Search is dead. Well, perhaps is not but I am provocative this morning. The first search engine was launched in 1993. Since then we made an incredible progress in terms of content discovery, indexing, scalability, ranking, machine learning, variety, and UX. However, the paradigm is still the same. You need to have an idea of what you are searching well before starting submitting queries based on keywords. Exactly like in 1993.

I think that Awareness is the new king. For awareness, I mean something that send to you the information you like to get by working on your behalf with no need of explicit searches. To be honest, the topic is not new. Patty Maes works on Intelligent Software Agents since 1987. However, I don't see a disruptive revolution. We still receive alerts via Google Alerts, which is based on explicit queries. Some initial steps - but not a revolution - are shown in Google Now where the location is the implicit query. Some other changes are in Google Glass, where the image captured might be the surrogate for query. Still is a world where 95% of our actions are described and learned via keywords.

What do you think?

## Sunday, August 31, 2014

### Search is dead, Awareness is the new king

## Saturday, August 30, 2014

### Internet of things: what is new?

We live in a world of catchy words. What was called Parallel Computation yesterday, became NoW (Network of Workstation), Grid and then Cloud. Same thing different words.

Another example is IoT, which is pretty much similar to Home Automation something discussed during the past 40 years.

So what is cool with IoT?

Frankly, I don't know. However, got an idea there and filed a patent. Let's see what will happen.

Another example is IoT, which is pretty much similar to Home Automation something discussed during the past 40 years.

So what is cool with IoT?

Frankly, I don't know. However, got an idea there and filed a patent. Let's see what will happen.

## Friday, August 29, 2014

### Learning something new: Apache Spark

Any suggestion about it?

## Friday, August 22, 2014

### Assignment matching problem

Assume
that we have workers and tasks to be completed.
For each pair (worker, task) we know the costs that should be paid
per worker to conclude the task. The goal is to conclude all the
tasks and to minimize the total cost, under the condition that each
worker can execute only one task and vice versa.

## Thursday, August 21, 2014

### Ford–Fulkerson’s Algorithm for Flow Network

## Wednesday, August 20, 2014

## Monday, August 18, 2014

### Find All_Pairs_Shortest_Path (Floyd-Warshall Algorithm)

## Sunday, August 17, 2014

### Find MST(Minimum Spanning Tree) using Prim’s Algorithm

## Saturday, August 16, 2014

## Friday, August 15, 2014

### Antonio Gulli Adaboost

Adaboost is one of my favorite Machine Learning algorithm. The idea is quite intriguing: You start from a set of weak classifiers and learn how to linearly combine them so that the error is reduced. The result is a strong classifier built by boosting the weak classifiers.

Mathematically, given a set of labels y_i = { +1, -1 } and a training dataset x_i, Adaboost minimizes the exponential loss function sum_i (exp - (y_i * f(x_i)) }. The function f = sum_t (alpha_t h_t) is a linear combination of the h_t classifier with weight alpha_t. For those loving Optimization Theory , Adaboost is a classical application of Gradient Descend.

The algorithm is quite simple and has been included in the top 10 data mining algorithms in 2007 and the GÃ¶del prize in 2003. After Adaboost, Boostingbecome quite popular in the data mining community with application in Ranking and Clustering.

Here you have the code for AdaBoosting in C++ and Boost.

Mathematically, given a set of labels y_i = { +1, -1 } and a training dataset x_i, Adaboost minimizes the exponential loss function sum_i (exp - (y_i * f(x_i)) }. The function f = sum_t (alpha_t h_t) is a linear combination of the h_t classifier with weight alpha_t. For those loving Optimization Theory , Adaboost is a classical application of Gradient Descend.

The algorithm is quite simple and has been included in the top 10 data mining algorithms in 2007 and the GÃ¶del prize in 2003. After Adaboost, Boostingbecome quite popular in the data mining community with application in Ranking and Clustering.

Here you have the code for AdaBoosting in C++ and Boost.

### Antonio Gulli KMeans

K-means is a classical clustering algorithm..

Here you have a C++ code for K-means clustering.

(Edit: 12/05/013)

See also my more recent posting A new lighter implementation of K-means

Here you have a C++ code for K-means clustering.

(Edit: 12/05/013)

See also my more recent posting A new lighter implementation of K-means

## Thursday, August 14, 2014

### Find the shortest path in a graph with non-negative edge weight

## Wednesday, August 13, 2014

### Find MST(Minimum Spanning Tree) using Kruskal’s Algorithm

## Tuesday, August 12, 2014

### Find the articulation points in a graph

## Monday, August 11, 2014

### Find the strongly connected components in a graph

## Sunday, August 10, 2014

### Find an Hamiltonian cycle

The problem is NP-Complete, so provide a backtracking exponential solution.

## Saturday, August 9, 2014

### Given a connected graph, compute the minimum spanning tree (MST)

use fibonacci heaps in boost library

## Friday, August 8, 2014

## Thursday, August 7, 2014

### Given a direct acyclic graph, implement a topological sort

## Wednesday, August 6, 2014

### Given two nodes in an undirected graph, find the path connecting them

## Tuesday, August 5, 2014

## Monday, August 4, 2014

### Implement a DFS visit

Try to be independent from underlying graph implementation

## Sunday, August 3, 2014

### Implement a BFS visit in C++

Try to be independent from underlying graph implementation

## Saturday, August 2, 2014

### Implement a graph in C++

Using adjacent matrix. If possible, use templates for storing node attributes and link attributes. Is this the best implementation?

## Friday, August 1, 2014

### Implement a Graph in C++

Direct graph with adjacent lists

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