Algorithms on strings

3.5.1 Syntactic Matching Standard syntactic matching methods used to compare the string, numeric and character type attributes of the two identities. Given Twitter identity and a candidate identity returned from Profile Search, Content Search, Network Search and Self-Mention Search, we used distance metric to compare their username and name attributes. Closer the match, smaller is the value of distance metric. 3.5.2 Image Matching There have been instances where users put same profile image on

robot required collaboration from the engineering team and the design team. The engineering team comprised of five people with customization experience. All the team members had a specific task that they carried out. One individual worked on the algorithm while someone else worked on the servo and another person worked on the fabrication along with the fifth member who worked on the vision system. Since the robot was going to be on display the team worked with a display and design team for the museum

called edx, where many colleges have online courses available to the public. The course that I took was hosted by Harvard University. I signed up for it and completed the first five problem sets, which gave me a feel for programming and analyzing algorithms. The problem sets introduced me to C, where I built programs such as recovering deleted JPEG images. 2. My strongest coding language as of now is Java, as I have taken two college level courses that were based on it. In these two courses, I have

deques are cleared (though usually with a processor, the work is continuous and more efficient due to the algorithm). No worries though, we will just go through some simple examples from the Python interpreter. The datatype "deque" comes from the collections package. So we need to import it from there: >>> from collections import deque Much like the namedtuple type, we can give it a string and it will be extended into an array-like structure like so: >>> my_deque = deque('long') >>> print(type(my_deque))

queries. *they are paired with the user queries, which is referenced user query records maintained from user sessions. *We train and apply the monolingual Statistical machine Translation Model on these query-snippet pairs using a machine learning algorithm. *We get as output expansion

# std::vector The Standard Template Library(STL) provides several useful containers and algorithms built in to C++. These powerful tools help prevent the developer from reinventing the wheel. The vector is one such container provided by STL. The ```std::vector``` is a sequence container that supports random access of data like an array. However, vectors can dynamically resize themselves when more data needs to be inserted. Thus they allow very efficient data access and relatively efficient adding

Benchmarking LMDB and LevelDB for deep learning Weiyue Wang ABSTRACT Deep learning is a new emerging area of machine learning research, which has been shown to produce state-of-the-art results on various tasks. A high performance database management in deep learning framework will help increase learning efficiency. This work compares the performance of two key value data storage, Lightning Memory-Mapped Database (LMDB) and google LevelDB, for deep learning framework. Our key findings are followings

QoS Ranking Prediction Framework for Cloud Services J.Subathra Department of Information Technology B.S.Abdur Rahman University Chennai, Tamilnadu Email:Subathrait.jeeva24@gmail.com Ms. P. Latchoumy Department of information technology B.S.Abdur Rahman University Chennai, Tamilnadu Email:lak75dhana@gmail.com Abstract— The rising popularity of cloud computing makes building high quality cloud applications a critical and urgently required research problem. QoS rankings provide valuable

December 2016 Term Paper 3 Page Update Bioinformatics Algorithms: Dr. Kate Cooper A Comparative Analysis of Force Directed Layout Algorithms for Biological Networks Brief Description: I will conduct a comparative analysis of multiple force-directed algorithms used to identify clusters in biological networks. The analysis will consider topics such as the algorithm process, amount of preprocessing, complexity, and flexibility of the algorithms for different types and sizes of data. K-Means, SPICi

the XOR will return output as false.The X-Model X-OR is used in Parallel addition algorithm,Sine model X-OR is used in Listranking algorithm and finally Butterfly X-OR is used in Butterfly algorithm for the purpose of encryption. The inputs for the parallel addition algorithm are taken by using leftfingerprint acts as a plain text,right fingerprint acts as a key1 and palm acts as a key2.The parallel addition algorithm uses X-Model X-OR model which makes the inputs to do XOR operation in the format