Evolution of the Spanning Tree Protocol. MPLS-based Virtual Private Networks.
PART 7: IPv6. NAT and IPv4 exhaustion. Basics of IPv6 protocol and address space. ICMPv6. Source address selection and multihoming. IPv4-IPv6 transition mechanisms.

Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~impianti/
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)

Brief introduction to Julia language. Introduction to parallel architectures, Parallel and distributed programming with Julia. Primitives of communication and synchronization. Languages based on directives. Performance metrics. Matrix operations and dense linear systems. Sparse linear systems. Cache-oblivious algorithms. Collaborative development of projects. Test driven development and debugging.

Lecture slides

Blaise N. Barney, HPC Training Materials, by kind permission of Lawrence Livermore National Laboratory's Computational Training Center

J. Dongarra, J. Kurzak, J. Demmel, M. Heroux, Linear Algebra Libraries for High- Performance Computing: Scientific Computing with Multicore and Accelerators, SuperComputing 2011 (SC11)

Database technology: physical structures, transaction management, distributed architectures. Data bases for decision support, data warehousing. Seminars on recent topics in database management.

P. Atzeni et al. Basi di dati 5/Ed. McGraw-Hill, 2017 (in alternative, any major database textbook, contact the instructor for advice)
Additional material available on the course site:
http://www.dia.uniroma3.it/~atzeni/didattica/BD/BDIIindex.html

1. Introduction:

- Intelligent Agents.
- AI as representation and search.

2. Solving problems by Searching:

-Blind search (Breadth-first search, Uniform cost search, Depth-first search, Iterative deepening search).
- Heuristic search (Best First, A*, IDA*, Heuristic Functions).
- Approximate Algoritms (Hill Climbing, Simulated Annealing, etc.)
- Two-Person games (MiniMax, Alfa-Beta Pruning).

3. Knowledge Representation and Automated reasoning:

- Frames, Semantic Networks, Production Systems.
- Case-Based Reasoning.
- Knowledge Based Systems.

4. Machine Learning:

- Symbol-Based (Inductive Learning, Decision trees).
- Connectionist (Artificial Neural Networks).

5. Communicating, Perceiving and Acting:

- Natural language Processing and Information retrieval.
- Computer Vision.

S.J.Russel, P.Norvig "Artificial Intelligence: A Modern Approach", 3/Ed (2010). Pearson Education.

Lecture notes by the professor.

First order logic, automatic deduction, the Prolog programming language, formal verification methods

See the course web page: http://cialdea.dia.uniroma3.it/teaching/logica/

M. Cialdea Mayer. Logica (Lecture notes)
Any introductory book on Prolog
D. A. Peled. Software Reliability Methods, chapters 1 and 4
M. Cialdea Mayer. Logica temporale e verifica di proprietà dei programmi (lecture notes).
J.-P. Katoen. Concepts, Algorithms and Tools for Model Checking
Gerd Behrmann, Alexandre David, and Kim G. Larsen A Tutorial on Uppaal 4.0.

Fundamentals: Software architecture concepts; Architectural descriptions; Software qualities; The architecture definition process.
Designing for software qualities: performance; modifiability; availability; veriafiability; scalability; interoperability.
POSA architectural patterns: Domain Model; Domain Object; Layers; Pipes & Filters; other POSA patterns (model-view-controller, shared repository, microkernel, reflection).
Architecture of distributed systems; client/server, peer-to-peer, remote invocation; broker; asynchronous communication; messaging; components; services; REST services; microservices; software architecture on the cloud.
Software delivery: environments and their management; cloud computing; system virtualization; containers; container orchestration.
Middleware.

Lecture notes (http://cabibbo.dia.uniroma3.it/asw/).

The course will examine various methods for the design, implementation, and testing of adaptive systems on the Web, realized through Artificial Intelligence techniques. Particular attention will be paid to Information Retrieval systems, such as search engines, and to new and emerging technologies suitable for realizing the next generation of intelligent and personalized search tools. We will study classical retrieval models, such as the vector space model and probabilistic models, document ranking techniques, as well as the PageRank algorithm adopted by Google. Algorithms of Machine Learning in Information Retrieval will be addressed, including techniques for Sentiment Analysis, User Modeling methods needed for developing personalized research tools and recommender systems, identifying and analyzing online communities, and social networks (such as Facebook and Twitter). Finally, statistical methods for the experimental evaluation of the aforementioned systems will be described.

Lectures will cover topics dealt with in scientific papers and reference texts.
The teacher will make available the slides from the lectures through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of further texts for passing the exam.

1. Regression
Review of Linear Regression
Assessment and Overfitting in the Regression
Feature Selection and Lasso

2. Classification
Review of Logistic Regression for classification
Overfitting in the Classification
Boosting. AdaBoost algorithm
Support Vector Machine (Large Margin Classification, Kernel I, Kernel II)
Naïve Bayes

3. Clustering and Retrieval
Algorithm K-NN
Algorithm K-Means
Expectation Maximization
Applications to Information Retrieval

4. Dimensionality Reduction
Data compression and visualization
Principal Component Analysis (PCA)
Choice of the number of principal components
Applications to Recommender Systems

5. Deep Learning
Deep Forward Networks
Regularization for Deep Learning
Convolutional Networks
Various applications

6. Case studies and projects
Several case studies will be exposed and projects will be proposed to apply the notions learned on various domains of interest.

Lecture notes by the professor.

- Infrastructures and programming paradigms for big data
- The Hadoop Ecosystem
- Cloud computing
- Big data processing (MapReduce, Hive, Spark)
- NoSQL systems
- Big data analytics
- Systems and applications
- Business seminars

Martin J. Fowler, PramodkumarJ. Sadalage. "NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence".
Teacher slides (available on the Web side of the course)

Data and Visualization: Data overloading. Comparison of Scientific Visualization and Information Visualization. Structured and Unstructured data. Data transformation. Data tables.

Visual Perception: Our vision’s principles and limitations. Peripheral and central view. The perception of color.

Cognitive Issues and User Tasks: Perception abilities. Weber's law. Stevens' power law. Gestalt laws. A two stage model for visual perception. Task taxonomies.

Infovis on the Web - SVG and D3.js: Basic ingredients of Web data visualization. JavaScript crash course. Raster and vector graphics. Overview of JavaScript libraries. Focus on D3.js.

Multivariate Data Representations: Combined views. Icons or glyphs. Alternative coordinate systems.

Visualization in Computer Networks: Visual analysis in the computer network domain. Motivations. Taxonomies. Real-world examples and use cases. Open questions.

Design Methods and Evaluation: Design methodologies and design choices. Design evaluation (goals, difficulties, practices, guidelines).

Visualization of Time Series Data: Definition of time series and temporal data. Visualization of time series (single dependent variable, multiple dependent variables). Case studies.

Interaction: Classification of interaction mechanisms, goals, and timings. Examples of interaction strategies.

Introduction to Graph Drawing: Graph Drawing conventions and aesthetics. The divide an conquer approach for testing planarity of a graph.

Node-link Representations of Trees: Representing trees within the node-link paradigm. Layered drawings of trees. Hv-drawings of trees. Limitations of node-link representations.

Space-Filling Visualizations of Trees: Algorithms and systems for the representation of trees using the space-filling strategy. Treemaps. 3D Space-filling approaches.

Representations of Graphs and Networks with the Force-Directed Approach: The force-directed paradigm. The barycenter method. Spring embedders. Scalability and flexibility of the force-directed paradigm. Fruchterman-Reingold and Barnes–Hut algorithms. Simulating graph theoretic distances. Magnetic fields. Generic energy functions. Handling drawing constraints.

Representations of Hierarchical Data: Algorithms for the representation of layered networks. The Sugiyama approach. Step 1: Cycle removal. Step 2: Level Assignment. Step 3: Crossing Reduction. Step 4: X-Coordinate Assignment

Orthogonal Drawings: Computing orthogonal drawings via Network Flows. The Topology-Shape-Metric approach. Extension to graphs of arbitrary degree. Representations of orthogonal drawings obtained from visibility representations and by incremental approaches.

Visualizing Large Graphs: Strategies for the visualization of massive amount of data providing both overview and details. Alternate between views. Combine different views. Filtering and clustering principles. Three-dimensional and two-dimensional representations of clustered graphs. Hybrid representations.

Tools and Libraries for Drawing Graphs: Tools and Libraries for drawing graphs. Programming languages, input and output formats, and interaction. Some practical example.

Architectures for Scalable Information Visualization: Computational and memory scalabity. Visualization architectures. Strategies for visualizing massive amounts of data.

Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~infovis/
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)

WEB DATA EXTRACTION. INFORMATION EXTRACTION. WEB DATA FUSION. KNOWLEDGE GRAPHS.

- Slides
- Selected research papers

TEXTBOOKS:
- MANNING, RAGHAVAN, SHUTZE: "INTRODUCTION TO INFORMATION RETRIEVAL" - CAMBRIDGE UNIVERSITY PRESS
- JURAFSKY, MARTIN: "SPEECH AND LANGUAGE PROCESSING: AN INTRODUCTION TO NATURAL LANGUAGE PROCESSING, COMPUTATIONAL LINGUISTICS, AND SPEECH RECOGNITION" SECOND EDITION
- Ronen Feldman: Text Mining Handbook - Advanced Approaches in Analyzing Unstructured Data

The program depends upon foreign professors.

The books depend upon foreign professors.

- Course introduction
- Introduction to cybersecurity and terminology
- Software vulnerabilities. Trusted and untrusted input, input validation. Vulnerabilities of applications written in interpreted languages.
Code injection. Injection into web pages: XSS. Cross site request forgery. OWASP. An example of a site vulnerable to sql injection.
- Buffer overflow attacks. Exploitation: privilege escalation, intrusions by open ports, intrusions by untrusted documents (email, web, etc.).
An example of code with buffer overflow vulnerability and its exploit.
- Network vulnerability: sniffing, mac flood, ARP poisoning, DNS vulnerability, Kaminsky attack. TCP session hijacking, MitM attacks, DoS and distributed DoS. Route hijacking.
- Cybersecurity planning: content of a security plan, risk analysis.
- Design Principles of policies and mechanisms.
- Models: AAA, confinement, DAC, MAC, access control matrix.
- Considerations about automatic anomaly detection systems
- System security: general principles (passwords vulnerabilities, hardening methodology, assessment, auditing). Unix, discretionary access control, filesystem security, authentication, PAM, syslog)
- Network security: Level 1 and 2 security, stateless and stateful firewalls, linux netfilter with configuration examples, proxies and their vulnerabilities. Load balancing and full high-availability. Network intrusion detection systems.
- Cryptographic techniques: cryptographic basics (hash, symmetric, asymmetric, MAC, digital signature), birthday attacks, rainbow, key quality, pseudo-random number generators.
- Authentication and key exchange protocols. Replay and reflection attacks. Nonces. Perfect Forward Secrecy. Diffie-Helman.
- Certificates, certification authority, public key infrastructures and their vulnerabilities. Applications: protocols ssl, tls, ssh, virtual private networks, ipsec, etc. Authentication protocols point-to-point and in local area network. Radius and its vulnerabilities. Other applications.
- Authenticated Data Structures
- Distributed Ledger Technologies and Bitcoin
- Smart contracts
- Cybersecurity in large organisations.