Software

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Building Named Entity Recognizer (NER) using Conditional Random Fields (CRF)

Named entity recognizer is a program that recognizes named entity in text. The named entities could be anything from locations, company or person’s names, drug or disease names, etc.

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Predicting stock prices using recurrent neural networks (LSTM)

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SchumaNN: Recurrent neural networks composing music

With 2 friends of mine (Team was: Maksim Belousov, Mike Phuycharoen, Nikola Milosevic (myself)) on 12th and 13th November I participated at the GreatUniHack that was held in John Dalton building of Manchester Metropolitan University. We were implementing idea of machine learning-based music composer, for which we later came with name SchumaNN. For some time I wanted to experiment with this idea and this 24 hour hackathon came as a perfect match. We had couple of meetings prior to hackathon when we discussed some solutions and papers/blog posts we read. Some of the approaches we found are:

The decision we went at the end is to create a recurrent

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Impressions about Quantopian

Usually I don’t write about services I use, but one came into my way that I like – Quantopian. For a while I am interested in financial side of computer science, quantitative analysis, machine learning applications to predicting a stock market, etc. However, I was not really able to get grasp of these fields. I certainly could get some data from either Yahoo Finance and I even had access to Bloomberg terminal to play a bit with at the University library. I made some models, published a paper or ArXiv , made a tool that make these models available and published it on GitHub and I am talking with people at MUTIS Finance society to make a Quant

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FinAnalizer – Tool for financial analysis of stocks

For a while I was playing with financial data and financial data mining. I have already written about it in a separate post. Now, I decided to try to make my research so far accessible a bit more, so people can use it. Basically, what I previously did is that I used a bunch of technical financial parameters, such as price, P/E ratio, Price to Sales, Price to Book and many other ratios to try to create a model that will be able to predict a stock price movement over the long term (1 year period). It seems that it is working for about 75% of times using machine learning, namely Random Forests algorithm. For more details you can

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Marvin – A tool for semantic annotation released

During the last week I have released a version of Marvin – a tool for semantic annotations, that is able to annotate text using various sources, such as UMLS (using MetaMap), DBPedia, using some SPARQL interface, WordNet and probably most importantly SKOS (Simple Knowledge Organization System ) format for representing lexicons, dictionaries and terminologies. Primarily, the tool is supposed to be helpful in data labeling and normalization of biomedical texts, however, with the help of SKOS, WordNet and DBPedia it can be helpful in any domain.

When I mentioned normalization and labeling, for some readers not familiar with text mining and some aspects of semantic web, I better briefly explain. Basically, usual natural language text

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Starting an open source effort to bring biomedical data and tools together

Motivation from the community

A week ago I posted an blog post about my idea of creating Open Source organization that would have an goal to bring biomedical data and tools together to a single place. The original post can be read found here: https://inspiratron.org/blog/2015/12/12/open-source-effort-to-bring-all-open-biomedical-data-together/.

Since I posted it, quite large number of people contacted me, either by email or by social media, telling me that that would like to help. This quite motivated me and I believe validated this idea, so I decided to kick start it.

Community

In order to facilitate and coordinate the efforts of the community towards our goal of integrating, indexing and make data and tools accessible from the single source, I have created an

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Introduction to reverse engineering

1. Introduction

Reverse engineering is the process of discovering the technological principles of a device, object, or system through analysis of its structure, function, and operation. This involves taking some device, system or software and breaking it apart, analyze it and conclude how it works. When reverse engineer conclude how system works he can take advantage of it, he can recreate it, document it or fix flaws. Reverse engineering is commonly used to document system that is poorly documented and designers are no longer available, to retrieve lost source code and fix problems, to use undocumented API for interoperability, to create competitive project similar as reversed project or to overcome protection. Here we will focus on software reverse engineering in

OWASP Seraphimdroid
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New version of OWASP Seraphimdroid (v2.0) is published

Dear users and security aware people, we have a great announcement. The new version of OWASP Seraphimdroid is published with some very interesting breakthrough features. If you liked OWASP Seraphimdroid before, now you will probably love it. We have improved machine learning aided permission scanner, new settings scanner, improved SMS interceptor, improved application locker, and some more. OWASP organized OWASP Code Summer Sprint, where OWASP Seraphimdroid participated as one of the project. Student that was proposed some and was selected to develop improvements on OWASP Seraphimdroid was Kartik Kohli. I had opportunity to mentor him as OWASP Seraphimdroid project leader. So let’s start explaining the major improvements.

If you are not aware or do not know what features

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USSD Code List for Android Smartphone

Unstructured Supplementary Service Data (USSD) is a protocol used by GSM cellular telephones to communicate with the service provider’s computers. USSD can be used for WAP browsing, prepaid callback service, mobile-money services, location-based content services, menu-based information services, and as part of configuring the phone on the network.

USSD messages are up to 182 alphanumeric characters in length. Unlike Short Message Service (SMS) messages, USSD messages create a real-time connection during a USSD session. The connection remains open, allowing a two-way exchange of a sequence of data. This makes USSD more responsive than services that use SMS.

Some of these codes may lead you to a menu use the option key (far left soft key) to navigate. Some of the