Artificial intelligence for decision making in the era of Big Data – evolution,challenges and research agenda

Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.     

Considerations for development and use of AI in response to COVID-19

Artificial intelligence (AI) is playing a key supporting role in the fight against COVID-19 and perhaps will contribute to solutions quicker than we would otherwise achieve in many fields and applications. Since the outbreak of the pandemic, there has been an upsurge in the exploration and use of AI, and other data analytic tools, in a multitude of areas. This paper addresses some of the many considerations for managing the development and deployment of AI applications, including planning; unpredictable, unexpected, or biased results; repurposing; the importance of data; and diversity in AI team membership. We provide implications for research and for practice, according to each of the considerations. Finally we conclude that we need to plan and carefully consider the issues associated with the development and use of AI as we look for quick solutions.     

The impact of deep learning on document classification using semantically rich representations

This paper presents a semantically rich document representation model for automatically classifying financial documents into predefined categories utilizing deep learning. The model architecture consists of two main modules including document representation and document classification. In the first module, a document is enriched with semantics using background knowledge provided by an ontology and through the acquisition of its relevant terminology. Acquisition of terminology integrated to the ontology extends the capabilities of semantically rich document representations with an in depth-coverage of concepts, thereby capturing the whole conceptualization involved in documents. Semantically rich representations obtained from the first module will serve as input to the document classification module which aims at finding the most appropriate category for that document through deep learning. Three different deep learning networks each belonging to a different category of machine learning techniques for ontological document classification using a real-life ontology are used.Multiple simulations are carried out with various deep neural networks configurations, and our findings reveal that a three hidden layer feedforward network with 1024 neurons obtain the highest document classification performance on the INFUSE dataset. The performance in terms of F1 score is further increased by almost five percentage points to 78.10% for the same network configuration when the relevant terminology integrated to the ontology is applied to enrich document representation. Furthermore, we conducted a comparative performance evaluation using various state-of-the-art document representation approaches and classification techniques including shallow and conventional machine learning classifiers.     

Improving energy expenditure estimates from wearable devices: A machine learning approach

ABSTRACT

A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of activities using acceleration, physiological signals (e.g., heart rate, body temperature, galvanic skin response), and participant characteristics (e.g., sex, age, height, weight, body composition) collected from wearable devices (Fitbit charge 2, Polar H7, SenseWear Armband Mini and Actigraph GT3-x) as potential inputs. By utilising a leave-one-out cross-validation approach in 59 subjects, we investigated the predictive accuracy in sedentary, ambulatory, household, and cycling activities compared to indirect calorimetry (Vyntus CPX). Over all activities, correlations of at least r = 0.85 were achieved by the models. Root mean squared error ranged from 1 to 1.37 METs and all overall models were statistically equivalent to the criterion measure. Significantly lower error was observed for Actigraph and Sensewear models, when compared to the manufacturer provided estimates of the Sensewear Armband (p < 0.05). A high degree of accuracy in EE estimation was achieved by applying non-linear models to wearable devices which may offer a means to capture the energy cost of free-living activities.     

基于UAMRT的英汉翻译系统的设计与实现原理

本文提出了一种设计和实现英汉翻译系统的新方法。该方法以模板匹配替换通用算法 (UAMRT)为基础 ,通过结合句型分析算法和从句分析算法建立启发式搜索机制 ,实现从源语言句子到目标语言句子的自动翻译。它不仅能够合理地利用基于规则和基于例子的翻译系统的优点 ,而且能够在一定程度上克服它们的缺点。速度测试表明以该方法为基础实现的英汉翻译系统在P IV1 7G的计算机上的翻译速度每秒可以达到 130 0个单词左右 ;质量测试表明该系统具有较好的发展潜力     

当前知识抽取的主要技术方法解析*

 对MnM、KIM、Text2Onto、Amilcare、Melita等具有知识抽取功能的系统所应用的技术方法进行解析。提出在当前知识抽取技术中,机器学习和自然语言分析两大思路各自得到较大发展,并且在相互融合、相互借鉴中受益。在基于机器学习的知识抽取方面,出现以自适应信息抽取（Adaptive IE）、开放信息抽取（Open IE）为代表的新思路,并且有向自动本体学习（Ontology Learning）方向发展的趋势;在基于自然语言分析的知识抽取方面,基于模式标注、语义标注的方法得到广泛关注和进一步完善,并且有向基于Ontology的信息抽取（OBIE）方向发展的趋势。此外,为减少Ontology建设成本,让人们可以利用简单的自然语言构建Ontology,基于受控语言的信息抽取（CLIE）技术也得到一定的关注。     

面向科技文献的中日机器翻译合作研究

本文以多语言科技信息服务为立足点,结合中日两国面向科技文献的机器翻译研究现状,介绍了两国近几年开展的机器翻译合作项目的情况,包括合作背景与基础、知识产权、具体合作内容与成果,以及在机器翻译实用化方面的一些思考。     

机械设计类课程创新改革实验的构思与实践

根据国内外高校实践性教学过程,分析了我国高等教育中传统的实践性教学模式,从培养学生创新能力方面入手,结合教学改革实践,探讨实验教学改革的新模式,进行了创新实验构思与实践。     

Rate-equation-based VCSEL thermal model and simulation

INTRODUCTION Vertical-Cavity Surface-Emitting Lasers (VCSELs) are a new generation of semiconductor lasers that differ considerably from the conventional edge-emitting lasers. In recent years, the characteris- tics of VCSELs have improved enormously (Badilita et al., 2004). Today’s VCSELs have low-threshold current densities and high output power. Moreover, their circular output beam profiles and the suitability to be integrated into 2D arrays make them most promising candidates …     

水电安装工程预结算专家信息系统的设计

以专家系统的理论与方法为指导,利用统一建模语言(UML),探讨了水电安装工程预结算专家信息系统的建模过程与开发方法.