Research
A Review of Clone Detection Techniques using Model Semantics
A model clone is a set of similar or identical fragments in a model of the system. Understanding and Identifying model clones are important aspects in software evolution. During the Evolution of the Software product Cloning is often a strategic means for the same. Clone detection techniques play an important role in software evolution research where attributes of the same code entity are observed over multiple versions. To successfully create any method or technique for model clones detection we will have to study all the models defined in UML including internal and External Structure of UML This paper reviews some of the techniques available for the Model Clone Prevention and Detection.
Object Serialization Formats and Techniques a Review
Serialization is a process of converting an object into a stream of data so that it can be easily transmittable over the network or can be continued in a persistent storage location. This storage location can be a physical file, database or Network Stream. This paper concludes some the work that is going on in the field of Object Serialization. This paper presents Object Serialization Techniques that can be useful for various purposes, including object serialization Minimization which can be used to decrease the size of Serialized data.
A Review of NTLM Rainbow Table Generation Techniques
Rainbow tables reduce the difficulty in brute force cracking a single password by creating a large pre-generated data set of hashes from nearly every possible password Rainbow Tables. This method, known as the Faster Time-Memory Trade-Off Technique, is based on research by Martin Hellman & Ronald Rivest done in the early 1980’s on the performance trade-offs between processing time and the memory needed for cryptanalysis. In this paper we review some of the most important works in rainbow table generation and using rainbow tables in window NT environment, i.e. against NTLM. We will discuss how NTLM is weak against rainbow table attacks.
Gesture Recognition: A Survey of Gesture Recognition Techniques using Neural Networks
Understanding human motions can be posed as a pattern recognition problem. In order to convey visual messages to a receiver, a human expresses motion patterns. Loosely called gestures, these patterns are variable but distinct and have an associated meaning. The Pattern recognition by any computer or machine can be implemented via various methods such as Hidden Harkov Models, Linear Programming and Neural Networks. Each method has its own advantages and disadvantages, which will be studied separately later on. This paper reviews why using ANNs in particular is better suited for analyzing human motions patterns.
