Understanding the Correlations between Social Attention and Topic Trends of Scientific Publications

Purpose： We propose and apply a simplified nowcasting model to understand the correlations between social attention and topic trends of scientific publications. Design/methodology/approach： First, topics are generated from the obesity corpus by using the latent Dirichlet allocation （LDA） algorithm and time series of keyword search trends in Google Trends are obtained. We then establish the structural time series model using data from January 2004 to December 2012, and evaluate the model using data from January 2013. We employ a state-space model to separate different non-regression components in an observational time series （i.e. the tendency and the seasonality） and apply the ＂spike and slab prior＂ and stepwise regression to analyze the correlations between the regression component and the social media attention. The two parts are combined using Markov-chain Monte Carlo sampling techniques to obtain our results. Findings： The results of our study show that （1） the number of publications on child obesity increases at a lower rate than that of diabetes publications; （2） the number of publication on a given topic may exhibit a relationship with the season or time of year; and （3） there exists a correlation between the number of publications on a given topic and its social media attention, i.e. the search frequency related to that topic as identified by Google Trends. We found that our model is also able to predict the number of publications related to a given topic.

Determining winner in multi-attribute procurement auction:a method based on technical and business experts' evaluation information

A method for solving the winner determination problem （WDP） in multi-attribute procurement auctions is proposed, based on technical and business experts＇ evaluation information. Firstly, on the background of procurements in China, a multi-attribute pro- curement auction mechanism is presented, where technical and business experts participate in the bid evaluation. Then, the concept of TOPSIS is used to determine the positive and negative ideal points of the WDP according to bid prices, the technical and business experts＇ evaluation information. Further, the closeness coefficient of each bidder （candidate supplier） is obtained by calculating the distances to the positive and negative ideal points. Thus, the winning supplier can be determined according to the closeness coefficients. Finally, a numerical example is used to illustrate the use of the proposed method.

Artificial Intelligence Applications in the Development of Autonomous Vehicles:A Survey

The advancement of artificial intelligence(AI)has truly stimulated the development and deployment of autonomous vehicles(AVs)in the transportation industry.Fueled by big data from various sensing devices and advanced computing resources,AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion.To achieve goal of full automation(i.e.,self-driving),it is important to know how AI works in AV systems.Existing research have made great efforts in investigating different aspects of applying AI in AV development.However,few studies have offered the research community a thorough examination of current practices in implementing AI in AVs.Thus,this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue.Specifically,it intends to analyze their use of AIs in supporting the primary applications in AVs:1)perception;2)localization and mapping;and 3)decision making.It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation.Based on the exploration of current practices and technology advances,this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies:1)high definition maps,big data,and high performance computing;2)augmented reality(AR)/virtual reality(VR)enhanced simulation platform;and 3)5G communication for connected AVs.This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.

General Approach to Antinormally Ordering Boson Exponential Quadratic Operators and Its Applications

A general approach to antinormally ordering the boson exponential quadratic operators(EQOs)is presented.By the approach,some important EQOs,including squeezed operator of one mode and two mode,linear quantum transformation operator,Bogolubov transformation operator are antinormally ordered.

Fuzzy State Comprehensive Evaluation of Living Basis for Small Businesses

The problem of comprehensive evaluation of liying basis for small businesses is discussed, based on fuzziness of index, a corresponding fuzzy state comprehensive evaluation method is presented; the good results that is,the evaluation of living basis for small business are illustrated by a simulated example.

Blockchain and the related issues:a review of current research topics

The blockchain represents emerging technologies and future trends.For the traditional social organization and mode of operation,the development of the blockchain is a revolution.As a decentralized infrastructure and distributed general ledger agreement,the blockchain presents us with a great opportunity to establish data security and trust for automation and intelligence development in the Internet of Things(IoT)and it creates a new un-centralized programmable smart ecosystem.Our research synthesizes and analyses extant articles that focus on blockchain-related perspectives which will potentially play an important role in sustainable development in the world.Blockchain applications and future directions always attract more attention.Blockchain technology provides strong scalability and interoperability between the intelligent and the physical worlds.

Interactive Image Enhancement by Fuzzy Relaxation

In this paper, an interactive image enhancement （HE） technique based on fuzzy relaxation is presented, which allows the user to select different intensity levels for enhancement and intermit the enhancement process according to his/her preference in applications. First, based on an analysis of the convergence of a fuzzy relaxation algorithm for image contrast enhancement, an improved version of this algorithm, which is called FuzzIIE Method 1, is suggested by deriving a relationship between the convergence regions and the parameters in the transformations defined in the algorithm. Then a method called FuzzIIE Method 2 is introduced by using a different fuzzy relaxation function, in which there is no need to re-select the parameter values for interactive image enhancement. Experimental results are presented demonstrating the enhancement capabilities of the proposed methods under different conditions.

Precision Matrix Estimation by Inverse Principal Orthogonal Decomposition

We investigate the structure of a large precision matrix in Gaussian graphical models by decomposing it into a low rank component and a remainder part with sparse precision matrix.Based on the decomposition,we propose to estimate the large precision matrix by inverting a principal orthogonal decomposition(IPOD).The IPOD approach has appealing practical interpretations in conditional graphical models given the low rank component,and it connects to Gaussian graphical models with latent variables.Specifically,we show that the low rank component in the decomposition of the large precision matrix can be viewed as the contribution from the latent variables in a Gaussian graphical model.Compared with existing approaches for latent variable graphical models,the IPOD is conveniently feasible in practice where only inverting a low-dimensional matrix is required.To identify the number of latent variables,which is an objective of its own interest,we investigate and justify an approach by examining the ratios of adjacent eigenvalues of the sample covariance matrix?Theoretical properties,numerical examples,and a real data application demonstrate the merits of the IPOD approach in its convenience,performance,and interpretability.

Deep reinforcement learning with planning guardrails for building energydemand response

Building energy demand response is projected to be important in decarbonizing energy use. A demand responseprogram that communicates ‘‘artificial’’ hourly price signals to workers as part of a social game has the potentialto elicit energy consumption changes that simultaneously reduce energy costs and emissions. The efficacy ofsuch a program depends on the pricing agent’s ability to learn how workers respond to prices and mitigatethe risk of high energy costs during this learning process. We assess the value of deep reinforcement learning(RL) for mitigating this risk. Specifically, we explore the value of combining: (i) a model-free RL method thatcan learn by posting price signals to workers, (ii) a supervisory ‘‘planning model’’ that provides a syntheticlearning environment, and (iii) a guardrail method that determines whether a price should be posted to realworkers or the planning environment for feedback. In a simulated medium-sized office building, we compareour pricing agent against existing model-free and model-based deep RL agents, and the simpler strategy ofpassing on the time-of-use price signal to workers. We find that our controller eliminates 175,000 US Dollarsin initial investment, decreases by 30% the energy cost, and curbs emissions by 32% compared to energyconsumption under the time-of-use rate. In contrast, the model-free and model-based deep RL benchmarksare unable to overcome initial learning costs. Our results bode well for risk-aware deep RL facilitating thedeployment of building demand response.

Artificial intelligence:a survey on evolution,models,applications and future trends

Artificial intelligence(AI)is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies,such as graphic processing unit,Internet of Things,cloud computing,and the blockchain,in the new generation of big data and Industry 4.0.In this paper,we construct an extensive survey over the period 1961-2018 of AI and deep learning.The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI,from underlying mechanisms to practical applications,from fundamental algorithms to industrial achievements,from current status to future trends.Although there exist many issues toward AI,it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields.

Artificial intelligence applications in finance:a survey

Finance is in our daily life.We invest,borrow,lend,budget,and save money.Finance also provides guidelines for corporation and government spending and revenue collection.Traditional statistical solutions such as regression,PCA,and CFA have been widely used in financial forecasting and analysis.With the increasing interest in artificial intelligence in recent years,this paper reviews the Artificial Intelligence(AI)techniques in the finance domain systematically and attempts to identify the current AI technologies used,major applications,challenges,and trends in Finance.It explores AI-related articles in Finance in IEEE Xplore and EI compendex databases.Findings suggest AI has been engaged in Finance in financial forecasting,financial protection,and financial analysis and decision-making areas.Financial forecasting is one of the main sub-fields of Finance affected by AI technology.The major AI technology used is supervised learning.Deep learning has gained popular in recent years.AI could be used to address some emerging topics.

The RQ-Tech methodology:a new paradigm for conceptualizing strategic enterprise architectures

The purpose of this paper is to report on a new system-theoretic based methodology and corresponding model for Enterprise Architecture development.This model captures the essence of the strategic,conceptual,doctrinal layer of the organization.Reusable Quality Technical Architectures(RQ-Tech)graphically reveals a comprehensive array of enterprise decision alternatives in easily understandable views;all while maintaining the hyperlinks to its provenance in strategic authoritative documentation.The RQ-Tech method has combined the practice of Enterprise Architectures with a modern perspective grounded in Systems Theory and the theory regarding the computer science-oriented Semantic Web.This recombination results in a distinctive methodology for developing models.This new methodology supports the conclusion that system-specific solutions produce islands of technology and can be prevented by employing better enterprise change planning.A review of the literature in three major areas illustrates the overlap common to all three domains.This review provides support for critical thinking concerning how to enrich the Enterprise Architecture practice.The research centered on finding the most significant factors to consider when translating the authoritative text and rich pictures that enterprise managers use to describe the strategic mission and vision of their complex,service-oriented enterprise into user-oriented semantic models.It provides the basis for RQ-Tech as a methodology that enables increased understanding of complex systems through use of Semantic Web standards.