Zhi Li

Lucjan Janowski, Bogdan Ćmiel, Krzysztof Rusek et al.

A class of discrete probability distributions contains distributions with limited support, i.e. possible argument values are limited to a set of numbers (typically consecutive). Examples of such data are results from subjective experiments utilizing the Absolute Category Rating (ACR) technique, where possible answers (argument values) are $\{1, 2, \cdots, 5\}$ or typical Likert scale $\{-3, -2, \cdots, 3\}$. An interesting subclass of those distributions are distributions limited to two parameters: describing the mean value and the spread of the answers, and having no more than one change in the probability monotonicity. In this paper we propose a general distribution passing those limitations called Generalized Score Distribution (GSD). The proposed GSD covers all spreads of the answers, from very small, given by the Bernoulli distribution, to the maximum given by a Beta Binomial distribution. We also show that GSD correctly describes subjective experiments scores from video quality evaluations with probability of 99.7\%. A Google Collaboratory website with implementation of the GSD estimation, simulation, and visualization is provided.

Ali Vatankhah Barenji, Hanyang Guo, Zonggui Tian et al.

Recently, there has been growing interest in the field of cloud manufacturing (CM) amongst researchers in the manufacturing community. Cloud manufacturing is a customer-driven manufacturing model that was inspired by cloud computing, and its major objective was to provide ubiquitous on-demand access to services. However, the current CM architecture suffers from problems that are associated with a centralized based industrial network framework and third part operation. In a nutshell, centralized networking has had issues with flexibility, efficiency, availability, and security. Therefore, this paper aims to tackle these problems by introducing an ongoing project to a decentralized network architecture for cloud manufacturing which is based on the blockchain technology. In essence, this research paper introduces the blockchain technology as a decentralized peer to peer network for multiple cloud manufacturing providers.

Qingshan Liu, Renlong Hang, Huihui Song et al.

In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification. Specifically, we firstly warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since different SPP-nets have the same number of parameters, which share the identical initial values, and only fine-tuning the parameters in fully-connected layers ensures the effectiveness of each network, thereby greatly accelerating the training process. Then, the multi-scale satellite images are fed into their corresponding SPP-nets respectively to extract multi-scale deep features. Finally, a multiple kernel learning method is developed to automatically learn the optimal combination of such features. Experiments on two difficult datasets show that the proposed method achieves favorable performance compared to other state-of-the-art methods.