Expansion of Information in the Binary Autoencoder with Random Binary Weights

This letter studies the expansion and preservation of information in a binary autoencoder where the hidden layer is larger than the input. Such expansion is widespread in biological neural networks, as in the olfactory system of a fruit fly or the projection of thalamic inputs to the neocortex. We analyze the threshold model, the kWTA model, and the binary matching pursuit model to find how the sparsity and the dimension of the encoding influence the input reconstruction, similarity preservation, and mutual information across layers. It is shown that the sparser activation of the hidden layer is preferable for preserving information between the input and the output layers. All three models show optimal similarity preservation at dense, not sparse, hidden layer activation. Furthermore, with a large enough hidden layer, it is possible to get zero reconstruction error for any input just by varying the thresholds of neurons. However, we show that the preference for sparsity is due to the noise in the weight matrix between layers. A fixed number of nonzero connections to every neuron achieves better information preservation and input reconstruction for the dense hidden layer activation. The theoretical results give useful insight into models of neural computation based on sparse binary representation and association memory.

An Object Proxy-Based Dynamic Layer Replacement to Protect IoMT Applications

The Internet of medical things (IoMT) has become a promising paradigm, where the invaluable additional data can be collected by the ordinary medical devices when connecting to the Internet. The deep understanding of symptoms and trends can be provided to patients to manage their lives and treatments. However, due to the diversity of medical devices in IoMT, the codes of healthcare applications may be manipulated and tangled by malicious devices. In addition, the linguistic structures for layer activation in languages cause controls of layer activation to be part of program’s business logic, which hinders the dynamic replacement of layers. Therefore, to solve the above critical problems in IoMT, in this paper, a new approach is firstly proposed to support the dynamic replacement of layer in IoMT applications by incorporating object proxy into virtual machine (VM). Secondly, the heap and address are used to model the object and object evolution to guarantee the feasibility of the approach. After that, we analyze the influences of field access and method invocation and evaluate the risk and safety of the application when these constraints are satisfied. Finally, we conduct the evaluations by extending Java VM to validate the effectiveness of the proposal.