Signal Temporal Logic (STL) is a powerful formal language for specifying real-time specifications of Cyber-Physical Systems (CPS). Transforming specifications written in natural language into STL formulas automatically has attracted increasing attention. Existing rule-based methods depend heavily on rigid pattern matching and domain-specific knowledge, limiting their generalizability and scalability. Recently, Supervised Fine-Tuning (SFT) of large language models (LLMs) has been successfully applied to transform natural language into STL. However, the lack of fine-grained supervision on atomic proposition correctness, semantic fidelity, and formula readability often leads SFT-based methods to produce formulas misaligned with the intended meaning. To address these issues, we propose RESTL, a reinforcement learning (RL)-based framework for the transformation from natural language to STL. RESTL introduces multiple independently trained reward models that provide fine-grained, multi-faceted feedback from four perspectives, i.e., atomic proposition consistency, semantic alignment, formula succinctness, and symbol matching. These reward models are trained with a curriculum learning strategy to improve their feedback accuracy, and their outputs are aggregated into a unified signal that guides the optimization of the STL generator via Proximal Policy Optimization (PPO). Experimental results demonstrate that RESTL significantly outperforms state-of-the-art methods in both automatic metrics and human evaluations.

Due to the passage of time and uncertain demand changes, the two-level supply chain finance system transforms into a dynamic and uncertain multimodel discrete system. The continuous fluctuations in the capital levels of node companies significantly impact both the business activities of these companies and the stability of the system. Effective capital equilibrium switching control can adjust the system’s steady state. Consequently, this article introduces a strategy for dynamic capital equilibrium based on a fundamental model of the system’s capital. Additionally, a capital equilibrium switching strategy, composed of a robust control strategy and a capital equilibrium strategy, is designed based on the Takagi-Sugeno model to achieve stability. Furthermore, multiple sets of simulation tests not only validate the effectiveness of the capital equilibrium switching strategy under different external demand distributions but also confirm the scientificity and reliability of the method proposed. Finally, this article analyzes the impact of internal capital scheduling on both the capital equilibrium and the overall system.

Opinion and action have important roles in human society, but few studies have considered the interactive evolution of opinion and action. Inspired by the social network DeGroot (SNDG) model and the social network opinions and actions evolution (SNOAE) model, an opinion and action interactive evolution (OAIE) model based on social network leadership and opinion estimation of action is proposed, and the stable state and consensus of agents’ opinions and actions are investigated. The results show that in the interactive evolution of opinion and action, when each agent can accurately estimate the opinions of other agents based on their actions in the final stage, all agents are stable agents. If the opinion leader exists, a consensus and unified action can be formed by all agents; otherwise, all agents can reach a consensus and unified action in the subnetwork. In addition, if each agent is always unable to estimate the true opinions of other agents, then all agents are oscillatory agents.

Cyber-Physical Systems (CPSs) connect the cyber world with the physical world through a network of interrelated elements, such as sensors and actuators, robots, and other computing devices. There are increasing number of beneficial applications in dependable sectors such as aviation, transportation, aerospace, healthcare, etc.. The inherent characteristics of CPSs pose a number of challenges to requirements engineering. Unlike normal information systems, CPSs need to continuously detect and adapt to the environment changes. The interactive environment becomes the first-class citizen because the features and the changing patterns in environment are must-to-be considered. Moreover, in such systems, many non-functional requirements are environment related, like timing, safety, security, and privacy requirements. This tutorial will introduce an environment modelling based approach to engineering the requirements of CPSs. Extending the Problem Frames representations, this approach structures the model of the environmental elements and provides analysis methods for deriving and specifying requirements. We deliver this tutorial with a few supporting tools that assist the modelling and verification of the system specification, demonstrated with working examples in sufficient details. After the tutorial, participants will be able to work on the environment modelling requirements engineering for their own projects, with some hands-on experience and a good knowledge of some tool support.

Abstract 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.

Hesitant information is powerful and flexible to denote decision maker's judgments. Hesitant multiplicative preference relations (HMPRs) own the advantages of preference relations and hesitant fuzzy sets that permit the decision makers (DMs) to compare objects by using several values. Just as other types of preference relations, how to derive the priority weight vector is a crucial step. According to the principle of the consistency concept for multiplicative preference relations, this paper first introduces a new consistency concept for HMPRs, which avoids the disadvantages of the previous ones. Using the new concept, models to judge the consistency of HMPRs are built. Then, a consistency probability-based method to derive the hesitant fuzzy priority weight vector from HMPRs is offered. Considering the incomplete case, consistency-based programming models to determine the missing values are constructed. To address group decision making with HMPRs, a distance measure is defined to determine the weights of the DMs, and a consensus index is proposed. Then, a consistency and consensus-based group decision-making algorithm is performed. Finally, two practical examples, an investment problem and a water conservancy problem are offered to illustrate the feasibility and efficiency of the new algorithm. Comparison analysis from the numerical and theoretical aspects verifies the potential application of the new procedure.

Microscopic vehicle emissions models have been well developed in the past decades. Those models require second-by-second vehicle trajectory data as a key input to perform vehicle energy/emissions estimation. Due to the omnipresence of mobile sensors such as floating cars, real-world vehicle trajectory data can be collected in a large scale. However, most large-scaled mobile sensor data in practice are sparse in terms of sampling rate due to the consideration in implementation cost. In this paper, a new modal activity framework for vehicle energy/emissions estimation using sparse mobile sensor data is presented. The valid vehicle dynamic states are identified including four driving modes, named acceleration, deceleration, cruising, and idling. The best valid vehicle dynamic state with the largest probabilities is selected to reconstruct the second-by-second vehicle trajectory between consecutive sampling times. Then vehicle energy/emissions factors are estimated based on operating mode distributions. The proposed model is calibrated and validated using the Next Generation Simulation's dataset, and shows better performance in vehicle energy/emissions estimation compared with the linear interpolation model. Sensitivity analysis is performed to show the model accuracy with different time intervals. This paper provides a new methodology for vehicle energy/emissions estimation and extends the application area of sparse mobile sensor data.

The equipment faults of the interlocking system in rail transit system has occurred frequently, and these faults can cause serious accidents.The modeling of the interlocking system must aim at the stochasticity and real-time characteristics of equipment faults.Therefore, this paper proposes to model the interlocking system using stochastic hybrid automata.In order to improve model efficiency, we try to extract the pattern of the interlocking system model, and reuses these patterns in system modeling.The main contributions include: (1) Based on the business analysis of the interlocking system, 12 model patterns of the interlocking system are extracted; and (2) the modeling process of the interlocking system based on patterns reuse is given to guide system modeling.Finally, a case study is presented to illustrate the feasibility and effectiveness of our approach.

Prior research has discovered that groups tend to discuss shared information while failing to discuss unique information in decision‐making processes. In our study, we conducted a lab experiment to examine the effect of prediscussion note‐taking on this phenomenon. The experiment used a murder‐mystery hidden profile task. In all, 192 undergraduate students were recruited and randomly assigned into 48 four‐person groups with gender being the matching variable (i.e., each group consisted of four same‐gender participants). During the decision‐making processes, some groups were asked to take notes while reading task materials and had their notes available in the following group discussion, while the other groups were not given this opportunity. Our analysis results suggest that (a) the presence of an information piece in group members' notes positively correlates with its appearance in the subsequent discussion and note‐taking positively affects the group's information repetition rate; (b) group decision quality positively correlates with the group's information sampling rate and negatively correlates with the group's information sampling/repetition bias; and (c) gender has no statistically significant moderating effect on the relationship between note‐taking and information sharing. These results imply that prediscussion note‐taking could facilitate information sharing but could not alleviate the biased information pooling in hidden profile tasks.

Indefinite kernel support vector machine (IKSVM) has recently attracted increasing attentions in machine learning. Different from traditional SVMs, IKSVM essentially is a non-convex optimization problem. Some algorithms directly change the spectrum of the indefinite kernel matrix at the cost of losing some valuable information involved in the kernels so as to transform the non-convex problem into a convex one. Other algorithms aim to solve the dual form of IKSVM, but suffer from the dual gap between the primal and dual problems in the case of indefinite kernels. In this paper, we directly focus on the non-convex primal form of IKSVM and propose a novel algorithm termed as IKSVM-DC. According to the characteristics of the spectrum for the indefinite kernel matrix, IKSVM-DC decomposes the objective function into the subtraction of two convex functions and thus reformulates the primal problem as a difference of convex functions (DC) programming which can be optimized by the DC algorithm (DCA). In order to accelerate convergence rate, IKSVM-DC further combines the classical DCA with a line search step along the descent direction at each iteration. A theoretical analysis is then presented to validate that IKSVM-DC can converge to a local minimum. Systematical experiments on real-world datasets demonstrate the superiority of IKSVM-DC compared to state-of-the-art IKSVM related algorithms.

With respect to multiattribute group decision-making with interval-valued intuitionistic uncertain linguistic information, correlation and correlation coefficient are defined. To obtain the optimal weight vector, models based on the introduced correlation coefficient are constructed. Furthermore, two aggregation operators called the interval-valued intuitionistic uncertain linguistic Shapley averaging operator and the interval-valued intuitionistic uncertain linguistic Shapley geometric operator are defined, which are used to aggregate the interval-valued intuitionistic uncertain linguistic information. Moreover, an approach to multiattribute group decision-making under interval-valued intuitionistic uncertain linguistic environment is developed. Finally, an illustrative example is selected to verify the practicality and feasibility of the proposed procedure.

Interval-valued linguistic variables are efficient tools to express the decision makers’ uncertain qualitative judgments. Considering the application of interval-valued linguistic variables, this paper proposes an interval distance measure, which is then used to define interval-valued linguistic interval distance measures by combining the 2-tuple linguistic representation model. To reflect the interactions between elements in a set, three correlative interval distance measures on intervalvalued linguistic variables are proposed. Meanwhile, several models designed to obtain the optimal weighting vector are constructed. After that, an approach to pattern recognition and to multi-attribute decision making with interval-valued linguistic information is developed. Meanwhile, associated examples are offered to demonstrate the concrete application of the proposed procedure.

A real-world signal is always corrupted with noise. The separation between a signal and noise is an indispensable step in a variety of signal-analysis applications across different scientific domains. In this paper, we propose a double least-squares projections (DLSPs) method to estimate a signal from the noisy data. The first least-squares projection is to find a signal-dimensional optimal approximation of the noisy data in the least-squares sense. In this step, a rough estimation of the signal is obtained. The second least-squares projection is to find an approximation of a signal in another crossed signal-dimensional space in the least-squares sense. In this step, a much improved signal estimation that is close to orthogonal to the separated noise subspace can be obtained. The DLSP implements projection operation twice to obtain an almost perfect estimation of a signal. The application of the DLSP method in seismic random noise attenuation and signal reconstruction demonstrates the successful performance in seismic data processing.

Interval fuzzy preference relations (IFPRs) that can simply denote the lower and upper bounds of decision makers' uncertain judgments are keenly studied by researchers and are widely used in practical decision-making problems. Consistency analysis is vitally important to avoid illogical ranking orders. First, this paper analyzes issues in previous additive consistency concepts for IFPRs. Then, it presents a new additive consistency definition that overcomes issues in previous ones. A linear programming model to judge the additive consistency of IFPRs is constructed, and a method to derive additive consistent IFPRs is proposed. Furthermore, a goal programming model to determine missing values in an incomplete IFPR is built, which have the highest consistent level with respect to known values. Regarding group decision making, a group consensus index is defined to measure the consensus of individual IFPRs, and an approach to improve the consensus level is introduced. Finally, a method for group decision making with IFPRs is developed, which can address incomplete and inconsistent cases. Associated examples are given to show the efficiency and feasibility of the developed theoretical results and comparative analysis is made.

Interval-valued hesitant fuzzy sets permit the membership degree of an element to have several possible interval values in [0, 1] rather than real numbers, which can well deal with inherent hesitancy and uncertainty in the human decision-making process. In this paper, we first point out the issue of the existing correlation coefficients of interval-valued hesitant fuzzy sets and then define several new ones, which do not have to consider the lengths of interval-valued hesitant fuzzy elements and the arrangement of their possible interval values. Since the assumption that the elements in a set are independent is usually violated, we further define several Shapley weighted correlation coefficients, which consider their interactions. To deal with the situations where the elements are correlative and the weight formation is incompletely known, models for the optimal fuzzy measures on a feature set and on an attribute set are established, respectively. Finally, a procedure to pattern recognition and multiattribute decision making with incomplete weight information and interactive conditions is developed. Meanwhile, the corresponding examples are provided to show the practicality and feasibility of the proposed procedures.

Requirements elicitation is one of the most important and challenging issues in requirements engineering. This paper proposes a systematic approach for capturing the software requirements from the expected interactions between the software-to-be and its interactive environment. Firstly, the software environment ontology and the interaction ontology are developed for serving as the meta-models for the descriptions of the interactive environment and the interactions. Then a process has been proposed for guiding the analysts to capture the software requirements step by step from descriptions of the interactive environment and the expected interactions. Finally, a case study has been presented for illustrating the usage of our approach.

The distance-separated simultaneous-sourcing (DSSS) technique can make the smallest interference between different sources. In a distance-separated simultaneous-source acquisition with two sources, we propose the use of a novel iterative seislet-frame thresholding approach to separate the blended data. Because the separation is implemented in common shot gathers, there is no need for the random scheduling that is used in conventional simultaneous-source acquisition, where random scheduling is applied to ensure the incoherent property of blending noise in common midpoint, common receiver, or common offset gathers. Thus, DSSS becomes more flexible. The separation is based on the assumption that the local dips of the data from different sources are different. We can use the plane-wave destruction algorithm to simultaneously estimate the conflicting dips and then use seislet frames with two corresponding local dips to sparsify each signal component. Then, the different signal components can be easily separated. Compared with the FK-based approach, the proposed seislet-frame-based approach has the potential to obtain better separated components with less artifacts because the seislet frames are local transforms while the Fourier transform is a global transform. Both simulated synthetic and field data examples show very successful performance of the proposed approach.

The modern economy involves a variety of marketplaces, and the Internet has led to the development of a new efficient marketplace-the one-shot multiattribute exchange. It is an important decision problem for a matchmaker (or broker) to achieve the optimal trade matching in one-shot multiattribute exchanges; however, to the best of our knowledge, there has been little work on this issue under fuzzy environments. This paper proposes an optimal matching approach for one-shot multiattribute exchanges with simultaneous fuzzy information and indivisible demand considerations. First, we employ fuzzy set theory to represent the traders' orders with fuzzy information and then put forward a calculation method of the matching degree based on the improved fuzzy information axiom. Second, on the basis of the matching degree, we construct a fuzzy multiobjective programming model for one-shot multiattribute exchanges with indivisible demand. Afterward, the credibility measure is introduced to convert the model into a crisp one. The crisp model belongs to a class of multiobjective nonlinear general assignment problems and has NP-hard complexity. In order to solve the crisp model effectively, we develop a problem-specified metaheuristic algorithm, i.e., multiobjective discrete differential evolution. Finally, we conduct comprehensive computational experiments on numerical examples to illustrate the application and performance of the proposed model and algorithm.

In recent years, with gradual application of formal verification methods to automatic train control (ATC) systems, the problem of verification failure occurs due to the complexity of the verification system. In order to solve this problem, this paper proposes a safety attribute based projection for the ATC systems. We describe this verification system using Problem Frames approach and define projection operators. By projection, the original verification system is divided into several sub-systems. An experiment is also conducted with data collected from a metro line. The experimental results show that the projection can effectively simplify the system state, and increase the efficiency of formal verification.

Due to the radical change in both Chinese and global economic environment, it is essential to develop a practical model to predict financial distress. The support vector machine (SVM), a new outstanding learning machine based on the statistical learning theory, embodying the principle of structural risk minimization instead of empirical risk minimization principle, is a promising method for such financial distress prediction. However, to some extent, the performance of single classifier depends on the sample's pattern characteristics and each single classifier has its own uncertainty. Using the ensemble methods to predict financial distress becomes a rising trend in this field. This research puts forward a SVM ensemble based on the Choquet integral for financial distress prediction in which Bagging algorithm is used to generate new training sets. The proposed ensemble method can be expressed as "Choquet + Bagging + SVMs". With real data from Chinese listed companies, an experiment is carried out to compare the performance of single classifiers with the proposed ensemble method. Empirical results indicate that the proposed ensemble of SVMs based on the Choquet integral for financial distress prediction has higher average accuracy and stability than single SVM classifiers.

In this paper, two uncertain linguistic Choquet aggregation operators called the induced uncertain linguistic hybrid generalized Shapley Choquet weighted averaging (IULHGSCWA) operator and the induced uncertain linguistic hybrid generalized Shapley Choquet geometric mean (IULHGSCGM) operator are defined. To reduce the complexity of solving a fuzzy measure, the induced uncertain linguistic hybrid 2-additive generalized Shapley Choquet weighted averaging (IULHAGSCWA) operator and the induced uncertain linguistic hybrid 2-additive generalized Shapley Choquet geometric mean (IULHAGSCGM) operator are presented. When the weight information is incompletely known, models for the optimal fuzzy measures on an expert set, on an attribute set and on their ordered sets are built, respectively. Furthermore, an approach to multi-attribute group decision making under uncertain linguistic environment is developed, which can cope with the interdependent or correlative characteristics between elements. Finally, a practical application is selected to illustrate the validity and practicability of the developed procedure.

In this paper, a new approach is presented for solving multi-criteria group decision-making (MCGDM) problems, which is based on new arithmetic operations and the ranking rules of trapezoidal interval type-2 fuzzy numbers (IT2FNs). Firstly, the shortcomings of some existing arithmetic operations of trapezoidal IT2FNs are discussed along with their ranking methods, before some new arithmetic operations and ranking rules are proposed. Secondly, some new aggregation operators including the arithmetic averaging aggregation operator, the ordered weighted averaging aggregation operator and the hybrid weighted averaging aggregation operator for trapezoidal IT2FNs are also developed. Thirdly, a new approach for MCGDM problems is developed based on the proposed operators and ranking rules. Finally, an example is provided to illustrate the feasibility and validity of this new approach, and a comparison analysis referring to the same example is also presented.

The cloud model, which can implement uncertain transformation between a qualitative concept and its quantitative instantiations, has attracted great attention in multicriteria decision-making problems with linguistic information. This paper proposes some operations and a possibility degree of trapezium clouds, as well as several new aggregation operators: the trapezium cloud weighted arithmetic averaging operator, the trapezium cloud ordered weighted arithmetic averaging operator, and the trapezium cloud hybrid arithmetic operator. Moreover, a method with Atanassov's interval-valued intuitionistic linguistic numbers (AIVILNs) based on trapezium clouds is presented, which can provide solutions to multicriteria group decision-making problems with Atanassov's interval-valued intuitionistic linguistic information. In this method, AIVILNs are first converted into trapezium clouds and aggregated by trapezium cloud aggregation operators. Then, the ranking of alternatives is determined by the possibility degree matrix of trapezium clouds. Finally, an illustrative example confirming the validity and feasibility of the proposed method is given.

Fuzziness is inherent in decision data and decision making process. In this paper, interval-valued intuitionistic fuzzy set is used to capture fuzziness in multi-criteria decision making problems. The purpose of this paper is to develop a new method for solving multi-criteria decision making problem in interval-valued intuitionistic fuzzy environments. First, we introduce and discuss the concept of interval-valued intuitionistic fuzzy point operators. Using the interval-valued intuitionistic fuzzy point operators, we can reduce the degree of uncertainty of the elements in a universe corresponding to an interval-valued intuitionistic fuzzy set. Then, we define an evaluation function for the decision-making problem to measure the degrees to which alternatives satisfy and do not satisfy the decision-maker's requirement. Furthermore, a series of new score functions are defined for multi-criteria decision making problem based on the interval-valued intuitionistic fuzzy point operators and the evaluation function and their effectiveness and advantage are illustrated by examples.

In this paper, another important solution for fuzzy games, called the Banzhaf function, is investigated. First, a simplified expression for the Banzhaf function for games with fuzzy coalition is proposed. The relationship between this simplified expression and two kinds of particular Banzhaf functions are discussed in detail. Furthermore, in the framework of games with fuzzy coalitions, we give a definition of the potential function of fuzzy games, and prove that the marginal values of the potential function are the components of the Banzhaf function. Furthermore, a new axiomatic characterization of the Banzhaf function for games with fuzzy coalitions is provided.

The number of research studies on social tagging has increased rapidly in the past years, but few of them highlight the characteristics and research trends in social tagging. A set of 862 academic documents relating to social tagging and published from 2005 to 2011 was thus examined using bibliometric analysis as well as the social network analysis technique. The results show that social tagging, as a research area, develops rapidly and attracts an increasing number of new entrants. There are no key authors, publication sources, or research groups that dominate the research domain of social tagging. Research on social tagging appears to focus mainly on the following three aspects: (a) components and functions of social tagging (e.g., tags, tagging objects, and tagging network), (b) taggers' behaviors and interface design, and (c) tags' organization and usage in social tagging. The trend suggest that more researchers turn to the latter two integrated with human computer interface and information retrieval, although the first aspect is the fundamental one in social tagging. Also, more studies relating to social tagging pay attention to multimedia tagging objects and not only text tagging. Previous research on social tagging was limited to a few subject domains such as information science and computer science. As an interdisciplinary research area, social tagging is anticipated to attract more researchers from different disciplines. More practical applications, especially in high‐tech companies, is an encouraging research trend in social tagging.

In recent years, the Pearl River Delta (PRD) of China experienced serious saline water intrusion in the dry season, leading to water supply crisis. A challenging issue in water supply management is that there is not enough saline water intrusion information for decision making due to limited site-monitoring. Therefore, the objectives of this study is to investigate the spectral characteristics of seawater with different chlorosity, the most fundamental step in remote sensing application. Experiments in laboratory were performed to obtain spectral characteristics of saline water by spectroradiometer. The primary results show that, (1) reflectance of seawater was small between 400 and 850 nm, ranging from 0.01 to 0.14; (2) reflectance spectra of seawater was unique because spectral curves changed slightly in the ranges of 400-700 nm and 850-930 nm, but fluctuated observably in 700-850 nm, i.e., suddenly declined from 700 to 750 nm and then formed a narrow peak (750-770 nm) and a wide peak (770-850 nm); and (3) spectral reflectance increased with the increasing of chlorosity.

Internetware literally means "the software paradigm for the Internet". The Problem Frames (PF) approach can be used to model Internetware entities. Internetware entities are running on the open, dynamic and uncertainty environment of Internet, which makes the issue of non-functional requirements even more important. However, the PF approach doesn't pay enough attention to deal with non-functional requirements. This paper proposes an approach for selecting implementation strategies of non-functional requirements. Finally, an example shows the feasibility of our approach.

The Netherlands Forensic Institute and the Institute for Forensic Science in Shanghai are in search of a signature verification system that can be implemented in forensic casework and research to objectify results. We want to bridge the gap between recent technological developments and forensic casework. In collaboration with the German Research Center for Artificial Intelligence we have organized a signature verification competition on datasets with two scripts (Dutch and Chinese) in which we asked to compare questioned signatures against a set of reference signatures. We have received 12 systems from 5 institutes and performed experiments on online and offline Dutch and Chinese signatures. For evaluation, we applied methods used by Forensic Handwriting Examiners (FHEs) to assess the value of the evidence, i.e., we took the likelihood ratios more into account than in previous competitions. The data set was quite challenging and the results are very interesting.

Chlorophyll a (Chl-a) is an indicator of water quality, especially of eutrophic state. Remote sensing has been used to mapping Chl-a in lake, large river, estuary and ocean, due to its andvantages such as to provide data logistically and economically. The aim of this paper was to use LandSat TM image and suitable algorithm to estimate and map Chl-a concentration in reservoir Dajingshan, an important source for drinking water supply in Zhuhai city, China. After comparing several simple linear regression algorithms, a most suitable algorithm using brightness value of bands 1 to 4, was adopted. The results showed that the modeled chl-a concentration in November 23th, 2005 had reasonable accuracy, with a mean absolute error of 0.002 mg/L when compared with the ground truth measurements. The chl-a concetration map showed that the reservior Dajingshan was in eutrophic conditions and the water could not be used as drinking water.

Yager (Fuzzy Sets, Syst 2003;137:59–69) extended the idea of order-induced aggregation to the Choquet aggregation and defined induced Choquet ordered averaging operator. In this paper, an induced intuitionistic fuzzy Choquet (IFC) integral operator is proposed for the multiple criteria decision making. Some of its properties are investigated. Furthermore, an induced generalized IFC integral operator is introduced. It is worth mentioning that most of the existing intuitionistic fuzzy aggregation operators are special cases of this induced aggregation operator. A decision procedure based on the proposed induced aggregation operator is developed for solving the multicriteria decision-making problem in which all the decision information is represented by intuitionistic fuzzy values. An illustrative example is given for demonstrating the applicability of the proposed decision procedure. © 2011 Wiley Periodicals, Inc.

Problem Frames (PF) approach is prospective for describing and analyzing software problems. Problem decomposition is fundamental for managing problem size and complexity in RE and ‘projection’ has been argued to be an effective technique for analyzing and decomposing complex problems in PF. However, problem analysis in PF approach is still an empirical, tedious, and subjective process, and it heavily depends on the analysts’ experiences. A suitable tool is highly demanded for providing guidance of the problem description and projection. This paper gives a supporting tool (named DPTool) for describing the problem and performing the problem projection.

Yager (Fuzzy Sets Syst 2003;137:59–69) extended the idea of order-induced aggregation to the Choquet aggregation and defined a more general type of Choquet integral operator called the induced Choquet ordered averaging (I-COA) operator, which take as their argument pairs, in which one component called order-inducing variable is used to induce an ordering over the second components called argument variable and then aggregated. The aim of this paper is to develop the I-COA operator. Some of its properties are investigated. We show its relationship to the induced-ordered weighted averaging operator. Finally, we provide some I-COA operators to aggregate fuzzy preference relations in group decision-making problems. © 2009 Wiley Periodicals, Inc.

In the Problem Frames (PF) approach there are five basic problem frames and some variants to them. When a problem is being analysed, it is initially matched against these frames. If the problem does not fit into the basic problem frames or their variants, then problem analysis is performed. It has been recognised that `projection' is an effective technique for analysing problems. That is, each sub-problem is considered as a projection of the main problem concerned only with the phenomena relevant to that sub-problem. The PF approach lacks a precise definition of problem projection and does not provide specific instructions on how to perform this projection. In this paper, we use the concept of projection from relational algebra and combine it with concepts from the PF and scenario-based approaches to present a conceptual model for conducting problem projection in requirements engineering. This model and ontology extend problem description at scenario level and support systematic derivation of sub-problems from scenarios. We also provide a detailed process description for performing projection for problem analysis and present the utility of our approach with a case study.

In the Internet, there are a mass of malicious, fraudulent services in addition to the dynamics change and differences in service of quality. As a result, it is difficult for a client to fastly get high- quality services. In this paper, a service-composition framework based on the trust evolution and alliance has been proposed. It is different from traditional service workflow model. The trust relationship will be built after the trust evolution between services requester and provider. The trust services requester and provider are going to form an alliance through the relationship, so the malicious, fraudulent services will be excluded from the system and the service-composition will be carried out in a trust environment. On this basis, a strategy measuring the trust relationship among the services based on information entropy has been proposed, which can avoid the deficiencies of multi-dimensional trust indicators simply weighted in the previous studies. Theoretical analysis and experimental result show that the method proposed in this paper is more superior in credibility and security, comparing to the traditional services selection methods.