No abstract

As a bridge between the physical and cyber world, the Internet of Things (IoT) senses and collects a large amount of user data through different types of devices connected to it. As a general information filtering technology, the recommender systems can help to associate information with each other in the IoT and to recommend personalized services for users. However, in practical applications, the collected data is uncertain due to noise, sensor errors, transmission errors, etc., which in turn affects system performance. In order to solve the data uncertainty problem in the IoT-based recommender systems, we propose a new recommender framework with item dithering. In this framework, the list of recommendations generated by the recommender algorithm is stored in a newly opened storage space for the entire session of the interaction between the user and the system. When the user interacts with the system, the list is pushed to the user after being shaken. Based on the proposed framework, we designed IDither, an item-based dithering and recommendation algorithm to shake out irrelevant items through predetermined indicators, thereby retaining the items required by the user and recommending them to the user. Experiment evaluations on real datasets show that IDither is an effective solution for handling uncertainty in the IoT-based recommender systems. We also found that IDither can be viewed as a list updating tool to increase diversity and novelty. INDEX TERMS Recommender systems, Internet of Things, data uncertainty, dithering.

In this paper, we investigate the basic features of a simple susceptible-infected (SI) epidemic model of Feline immunodeficiency virus (FIV) within cat populations in presence of multiplicative noise terms to understand the effects of environmental driving forces on the disease dynamics. The value of this study lies in two aspects. Mathematically, we propose three threshold parameters, R h s , R 1 and R 2 to utilize in identifying the stochastic extinction and persistence. In the case of stochastic persistence, we prove that there is a stationary distribution. Based on the statistical data for rural cat populations Barisey-la-Côte in France, we perform some numerical simulations to verify/extend our analytical results. Epidemiologically, we find that: (1) Large environment fluctuations can suppress the outbreak of FIV; (2) The distributions are governed by R h s ; (3) White noise perturbations of the birth rate for infectious cats (i.e., the vertical transmission) can can induce the susceptible-free dynamics.

No abstract

Understanding temporal dependencies of videos is fundamental for vision problems, but deep learning–based models are still insufficient in this field. In this article, we propose a novel deep multiplicative neural network (DMNN) for learning hierarchical long-term representations from video. The DMNN is built upon the multiplicative block that remembers the pairwise transformations between consecutive frames using multiplicative interactions rather than the regular weighted-sum ones. The block is slided over the timesteps to update the memory of the networks on the frame pairs. Deep architecture can be implemented by stacking multiple layers of the sliding blocks. The multiplicative interactions lead to exact, rather than approximate, modeling of temporal dependencies. The memory mechanism can remember the temporal dependencies for an arbitrary length of time. The multiple layers output multiple-level representations that reflect the multi-timescale structure of video. Moreover, to address the difficulty of training DMNNs, we derive a theoretically sound convergent method, which leads to a fast and stable convergence. We demonstrate a new state-of-the-art classification performance with proposed networks on the UCF101 dataset and the effectiveness of capturing complicate temporal dependencies on a variety of synthetic datasets.