Online Bayesian learning with natural sequential prior distribution

A Neural Probabilistic Graphical Model for Learning and Decision Making in Evolving Structured Environments

A difficult and open problem in artificial intelligence is the development of agents that can operate in complex environments which change over time. The present communication introduces the formal notions, the architecture, and the training algorithm of a machine capable of learning and decision-making in evolving structured environments. These environments are defined as sets of evolving relations among evolving entities. The proposed machine relies on a probabilistic graphical model whose time-dependent latent variables undergo a Markov assumption. The likelihood of such variables given the structured environment is estimated via a probabilistic variant of the recursive neural network.

Adaptive Human-Machine Interactive Behavior Analysis With Wrist-Worn Devices for Password Inference

The pervasiveness of wearable devices furnished with state-of-the-art sensors has shown the powerful capability in context-aware applications. However, embedded sensors also become targets for adversaries to launch potential side-channel attacks. In this paper, we present a self-adaptive and pretraining-independent pattern attack that infers a graphical password by recovering the victim's hand movement trajectory via motion sensors of a wrist-worn smart device. With the adaptive pattern inference algorithm, the discovered attack can be launched remotely without requiring previous training data from victims or the prior knowledge about the keyboard input settings. Toward the proposed attack, we create a method to detect the sliding behavior that draws a graphical password on the screen. We also propose an inference algorithm to generate password candidates from hand movement trajectories for different keypad input settings. We implement the discovered attack on a smartwatch and conduct experiments to evaluate the impact of this attack. The evaluation results show that for complex graphical patterns, with a single try, the attack can infer the passwords at a success rate as high as 80%, and the success rate can be further boosted to over 90% within five attempts, which reveals the overlooked privacy information threat caused by sensor data leakage.

MLPNN Training via a Multiobjective Optimization of Training Error and Stochastic Sensitivity

The training of a multilayer perceptron neural network (MLPNN) concerns the selection of its architecture and the connection weights via the minimization of both the training error and a penalty term. Different penalty terms have been proposed to control the smoothness of the MLPNN for better generalization capability. However, controlling its smoothness using, for instance, the norm of weights or the Vapnik-Chervonenkis dimension cannot distinguish individual MLPNNs with the same number of free parameters or the same norm. In this paper, to enhance generalization capabilities, we propose a stochastic sensitivity measure (ST-SM) to realize a new penalty term for MLPNN training. The ST-SM determines the expectation of the squared output differences between the training samples and the unseen samples located within their Q -neighborhoods for a given MLPNN. It provides a direct measurement of the MLPNNs output fluctuations, i.e., smoothness. We adopt a two-phase Pareto-based multiobjective training algorithm for minimizing both the training error and the ST-SM as biobjective functions. Experiments on 20 UCI data sets show that the MLPNNs trained by the proposed algorithm yield better accuracies on testing data than several recent and classical MLPNN training methods.