When and where do feed-forward neural networks learn localist representations?

According to parallel distributed processing (PDP) theory in psychology, neural networks (NN) learn distributed rather than interpretable localist representations. This view has been held so strongly that few researchers have analysed single units to determine if this assumption is correct. However, recent results from psychology, neuroscience and computer science have shown the occasional existence of local codes emerging in artificial and biological neural networks. In this paper, we undertake the first systematic survey of when local codes emerge in a feed-forward neural network, using generated input and output data with known qualities. We find that the number of local codes that emerge from a NN follows a well-defined distribution across the number of hidden layer neurons, with a peak determined by the size of input data, number of examples presented and the sparsity of input data. Using a 1-hot output code drastically decreases the number of local codes on the hidden layer. The number of emergent local codes increases with the percentage of dropout applied to the hidden layer, suggesting that the localist encoding may offer a resilience to noisy networks. This data suggests that localist coding can emerge from feed-forward PDP networks and suggests some of the conditions that may lead to interpretable localist representations in the cortex. The findings highlight how local codes should not be dismissed out of hand

Steganalysis in computer forenics

Steganography deals with secrecy and convert communication and today the techniques for countering this in the context of computer forensics has somewhat fallen behind. This paper will discuss on how steganography is used for information hiding and its implications on computer forensics. While this paper is not about recovering hidden information, tools that are used for both steganography and steganalysis is evaluated and identifies the shortcomings that the forensic analysts would face. In doing so this paper urges on what the stakeholders in the field of computer forensics needs to do to keep ahead of criminals who are using such techniques to their advantage and obscure their criminal activities

Steganalysis in computer forensics

Steganography deals with secrecy and convert communication and today the techniques for countering this in the context of computer forensics has somewhat fallen behind. This paper will discuss on how steganography is used for information hiding and its implications on computer forensics. While this paper is not about recovering hidden information, tools that are used for both steganography and steganalysis is evaluated and identifies the shortcomings that the forensic analysts would face. In doing so this paper urges on what the stakeholders in the field of computer forensics needs to do to keep ahead of criminals who are using such techniques to their advantage and obscure their criminal activities

A new method to detect event-related potentials based on Pearson\u2019s correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience. Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise. The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP\u2019s waveform, these waveforms being time- and phase-locked. In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson\u2019s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase\u2014in consonance with the stimuli\u2014in EEG signal correlation over all channels. This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs. These hidden components seem to be caused by variations (between each successive stimulus) of the ERP\u2019s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology. The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language

Prediksi Daya Serap Perusahaan Terhadap Alumni Teknik Informatika Ibi Darmajaya Berbasis Jaringan Syaraf Tiruan

Informatics And Business Institute (IBI) Darmajaya was one of the institutions in the Lampung area that was involved in computer education and economics. One of the available routes in IBI Darmajaya in the computer field was Computer Science (CS). CS IBI Darmajaya produced the numbering graduate's ±1000 students who were spread in the territory. Up to now the level of the success from the CS graduate in the matter fast or not this graduate was absorbed by the world of the work still could not be predicted. So as to be needed one sitem the prediction of the absorbency of the company against the alumnus CS was based on Neural Network. The neural network used the algorithm backpropagatioan and the function activation sigmoid binary that consist of tansig, logsig, and purelin. The network that was formed consisted of 25 of the number of layer cells of input, 52 of the number of layer cells were hidden first (hidden the layer), 26 of the number of layer cells hidden the two, and 5 of the number of layer cells of output. The training data as well as the testing were carried out with the spreading questioner against 93 alumni TI that was taken in a random manner, where questioner this consisted of 24 skills criteria that must be owned by the graduate CS that was received from the spreading of the poll of the 39 companies in the Lampung territory. This data will be put into the prediction system used Matlab v6.0.Th