Information Ordering with an Event‐Enriched Vector Space Model for Multi‐Document News Summarization

Information ordering is a nontrivial task in multi‐document summarization (MDS), which typically relies on the traditional vector space model (VSM) notorious for semantic deficiency. In this article, we propose a novel event‐enriched VSM to alleviate the problem by building event semantics into sentence representations. The mediation of event information between sentence and term, especially in the news domain, has an intuitive appeal as well as technical advantage in common sentence‐level operations such as sentence similarity computation. Inspired by the block‐style writing by humans, we base the sentence ordering algorithm on sentence clustering. To accommodate the complexity introduced by event information, we adopt a soft‐to‐hard clustering strategy on the event and sentence levels, using expectation–maximization clustering and K‐means, respectively. For the purpose of cluster‐based sentence ordering, the event‐enriched VSM enables us to design an ordering algorithm to enhance event coherence computed between sentence and sentence–context pairs. Drawing on the findings of earlier research, we also incorporate topic continuity measures and time information into the scheme. We evaluate the performance of the model and its variants automatically and manually, with experimental results showing clear advantage of the event‐based model over baseline and non‐event‐based models in information ordering for multi‐document news summarization. We are confident that the event‐enriched VSM has even greater potential in summarization and beyond, which awaits further research. © 2014 Wiley Periodicals, Inc.     

Codifying a crisis: Progressing from information sharing to distributed decision‐making

A key challenge in crisis management is maintaining an adequate information position to support coherent decision‐making between a range of actors. Such distributed decision‐making is often supported by a common operational picture that not only conveys factual information but also attempts to codify a dynamic and vibrant crisis management process. In this paper, we explain why it is so difficult to move from information sharing towards support for distributed decision‐making. We argue that two key processes need to be considered: supporting both the translation of meaning and the transformation of interests between those on the front line and those in the remote response network. Our analysis compares the information‐sharing processes in three large‐scale emergency response operations in the Netherlands. Results indicate that on several occasions the collaborative decision‐making process was hampered because actors limited themselves to factual information exchange. The decision‐making process only succeeds when actors take steps to resolve their varying interpretations and interests. This insight offers important lessons for improving information management doctrines and for supporting distributed decision‐making processes.     

Distance‐based multicriteria group decision‐making approach with probabilistic linguistic term sets

Probabilistic linguistic term sets (PLTSs) are an important expression for hesitant linguistic preference information under group decision‐making circumstances. This study investigates problems of multicriteria group decision making (MCGDM) with PLTSs. A novel and rational comparison method is first proposed, and two distance measures for PLTSs are defined. The weight of each criterion is then obtained via maximum deviation method. Subsequently, extended Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) ‐ VIseKriterijumska Optimizacija I Kompromisno Resenje a Serbian name (VIKOR) and TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) methods are developed as decision support models to handle MCGDM problems. An illustrative example is also analysed to demonstrate the rationality and feasibility of the proposed methods.     

A cloud‐based responsive replenishment system in a franchise business model using a fuzzy logic approach

In today's volatile markets, increasingly unpredictable customer demand is exerting great challenges to responsive replenishment. The complexity of responsive replenishment is higher when the business is global in which demand in both domestic and overseas markets has to be catered for. The emergence of cloud computing has eased the difficulties as it allows nearly real‐time access to a universal platform for information sharing between franchisors and franchisees, creating huge opportunities for understanding global market needs for responsive replenishment. Considering the existence of uncertainties due to the fluctuating demands, fuzzy logic is useful in providing decision support for replenishment in uncertain environments. This paper presents a cloud‐based responsive replenishment system to manage operation data of a franchise business using cloud computing, and for analysis using fuzzy logic in order to provide franchisors with the required inventory levels. To the best of our knowledge, this is the first study that applies cloud computing and artificial intelligence techniques in franchising. A pilot run of the system is conducted in an education company, which is considered to be a good representation of an industry operating with a franchise model. The results show that the system allows franchisors to formulate effective responsive replenishment strategies.     

Support‐vector modeling of electromechanical coupling for microwave filter tuning

This article presents a support‐vector modeling method for electromechanical coupling of microwave filter tuning in the case of the scarcity of experimental data available. This has been done for the purpose of establishing an accurate coupling model which can be used in an automatic tuning device of volume‐producing filters. In the method, a coupling model that reveals the effect of mechanical structure on the filter electrical performance is established by using a proposed algorithm which can incorporate multi‐kernel and prior knowledge into linear programming support vector regression (LPSVR). Some experiments from three microwave filters have been performed, and the results confirm the effectiveness of the support‐vector modeling method. Moreover, the comparative results also show that the proposed multi‐kernel prior knowledge LPSVR can improve the data‐driven modeling accuracy of small dataset. The proposed algorithm show great potential in some problems where a sufficient experimental data is difficult and costly to obtain, but some prior knowledge data from a simulation model can be easily obtained. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Decision‐theoretic rough sets under Pythagorean fuzzy information

Decision‐theoretic rough sets (DTRSs), which provide a classical model of three‐way decisions (3WDs), play an important role in risk decision‐making problems. The risk is associated with the loss function of DTRSs, which is evaluated by the decision makers. As a new extension of fuzzy sets, Pythagorean fuzzy sets can handle uncertain information more flexibly than intuitionistic fuzzy sets in the process of decision making and it gives a new measure for the determination of loss functions of DTRSs. More specifically, we take into account the loss functions of DTRSs with Pythagorean fuzzy numbers and propose a Pythagorean fuzzy decision‐theoretic rough set (PFDTRS) model. Some properties of the expected losses are carefully investigated. Then we further design three approaches for deriving 3WDs with the PFDTRS model. The group decision making (GDM) based on the PFDTRS model is also discussed. It provides a novel interpretation for the determination of loss functions. With the aid of the Pythagorean fuzz weighted averaging operator, we aggregate the loss functions, as suggested by the all experts, which support a coherent way of designing information granules in the presence of numerics. An algorithm for 3WDs in GDM based on the PFDTRS model is designed. Then, an example is presented to elaborate on 3WDs with the PFDTRS model.     

Multi‐criteria group decision‐making method based on TODIM with probabilistic interval‐valued hesitant fuzzy information

Probabilistic interval‐valued hesitant fuzzy sets (PIV‐HFSs) are suitable for aggregating information from different groups because the probabilistic information of all the groups can be included by using interval values. Moreover, decision makers (DMs) prefer to use interval values to provide evaluation information. Furthermore, the traditional multi‐criteria group decision‐making (MCGDM) approach has some limitations, such as obtaining the DMs' weights with inappropriate methods and neglecting the interactions amongst the criteria and the psychological characteristics of DMs. Motivated by these research background, the main contents of this study are as follows. First, PIV‐HFSs are proposed, and the convex combination operation is extended into PIV‐HFSs. Second, a hybrid MCGDM approach with PIV‐HFSs is suggested that is based on the maximizing deviation method, fuzzy analytic network process (FANP) and TODIM (an acronym in Portuguese for interactive and multi‐criteria decision‐making model). Third, an evaluation case of health management centres based on the service‐specific failure mode and effect analysis (FMEA) is considered. The results show that the most crucial secondary factor is frequency (0.35775) and that the most serious failure mode is the inaccurate check‐in. The results demonstrate that the proposed model can evaluate service quality effectively and that it performs better than other methods.     

A database approach for information communication in a peer‐to‐peer collaborative CAD environment

Timely and efficient information communication is a key factor in ensuring successful collaboration in engineering collaborative design. This work proposes a database approach to support information communication between distributed and autonomous CAD systems. It provides the designer with an easy and flexible way, a project‐based propagation meta‐table, to specify what parts of a CAD information model should be communicated to other collaborating designers. A CAD peer manager, containing a peer database that stores information to be exchanged with the other collaborators, wraps each participating CAD system. The peer manager identifies changes made to the CAD model by using stored procedures and active rules in the peer database that are automatically generated based on the propagation meta‐table. The identified updates are propagated in a timely manner to other peers via inter‐database message passing, thereby minimizing the volume of necessary information to be exchanged. Furthermore, remote peer designers can flexibly incorporate, filter, or delete received updates by using a propagation control interface, which is also used to issue user's commands to download the data from the CAD system to the peer database and lookup the received messages in the peer database. The approach is applicable on any CAD system having a CORBA interface and can also be applied to other kinds of object‐oriented interfaces. Copyright © 2007 John Wiley & Sons, Ltd.     

Coastal decision‐makers’ perspectives on updating storm surge guidance tools

This paper reports on the process and results of focus groups conducted as stakeholder engagement for a two‐year, marine extension agency‐sponsored interdisciplinary project in coastal Louisiana, USA. The project involved refining the topographic features (eg flood protection infrastructure) of a computational storm surge model using local knowledge of elevation data. Coastal Louisiana experiences continual changes to the built and natural environment and persistent land loss, necessitating frequent adjustments to real‐time storm surge and restoration planning models to make them more useful for decision‐makers. Stakeholder involvement ensured accurate updates to the real‐time computational system and accessible and understandable modelling results displayed by an interactive decision‐support tool. The findings provide insights and recommendations from various practitioners that are applicable to decision‐support model and tool development for coastal hazard emergency response.     

A hybrid FAM‐CART model for online data classification

In this paper, an online soft computing model based on an integration between the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) for undertaking data classification problems is presented. Online FAM network is useful for conducting incremental learning with data samples, whereas the CART model prevails in depicting the knowledge learned explicitly in a tree structure. Capitalizing on their respective advantages, the hybrid FAM‐CART model is capable of learning incrementally while explaining its predictions with knowledge elicited from data samples. To evaluate the usefulness of FAM‐CART, 2 sets of benchmark experiments with a total of 12 problems are used in both offline and online learning modes. The results are examined and compared with those published in the literature. The experimental outcome positively indicates that the online FAM‐CART model is useful for tackling data classification tasks. In addition, a decision tree is produced to allow users in understanding the predictions, which is an important property of the hybrid FAM‐CART model in supporting decision‐making tasks.     

Allocating natural resource reduction amounts: A data envelopment analysis based‐approach considering production technology heterogeneity

Previous studies resource allocation methods based on data envelopment analysis assume that all the assessed decision‐making units share a common production technology, and all decision‐making units become efficient after the resources are allocated. However, in the real world, production technology tends to be heterogeneous among the decision‐making units because of the differences in economic development, geographic location, and market conditions. Correspondingly, when some decision‐making units are far away from the efficient frontier, they may not become efficient easily using the resources allocated to them. In this paper, we propose a data envelopment analysis‐based approach which considers production technology heterogeneity among decision‐making units when allocating resource reduction amounts to each. In our model, the decision‐making units are divided into subgroups based on their economic development level, an important indicator directly reflecting each decision‐making unit's production technology level. Each subgroup has its specific production technology, and the decision‐making units in the same subgroup have a similar technology level, which allows better identification of how the production of those decision‐making units can change when their resource inputs change. We present an empirical example using China's mainland provinces as decision‐making units to demonstrate the practicability and applicability of our proposed model.     

Tracking a benchmark index – using a spreadsheet‐based decision support system as the driver

This paper presents a two‐phase quantitative approach for enhanced index investing based on the mean‐variance model and the goal programming method. In the first stage, we use the mean‐variance theory to select better performing stocks for an investment pool. Then, in the second stage, we use a goal programming method to weight the selected stocks by balancing both the tracking error and the rate of return. In addition to the theoretical formulation, we construct a spreadsheet‐based decision support system (DSS) based on the transaction data to help resolve the index tracking problem. The paper contributes to the literature in two ways. For academics, we present original discussions on combining an interdisciplinary mean‐variance model and a goal programming method. Unlike the conventional approach used for enhanced index investing that requires a fund manager to actively buy and sell stocks to improve returns, our approach is based on historical data and deduces subjective judgments. Meanwhile, for practitioners, we present an original discussion on using a DSS to support index investing. The results of an empirical survey of the Taiwan stock market are also presented.     

Developing and assessing an intelligent forex rolling forecasting and trading decision support system for online e‐service

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back‐propagation neural network (BPNN)‐based forex forecasting subsystem and Web‐based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e‐service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.     

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

Patients always want a precise diagnosis and appropriate treatment advice when they are diagnosed with a disease. Furthermore, the original information about certain features and symptoms appears in different forms, and the effect of time is always ignored in the process of diagnosis. To overcome these defects, this paper develops a systematic multiperiod hybrid decision support model, which combines the similarity measurement and three‐way decision theory to provide prediction and treatment advice for patients under the fuzzy environment. This multiperiod hybrid decision support model, which considers the effect of time, including transformation module, multiperiod integration module, similarity module, prediction module, and three‐way decision module, provides disease prediction and advice on treatment based on similarities and three‐way decision theory. To validate this model, we construct an illustration composed of four cases, and this ultimately shows that MPH‐SDM can effectively support patients' disease diagnoses and treatment.     

An Interval‐Valued Pythagorean Fuzzy Outranking Method with a Closeness‐Based Assignment Model for Multiple Criteria Decision Making

The concept of interval‐valued Pythagorean fuzzy (IVPF) sets is capable of handling imprecise and ambiguous information and managing complex uncertainty in real‐world applications. This paper focuses on multiple criteria decision analysis involving IVPF information and proposes a new outranking decision‐making method that uses a closeness‐based assignment model. In contrast to the existing assignment‐based methodology, the uniqueness of this paper is the consideration of uncertain information represented by IVPF values, the determination of criterion‐wise precedence rankings based on a closeness‐based approach, and the development of a new measure for scalar representation. First, to underlie anchored judgments in subjective decision‐making processes, this paper presents a compromising concept of the closeness index with the positive‐ideal and negative‐ideal IVPF values to identify criterion‐wise precedence ranks among alternatives. Next, this paper defines the concept of matrices of precedence frequency and contribution to provide a basis for the proposed assignment model. To overcome the difficulty of lacking nontrivial scalar representations, a useful measure is also developed to appropriately describe IVPF values. Based on a closeness‐based assignment approach, a novel outranking decision‐making method is proposed to transform the extended criterion‐wise ranks into the ultimate priority orders of the alternatives. The proposed method is first implemented in a practical problem of selecting a bridge construction method to demonstrate its feasibility and applicability. Moreover, its practicality and effectiveness are verified through a comparative analysis with relevant assignment‐based approaches. Further comparative analyses with newly developed IVPF decision‐making methods are conducted for both a risk evaluation problem and an investment problem to examine the advantages of the proposed method and extend the current technique by considering distinct preference information for adapting to the particularities in practice.     

CLIPS 在黑河水文水资源决策支持系统中的集成研究

传统的决策支持系统使用数学模型和数值计算方法来辅助决策, 因此决策者必须对某领域的模型和数据有一定的了解。黑河水文水资源决策支持系统集成了SWAT、TopModel、水文系统模型、时间序列模型等一系列模型。这些模型的结构和参数都比较复杂, 决策者难以掌握。在黑河水文水资源决策支持系统中, 加入专家知识支持, 使得决策者更容易的使用该系统是很有必要的。研究中, 采用美国航空航天局(NASA ) 开发的通用专家系统开发工具CLIPS 来实现专家知识支持,使用嵌入式方式将CL IPS 的核心推理机集成到黑河水文水资源决策支持系统中。CLIPS 专家系统在黑河水文水资源决策支持系统中的作用有3 点: ①辅助决策者进行模型的选择; ②辅助决策者进行模型参数的输入; ③辅助决策者分析模型运行的结果。     

Integrated approach of cognitive maps and neural networks using qualitative information on the World Wide Web: the KBNMiner

Abstract: In this paper, we propose a method for integrating cognitive maps and neural networks to gain competitive advantage using qualitative information acquired from news information on the World Wide Web. We have developed the KBNMiner, which is designed to represent the knowledge of domain experts with cognitive maps, to search and retrieve news information on the Internet according to the knowledge and to apply the information to a neural network model. In addition, we investigate ways to train neural networks more effectively by separating the learning data into two groups on the basis of event information acquired from news information. To validate our proposed method, we applied 180,000 news articles to the KBNMiner. The experimental results are found to support our proposed method through tenfold cross‐validation.     

An ontology based framework for mining dependence relationships between news and financial instruments

Before news is input into financial trading algorithms/models, it needs human judgements for exploring the market implications of news content, distinguishing significance extent of news, and finding out the impact of polar type of each kind of news on certain financial instrument trading activity. But Dawes and Faust (1989) reported that people usually rely on clinical judgements, especially it is hard for them to distinguish valid decision variables from invalid ones in decision making. Thus, in order to alleviate this problem and provide more objective decision making support about news in financial market, an ontology based framework is proposed, for investigating the actuarial dependence relationships between news and financial instruments trading activities as well as identifying more valid news for trading decision making. This framework is expected to help people in financial market how to consider weight for each kind of news when inputted in trading algorithms/models of certain financial instruments.     

A Multigranularity Linguistic Group Decision‐Making Method Based on Hesitant 2‐Tuple Sets

Hesitant fuzzy linguistic term set (HFLTS) is a very useful technology in dealing with decision‐making problems where people have hesitancy in providing their linguistic assessments. Distinct methods have been developed to aid decision making with HFLTSs, yet there is little research involving the issue that how to deal with the multigranularity hesitant fuzzy linguistic information. The aim of this paper is to develop the aggregation method for multigranularity hesitant fuzzy linguistic information and solve the linguistic group decision problem with different linguistic term sets. To do so, we first modify the translation functions and aggregation operators in the existing 2‐tuple linguistic representation models so as to aggregate linguistic terms from different linguistic term sets. Then, we introduce the notion of hesitant 2‐tuple sets to make computation of HFLTSs without loss of information, and develop some new operators to aggregate HFLTSs from different linguistic term sets. Using these operators, we propose a method to deal with multigranularity linguistic group decision‐making problems with different situations where importance weights of either criteria or experts are known or unknown. Finally, the multigranularity linguistic group decision‐making model is implemented to the healthcare waste treatment in West China Hospital to validate its effectiveness and efficiency in aiding decision‐making process.