The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study ...develops explainable data-driven IFD approaches for nonlinear dynamic systems. More specifically, we parameterize nonlinear systems through a generalized kernel representation for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis, we discover the existence of a bridge (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance with the use of this bridge. In order to have a better understanding of the results obtained, both unsupervised and supervised neural networks are chosen as the learning tools to identify the generalized kernel representations and design the IFD schemes; an invertible neural network is then employed to build the bridge between them. This article is a perspective article, whose contribution lies in proposing and formalizing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems.
“Mirror, mirror, on the wall, who is the fairest of them all?”
The Evil Queen
What is a
fair
way to assign rooms to several housemates and divide the rent between them? This is not just a theoretical ...question: many people have used the
Spliddit
website to obtain
envy-free
solutions to rent division instances. But envy freeness, in and of itself, is insufficient to guarantee outcomes that people view as intuitive and acceptable. We therefore focus on solutions that optimize a criterion of social justice, subject to the envy-freeness constraint, in order to pinpoint the “fairest” solutions. We develop a general algorithmic framework that enables the computation of such solutions in polynomial time. We then study the relations between natural optimization objectives and identify the
maximin
solution, which maximizes the minimum utility subject to envy freeness, as the most attractive. We demonstrate, in theory and using experiments on real data from Spliddit, that the maximin solution gives rise to significant gains in terms of our optimization objectives. Finally, a user study with Spliddit users as subjects demonstrates that people find the maximin solution to be significantly fairer than arbitrary envy-free solutions; this user study is unprecedented in that it asks people about their real-world rent division instances. Based on these results, the maximin solution has been deployed on Spliddit since April 2015.
We propose a new image encryption algorithm based on the spatiotemporal chaos of the mixed linear–nonlinear coupled map lattices. This spatiotemporal chaotic system has more outstanding cryptography ...features in dynamics than the logistic map or the system of coupled map lattices does. In the proposed image encryption, we employ the strategy of bit-level pixel permutation which enables the lower bit planes and higher bit planes of pixels permute mutually without any extra storage space. Simulations have been carried out and the results demonstrate the superior security and high efficiency of the proposed algorithm.
A support vector machine (SVM) plays a prominent role in classic machine learning, especially classification and regression. Through its structural risk minimization, it has enjoyed a good reputation ...in effectively reducing overfitting, avoiding dimensional disaster, and not falling into local minima. Nevertheless, existing SVMs do not perform well when facing class imbalance and large-scale samples. Undersampling is a plausible alternative to solve imbalanced problems in some way, but suffers from soaring computational complexity and reduced accuracy because of its enormous iterations and random sampling process. To improve their classification performance in dealing with data imbalance problems, this work proposes a weighted undersampling (WU) scheme for SVM based on space geometry distance, and thus produces an improved algorithm named WU-SVM. In WU-SVM, majority samples are grouped into some subregions (SRs) and assigned different weights according to their Euclidean distance to the hyper plane. The samples in an SR with higher weight have more chance to be sampled and put to use in each learning iteration, so as to retain the data distribution information of original data sets as much as possible. Comprehensive experiments are performed to test WU-SVM via 21 binary-class and six multiclass publically available data sets. The results show that it well outperforms the state-of-the-art methods in terms of three popular metrics for imbalanced classification, i.e., area under the curve, F-Measure, and G-Mean.
Online social networks provide an opportunity to spread messages and news fast and widely. One may appreciate the quick spread of legitimate news and messages but misinformation can also be spread ...quickly and may raise concerns, questioning reliability and trust in such networks. As a result, detecting misinformation and containing its spread has become a hot topic in social network analysis. When misinformation is detected, some actions may be necessary to reduce its propagation and impact on the network. Such actions aim to minimize the number of users influenced by misinformation. This paper reviews approaches for solving this problem of minimizing spread of misinformation in social networks and proposes a taxonomy of different methods.
The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, current communication systems, which were designed ...on the basis of conventional communication theories, significantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learning-based communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of NOMA, massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We envision that the appealing deep learning- based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.
We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic curriculum learning, where the Student tries to learn a complex task, and the Teacher automatically chooses subtasks ...from a given set for the Student to train on. We describe a family of Teacher algorithms that rely on the intuition that the Student should practice more those tasks on which it makes the fastest progress, i.e., where the slope of the learning curve is highest. In addition, the Teacher algorithms address the problem of forgetting by also choosing tasks where the Student's performance is getting worse. We demonstrate that TSCL matches or surpasses the results of carefully hand-crafted curricula in two tasks: addition of decimal numbers with long short-term memory (LSTM) and navigation in Minecraft. Our automatically ordered curriculum of submazes enabled to solve a Minecraft maze that could not be solved at all when training directly on that maze, and the learning was an order of magnitude faster than a uniform sampling of those submazes.
Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many ...articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.
In the recent decade, there has been an exponential rise in the development and study of Blockchain Technology space. The driving force behind the interest in Blockchain research has been its key ...characteristics that provide security, anonymity, and integrity without relying on trusted third party organizations. Initially, Blockchain usage was restricted to cryptocurrencies only, however, with the advent of Ethereum that supports creation and execution of smart contracts (small computer programs that run on Blockchain), applications beyond cryptocurrencies are being developed and explored. Although there has been substantial progress towards improving Blockchain technology with a focus on the smart contracts, however, it was identified that there is a lack of descriptive review on the applicability of smart contracts in securing the Internet and IoT. This paper aims to identify and analyse peer reviewed literature that seeks to use Blockchain smart contracts for securing Internet in general and Internet of Things in particular and presents the systematic analysis of the identified literature. Finally, the paper highlights some challenges and future research directions in the field of Blockchain smart contracts application in securing Internet and IoT.
This article describes the development of Microsoft
, the
most popular social chatbot in the world. XiaoIce is uniquely designed as an
artifical intelligence companion with an emotional connection to ...satisfy the
human need for communication, affection, and social belonging. We take into
account both intelligent quotient and emotional quotient in system design, cast
human–machine social chat as decision-making over Markov Decision
Processes, and optimize XiaoIce for long-term user engagement, measured in
expected Conversation-turns Per Session (CPS). We detail the system architecture
and key components, including dialogue manager, core chat, skills, and an
empathetic computing module. We show how XiaoIce dynamically recognizes human
feelings and states, understands user intent, and responds to user needs
throughout long conversations. Since the release in 2014, XiaoIce has
communicated with over 660 million active users and succeeded in establishing
long-term relationships with many of them. Analysis of large-scale online logs
shows that XiaoIce has achieved an average CPS of 23, which is significantly
higher than that of other chatbots and even human conversations.
PDF
