Description of Input Patterns by Linear Mixtures of SOM Models

This paper introduces a novel way of analyzing input patterns presented to the Self-Organizing Map (SOM). Instead of identifying only the "winner," i.e., the model that matches best with the input, we determine the linear mixture of the models (reference vectors) of the SOM that approximates to the input vector best. It will be shown that if only nonnegative weights are allowed in this linear mixture, the expansion of the input pattern in terms of the models is very meaningful, contains only few terms, and provides a better insight into the input state than what the mere "winner" can give. If then the models fall into classes that are known a priori, the sums of the weights over each class can be interpreted as expressing the affiliation of the input with the due classes

Contextually self-organized maps of Chinese words

This is a technical report on the implementation of contextual SOMs of Chinese words. Several new developments are introduced. It seems that when the words are ordered on the SOM topologically, the order is not only determined by the word classes, but also the roles of the words as sentence constituents are reflected in their position on the SOM

Description of Input Patterns by Linear Mixtures of SOM Models

This paper introduces a novel way of analyzing input patterns presented to the Self-Organizing Map (SOM). Instead of identifying only the "winner," i.e., the model that matches best with the input, we determine the linear mixture of the models (reference vectors) of the SOM that approximates to the input vector best. It will be shown that if only nonnegative weights are allowed in this linear mixture, the expansion of the input pattern in terms of the models is very meaningful, contains only few terms, and provides a better insight into the input state than what the mere "winner" can give. If then the models fall into classes that are known a priori, the sums of the weights over each class can be interpreted as expressing the affiliation of the input with the due classes

Contextually self-organized maps of Chinese words : Part 2

In this second publication on the contextual SOMs of Chinese words, a new effect is reported. The SOM was trained by the complete MCRC corpus used in the previous publication. When its hit diagrams were formed using subsets of words of a certain word class with different word frequencies, the hit distribution was found to be a function of this frequency. An explanation of this effect might be that the usage of the words changes with time, and frequent use accelerates this transformation. Therefore, in planning new experiments on the contextually self-organizing word maps, one should be aware of this effect and take it into account in the selection of words to represent the word classes

Machine Learning Aided Static Malware Analysis: A Survey and Tutorial

Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in technical Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware analysis that has been thoroughly studied before. In this paper, we address this research gap by conducting an in-depth survey of different machine learning methods for classification of static characteristics of 32-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and evaluate accuracy and practical generalization of these techniques. Finally, the results of experimental study of all the method using common data was given to demonstrate the accuracy and complexity. This paper may serve as a stepping stone for future researchers in cross-disciplinary field of machine learning aided malware forensics.Comment: 37 Page

Self-organization and associative memory

Two significant things have happened since the writing of the first edition in 1983. One of them is recent arousal of strong interest in general aspects of "neural computing", or "neural networks", as the previous neural models are nowadays called. The incentive, of course, has been to develop new com­ puters. Especially it may have been felt that the so-called fifth-generation computers, based on conventional logic programming, do not yet contain in­ formation processing principles of the same type as those encountered in the brain. All new ideas for the "neural computers" are, of course, welcome. On the other hand, it is not very easy to see what kind of restrictions there exist to their implementation. In order to approach this problem systematically, cer­ tain lines of thought, disciplines, and criteria should be followed. It is the pur­ pose of the added Chapter 9 to reflect upon such problems from a general point of view. Another important thing is a boom of new hardware technologies for dis­ tributed associative memories, especially high-density semiconductor circuits, and optical materials and components. The era is very close when the parallel processors can be made all-optical. Several working associative memory archi­ tectures, based solely on optical technologies, have been constructed in recent years. For this reason it was felt necessary to include a separate chapter (Chap. 10) which deals with the optical associative memories. Part of its con­ tents is taken over from the first edition

Digital circuits and devices

Bibliography: p. 441-455