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Highlights • This paper applies a recent concept, namely the concept of stratification. • The dynamicity of the decision environment when a decision is made is considered. • The method mimics the brain's decision making process and has the potential for future applications in artificial intelligence. • The method will be useful to researchers who are applying different MCDM methods. Abstract Multiple Criteria Decision Making (MCDM) methods generally require the decision maker to evaluate alternatives with respect to decision criteria and also to assign importance weightings to the criteria. Then, based on the assigned weightings, the best alternative can be selected. However, after a decision is made it often happens that the decision maker becomes doubtful whether the right weightings have been assigned to the criteria given that a variety of eventualities may occur in the near future. The main aim of this paper is to address this concern and improve the application of MCDM methods by addressing possible fluctuations in the criteria weightings. The recently proposed concept of stratification (CST) is used in conjunction with MCDM methods to stratify the decision environment. The method is then applied to a supplier selection problem. The stratified MCDM (SMCDM) approach is in its early stages only and requires further research to reach its maturity. [ABSTRACT FROM AUTHOR]

Knowledge is the foundation of emergency decision-making (EDM), in which the experts from multi-fields express their knowledge with multi-granularity linguistic model to assistant decision-making. Thus, the paper proposed a new decision support methodology to generate decision-making knowledge. In this paper, the framework of decision knowledge generation in the EDM was introduced firstly. To generate decision-making knowledge accurately and objectively, two objective models, which can effectively determine the weights of criteria and experts respectively, were built based on the tacit knowledge hidden in the original information. Then, the personal knowledge, generated by combining the normalized decision knowledge and the weight vector of criteria, is further aggregated into the collective knowledge by means of aggregation operator. Finally, an illustrative example is presented to verify the application of the proposed methods, and relevant discussions prove the results obtained from the proposed decision support methodology can improve the scientificity and accuracy of the EDM. [ABSTRACT FROM AUTHOR]

In the sensing as a service paradigm, Internet of Things (IoT) Middleware platforms allow data consumers to retrieve the data they want without knowing the underlying technical details of IoT resources (i.e. sensors and data processing components). However, configuring an IoT middleware platform and retrieving data is a significant challenge for data consumers as it requires both technical knowledge and domain expertise. In this paper, we propose a knowledge driven approach called Context Aware Sensor Configuration Model (CASCOM) to simplify the process of configuring IoT middleware platforms, so the data consumers, specifically non-technical personnel, can easily retrieve the data they required. In this paper, we demonstrate how IoT resources can be described using semantics in such away that they can later be used to compose service work-flows. Such automated semantic-knowledge-based IoT resource composition approach advances the current research. We demonstrate the feasibility and the usability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized to any other middleware platform. [ABSTRACT FROM AUTHOR]

This paper introduces a knowledge-based system which grounds the detection of the user’s likes and dislikes on the topic structure of the conversation. The targeted study is set in a human-agent interaction with the aim to help the creation of dialogue strategies of an agent based on the user’s interests. In this paper, we first describe the system based on linguistic resources such as lexicons, dependency grammars and dialogue information provided by the dialogue system. Second, we explain how the system merges its outputs at the end of each topic sequence. Finally, we present an evaluation of both the linguistic rules and the merging process. The system enables a better identification of the target of the user’s likes and dislikes and provides a synthetic representation of the user’s interests. [ABSTRACT FROM AUTHOR]

Prior work introduced a gradient descent trained expert system that conceptually combines the learning capabilities of neural networks with the understandability and defensible logic of an expert system. This system was shown to be able to learn patterns from data and to perform decision-making at levels rivaling those reported by neural network systems. The principal limitation of the approach, though, was the necessity for the manual development of a rule-fact network (which is then trained using backpropagation). This paper proposes a technique for overcoming this significant limitation, as compared to neural networks. Specifically, this paper proposes the use of larger and denser-than-application need rule-fact networks which are trained, pruned, manually reviewed and then re-trained for use. Multiple types of networks are evaluated under multiple operating conditions and these results are presented and assessed. Based on these individual experimental condition assessments, the proposed technique is evaluated. The data presented shows that error rates as low as 3.9% (mean, 1.2% median) can be obtained, demonstrating the efficacy of this technique for many applications. [ABSTRACT FROM AUTHOR]

Abstract In a disaster response situation, Unmanned Aerial Vehicle (UAV) can be an effective medium for the assessment of damages/needs of affected regions through the collection of situational information in the form of texts, audio-visuals, images etc. Through field deployment in disaster response situations, it is challenging to determine the navigation route for a UAV to cover the appropriate shelter points within its allocated flight time due to the energy constraints. Besides, it is a very arduous task to predict the favorable weather conditions and data-rate through the actual deployment of UAVs in post-disaster situations. In this paper, a novel navigation technique for micro-jet has been designed based on the spatial data. The navigation technique has been thought of as a spatial decision problem which has further been solved systematically by means of Multi-Criteria Decision Making (MCDM) technique. A Multi-Criteria Evaluation Technique has been adopted for effective transformation of spatial and calibration parameters into meaningful decisions. The navigation path has been obtained dynamically based on the solution of the spatial decision problem. This decision problem utilizes the parameters likes wind speed, wind direction, remaining flight time, the distance between the neighboring Shelter Points, the volume of data to be transferred etc. From the simulations, it has been analyzed that the newly proposed navigation technique can significantly improve the flight time of micro-jet by consuming less energy. The proposed approach has a prolonged flight duration and consumes less energy compared to the previously proposed techniques i.e. MIN_ROUTE and ROUTE_PRIORITY for different variations of wind speed and wind direction. Two field trials at different venues have been conducted using a customized micro-jet (equipped with a Single Board Computer with 2.4 GHz Wi-Fi connectivity) to analyze, (a) optimal altitude for navigation route at which data transfer rate would be maximum, (b) performance in terms of flight path calibration parameters of micro-jet, data transfer rate and the volume of data transfer while implementing the navigation route generated during simulation at the optimal altitude for a particular wind speed and wind direction. It has been observed that (1) in the initial deployment, at an altitude of 55 meters, for the wind speed 3 m/s and wind direction towards south the highest data rate of 154.51 KBps has been achieved, (2) in the subsequent field trial, during navigation the highest data rate of 942.08 KBps, at 50–55 meters altitude at wind speed of 1.66 m/s and with the wind direction towards north-east, has been achieved. [ABSTRACT FROM AUTHOR]

Abstract The fuzzy analytic hierarchy process (AHP) is a widely applied multiple-criteria decision-making (MCDM) technique, making it possible to tackle vagueness and uncertainty arising from decision makers, especially in a pairwise comparison process. Indeed, as the human brain reasons with uncertain rather than precise information, pairwise comparisons may involve some degree of inconsistency, which must be correctly managed to guarantee a coherent result/ranking. Several consistency indexes for fuzzy pairwise comparison matrices (FPCMs) have been proposed in the literature. However, some scholars argue that most of these fail to be axiomatically grounded, which may lead to misleading results. To overcome this lack of an axiomatically grounded index, a new index is proposed in this paper, referred to as the knowledge-based consistency index (KCI). A comparative study of the proposed index with an existing one is carried out, and the results show that KCI contributes to substantially reducing the computation time. In addition, the different fuzzy weights derived from the initial FPCM (for KCI computation purposes) can also be employed to find a crisp set of weights that corresponds to an optimal solution to the MCDM problem according to the decision maker's viewpoint and expertise. Graphical abstract Highlights • A new consistency index (KCI) for fuzzy pairwise comparison matrix is proposed. • A comparison study shows that KCI performs faster than Ohnishi's index. • An optimal solution of the MCDM problem is computed using fuzzy AHP. • Decision makers can save a substantial amount of time by adopting KCI. [ABSTRACT FROM AUTHOR]

Abstract A perfect combination of fact-based and value-based judgments is what multiple criteria decision-making (MCDM) issues pursue. Tolerability constraints, an important part of value-based judgments, are easily observable and have drawn attention in theory. However, their practical applications in the MCDM process are still in their infancy. This paper aims to fill the gap between theory and application. A framework for tolerability constraints in the context of non-independent criteria is proposed and operated for the first time. In this framework, global tolerability constraints on the overall decision-making process and local constraints on a single criterion are considered at the same time. Furthermore, mandatory/ sufficient requirements are regarded as different aspects of tolerability constraints. Two cases are given to illustrate the application of the proposed framework. In short, the proposed framework deepens the understanding of MCDM issues and extends its application, especially with respect to tolerability requirements. [ABSTRACT FROM AUTHOR]

Abstract In consensus-based multiple attribute group decision making (MAGDM) problems, it is frequent that some experts exhibit non-cooperative behaviors owing to the different areas to which they may belong and the different (sometimes conflicting) interests they might present. This may adversely affect the overall efficiency of the consensus reaching process, especially when some uncooperative behaviors by experts arise. To this end, this paper develops a novel consensus framework based on social network analysis (SNA) to deal with non-cooperative behaviors. In the proposed SNA-based consensus framework, a trust propagation and aggregation mechanism to yield experts' weights from the social trust network is presented, and the obtained weights of experts are then integrated into the consensus-based MAGDM framework. Meanwhile, a non-cooperative behavior analysis module is designed to analyze the behaviors of experts. Based on the results of such analysis during the consensus process, each expert can express and modify the trust values pertaining other experts in the social trust network. As a result, both the social trust network and the weights of experts derived from it are dynamically updated in parallel. A simulation and comparison study is presented to demonstrate the efficiency of the SNA-based consensus framework for coping with non-cooperative behaviors. [ABSTRACT FROM AUTHOR]

Abstract Visual tracking algorithms based on structured output support vector machine (SOSVM) have demonstrated excellent performance. However, sampling methods and optimization strategies of SOSVM undesirably increase the computational overloads, which hinder real-time application of these algorithms. Moreover, due to the lack of high-dimensional features and dense training samples, SOSVM-based algorithms are unstable to deal with various challenging scenarios, such as occlusions and scale variations. Recently, visual tracking algorithms based on discriminative correlation filters (DCF), especially the combination of DCF and features from deep convolutional neural networks (CNN), have been successfully applied to visual tracking, and attains surprisingly good performance on recent benchmarks. The success is mainly attributed to two aspects: the circular correlation properties of DCF and the powerful representation capabilities of CNN features. Nevertheless, compared with SOSVM, DCF-based algorithms are restricted to simple ridge regression which has a weaker discriminative ability. In this paper, a novel circular and structural operator tracker (CSOT) is proposed for high performance visual tracking, it not only possesses the powerful discriminative capability of SOSVM but also efficiently inherits the superior computational efficiency of DCF. Based on the proposed circular and structural operators, a set of primal confidence score maps can be obtained by circular correlating feature maps with their corresponding structural correlation filters. Furthermore, an implicit interpolation is applied to convert the multi-resolution feature maps to the continuous domain and make all primal confidence score maps have the same spatial resolution. Then, we exploit an efficient ensemble post-processor based on relative entropy, which can coalesce primal confidence score maps and create an optimal confidence score map for more accurate localization. The target is localized on the peak of the optimal confidence score map. Besides, we introduce a collaborative optimization strategy to update circular and structural operators by iteratively training structural correlation filters, which significantly reduces computational complexity and improves robustness. Experimental results demonstrate that our approach achieves state-of-the-art performance in mean AUC scores of 71.5% and 69.4% on the OTB2013 and OTB2015 benchmarks respectively, and obtains a third-best expected average overlap (EAO) score of 29.8% on the VOT2017 benchmark. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a collection of refinement operators for directed labeled graphs (DLGs), and a family of distance and similarity measures based on them. We build upon previous work on refinement operators for other representations such as feature terms and description logic models. Specifically, we present eight refinement operators for DLGs, which will allow for the adaptation of three similarity measures to DLGs: the anti-unification-based, S λ , the property-based, S π , and the weighted property-based, S wπ , similarities. We evaluate the resulting measures empirically, comparing them to existing similarity measures for structured data in the context of instance-based machine learning. [ABSTRACT FROM AUTHOR]

Abstract Glaucoma is one of the leading causes of blindness in the world and there is no cure for it yet. But it is very meaningful to detect it early as earlier detection makes it possible to stop further loss of visions. Although deep learning models have proved their advantages in natural image analysis, they usually rely on large datasets to learn to extract hidden features, thus limiting its application in medical areas where data is hard to get. Consequently, it is meaningful and challenging to design a deep learning model for disease diagnosis with relatively fewer data. In this paper, we study how to use deep learning model to combine domain knowledge with retinal fundus images for automatic glaucoma diagnosis. The domain knowledge includes measures important for glaucoma diagnosis and important region of the image which contains much information. To make full use of this domain knowledge and extract hidden features from image simultaneously, we design a multi-branch neural network (MB-NN) model with methods to automatically extract important areas of images and obtain domain knowledge features. We evaluate the effectiveness of the proposed model on real datasets and achieve an accuracy of 0.9151, sensitivity of 0.9233, and specificity of 0.9090, which is better than the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Knowledge engineering often involves using the opinions of experts, and very frequently of a group of experts. Experts often cooperate in creating a knowledge base that uses fuzzy inference rules. On the one hand, this may lead to generating a higher quality knowledge base. But on the other hand, it may result in irregularities, for example, if one of the experts dominates the others. This paper addresses a research problem related to creating a method for automatic verification of inference rules. It would allow one to detect inconsistencies between the rules that have been developed and the actual knowledge of the group of experts. A method of multi-criteria group evaluation of variants under uncertainty was used for this purpose. This method utilises experts’ opinions on the importance of the premises of inference rules. They are expressed in terms of multiple criteria in the form of both numerical and linguistic assessments. Experts define the conclusions of rules as so-called half-marks in order to increase the method’s flexibility. Automatic rules are generated in a similar way. Such an approach makes it possible to automatically determine the final conclusions of inference rules. They can be regarded as consistent both with the opinions of a group of experts and with automatically generated rules. This paper presents the use of the method for verifying the rules of an expert system that is aimed to evaluate the effectiveness of a passenger and baggage screening system at an airport. This method allows one to detect simple logical errors that are made when experts are establishing rules as well as inconsistencies between the rules that have been developed and the experts’ actual knowledge. [ABSTRACT FROM AUTHOR]

Research on Engineering Knowledge Management (EKM) has identified challenges with the systematic source of engineering knowledge for the design process optimisation. In this context, Knowledge-Based Engineering (KBE) is acknowledged as a key area within the EKM field and designated by the research community as a potential solution to carry out the effective capture and reuse of expert knowledge. However, papers on KBE for knowledge sourcing are not abundant in the literature and they are also dispersed. From this perspective, this research is an effort to further consolidate the learning gained on industrial practice on how engineering knowledge can be effectively sourced. This is achieved by realising a research survey, where using the resulting insights KBE practice reaching aerospace engineering offices shall be more efficiently delivered through fast and accurate knowledge extraction and encoding into usable methods and tools. The research findings provided by literature survey confirmed the existence of a research gap on knowledge sourcing; and more precisely they underlined the need for an extended KBE development process which integrates Artificial Intelligence (AI) tools and expert intervention to systematically manage the knowledge (using the KM methods and tools) efficiently captured and modelled (employing AI algorithms and expert involvement). Therefore, this paper concludes that there is a need for further research on the knowledge sourcing KBE aspect and presents the integration of KBE systems and AI implementations as a potential solution to develop the extended KBE development process requested by the industry. [ABSTRACT FROM AUTHOR]

In this paper, the use of relevance feedback with fingerprint identification systems is investigated. Two key limitations in current systems are addressed. Firstly, performance in current systems is highly dependent upon the fingerprint features selected for identification and the accuracy of the underlying pattern matching algorithm. Secondly, there is no effective mechanism to improve future queries through knowledge captured from the users, who are often experienced fingerprint examiners. Relevance feedback, a human computer interaction technique to capture and re-use knowledge of a user, has been studied extensively in text-based document retrieval systems and content-based image retrieval systems, but to date examples of its application to fingerprint identification systems are rare. By exploiting relevance feedback, this paper presents a user-centric and adaptive framework that allows tacit knowledge of fingerprint examiners to be captured and re-used to enhance their future decisions. The outcome is a knowledge-based Decision Support System (DSS) that provides the examiner with both intuitive visualization displays to analyze the relationships between images in the fingerprint database and relevance feedback facility to produce a persistent and personalized semantic space overlay. This serves a long term memory that can be updated to reflect the knowledge captured from the user. Empirical experiments confirmed the ability of this approach to improve the accuracy of fingerprint identification queries when compared to the static data processing architecture of current systems. [ABSTRACT FROM AUTHOR]

Enterprises are focusing more and more on knowledge issues for global product development. This paper describes knowledge evolution processes in product development activities and proposes a knowledge evaluation method in product lifecycle design. The paper also theoretically analyzes the evaluation model and illustrates how knowledge values can be assessed by case study. The case study shows how knowledge values calculated by the model can provide suggestions about which knowledge to choose and what to do next. The knowledge evaluation model serves as a useful tool for managing knowledge in product lifecycle design and support. [ABSTRACT FROM AUTHOR]

Neighbor-based collaborative ranking algorithms exploit users’ pairwise preferences to predict how they will rank items. Current neighbor-based algorithms lie in the category of user-based recommendation methods: they calculate users’ similarities over pairwise preferences, estimate the unknown pairwise preferences, and finally, infer the ranking of items for the target user. However, it still is an open question how to adapt item-based recommendation for neighbor-based collaborative ranking. The more specific question is how to calculate items’ similarities in a pairwise preference dataset, and how these similarities can be employed to infer the total ranking of items for the target user. This paper presents a novel recommendation approach, PreNIt, that exploits preference networks for item-based collaborative ranking. PreNIt models the users’ pairwise preferences as two novel bipartite networks with labeled edges. These labeled edges enable us to model the choice context in which items are preferred/not preferred by the user. Once the networks are constructed, PreNIt finds the transitive similarities of items using a new personalized ranking algorithm in graphs with labeled edges. Experimental results shows the significant outperformance of PreNIt over the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Cyber Physical System(CPS) allows to collect different sensor and alarm data from large number of facilities in industrial plants. Failure and faulty diagnosis is one of the most complicated and dynamic problems in the industrial plant management since most of failures are extremely ambiguous which needs to be solved based on an expert’s experience. This makes the solutions very subjective and requires too much time, efforts and monetary investment. In this paper, we are proposing new failure detection approach with machine learning and human expertise by using alarm data. As the first step of developing this new method, we collected several types of alarm data that detected functional failure in Hyundai Steel factory. We analyzed and processed the alarm data with 35 domain experts. Based on the data, we propose a knowledge based system which is Ripple Down Rule-based. This system acquires knowledge by machine learning which is maintained by human experts. The evaluation results showed that the proposed failure detection framework can reduce the time of human expertise acquisition and the cost of solving over-generalization and over-fitting problems by using machine learning techniques. [ABSTRACT FROM AUTHOR]

The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: “What is the KnoSys community interested in?” and “How does such interest change over time?” are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions. [ABSTRACT FROM AUTHOR]

This paper presents an unsupervised approach to solve semantic ambiguity based on the integration of the Personalized PageRank algorithm with word-sense frequency information. Natural Language tasks such as Machine Translation or Recommender Systems are likely to be enriched by our approach, which includes semantic information that obtains the appropriate word-sense via support from two sources: a multidimensional network that includes a set of different resources (i.e. WordNet, WordNet Domains, WordNet Affect, SUMO and Semantic Classes); and the information provided by word-sense frequencies and word-sense collocation from the SemCor Corpus. Our series of results were analyzed and compared against the results of several renowned studies using SensEval-2, SensEval-3 and SemEval-2013 datasets. After conducting several experiments, our procedure produced the best results in the unsupervised procedure category taking SensEval campaigns rankings as reference. [ABSTRACT FROM AUTHOR]

Object tracking has been wildly used in security monitoring, traffic control, medical imaging and other fields. Conventional algorithms design local and holistic appearance model for effectively tracking. However, the performance of algorithms decreases in the complex scenes, including deformation and background cluster, partial or full occlusion and so on. In this paper, an object-tracking algorithm via a cooperative appearance model is proposed. Considering the visual characteristics of human eyes, we propose the impact region with different impact factors. The impact regions are defined as the regions which play different effects in making decision. The pixels in the regions with different distance from the target center will have different importance. We divide the impact regions into the significant impact region, the sub-impact region, and the non-impact region. The cooperative appearance model uses local collaborative representation to rectify holistic representation with impact regions. In local representation, positive and negative dictionary are derived from the candidates of video frames. The candidates are segmented into non-overlapping sub-blocks, and the sub-block responses of each candidate are obtained based on a collaborative dictionary. In holistic representation, the candidates are represented sparsely to obtain the total reconstruction error. The tracking result is decided by combing the sub-block responses in local representation and the reconstruction error in holistic representation with impact regions. The experimental results show that the proposed algorithm has performed well on deformation and illumination variation, partial or full occlusion, scale variation and background cluster compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Flexible job shop scheduling problem (FJSP) is quite a difficult combinatorial model. Various metaheuristic algorithms are used to find a local or global optimum solution for this problem. Among these algorithms, variable neighborhood search (VNS) is a capable one and makes use of a systematic change of neighborhood structure for evading local optimum. The search process for finding a local or global optimum solution by VNS is totally random. This is one of the weaknesses of this algorithm. To remedy this weakness of VNS, this paper combines VNS algorithm with a knowledge module and proposes knowledge-based VNS (KBVNS). In KBVNS, the VNS part searches the solution space to find good solutions and knowledge module extracts the knowledge of good solution and feed it back to the algorithm. This would make the search process more efficient. Computational results of the paper on different size test problems prove the efficiency of our algorithm for FJS problem. [Copyright &y& Elsevier]

Abstract: In recent years, product knowledge has played increasingly significant roles in new product development process especially in the development of One-of-a-Kind products. Although knowledge-based systems (KBSs) have been proposed to support product development activities and new knowledge modelling methodologies have been developed, they are still far from complete. This area has become attractive to many researchers and as a result, many new knowledge-based systems, methods and tools have been developed. However, to the best of our knowledge, knowledge-based systems for product development have not been systematically reviewed, compared and summarized. This paper provides a comprehensive review on the recent development of KBS, methods and tools in supporting rapid product development. In the paper, the relevant technologies for modelling, managing and representing knowledge are investigated and reviewed systematically for better understanding their characteristics. The focus is placed on knowledge-based systems that support product development, and how product knowledge is identified, captured, represented and reused during the processes of One-of-a-Kind product development. The limitations and the future trend of KBS are presented in terms of how they can help One-of-a-Kind Production (OKP) companies. [Copyright &y& Elsevier]

Abstract: The isomorphic relationship between indiscernibility relation in rough set theory and nominal scale in formal concept analysis was studied in this paper, upon which the conceptual elements were constructed as the basic units of internal elements in a knowledge-based complex system. The nature of the association between conceptual elements was studied in this paper, where the definition and properties of association intensity were provided. Furthermore, concept granules were constructed by studying the mapping between conceptual elements. The general forms of expansion and aggregation of concept granules were also given. The theories in the study laid the foundation for evolutionary analysis of knowledge-based complex system, and offered a referential model to the studies on knowledge evolution of ontology engineering. [Copyright &y& Elsevier]

Abstract: Fuzzy rough set is a generalization of crisp rough set to deal with data sets with real value attributes. A primary use of fuzzy rough set theory is to perform attribute reduction for decision systems with numerical conditional attribute values and crisp (symbolic) decision attributes. In this paper we define inconsistent fuzzy decision system and their reductions, and develop discernibility matrix-based algorithms to find reducts. Finally, two heuristic algorithms are developed and comparison study is provided with the existing algorithms of attribute reduction with fuzzy rough sets. The proposed method in this paper can deal with decision systems with numerical conditional attribute values and fuzzy decision attributes rather than crisp ones. Experimental results imply that our algorithm of attribute reduction with general fuzzy rough sets is feasible and valid. [Copyright &y& Elsevier]

Abstract: Geometrical product specification and verification (GPS) matrix system is a universal tool for expressing geometrical requirements on product design drawings. It benefits product designers through providing detailed description of functional requirements for geometrical products, and through referring to corresponding manufacturing and verification processes. In order to overcome current implementation problems highlighted in this paper, a GPS knowledge base and a corresponding innovative inference mechanism have been researched, which led to the development of an integrated GPS knowledge-based system to facilitate rapid and flexible manufacturing requirements. This paper starts with a brief introduction of GPS, GPS application problems and the project background. It then moves on to demonstrate a unified knowledge acquisition and representation mechanism based on the category theory (CT) with five selected examples of this project. The paper concludes with a discussion on the future works for this project. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the problem of semantics-based temporal expert finding, which means identifying a person with given expertise for different time periods. For example, many real world applications like reviewer matching for papers and finding hot topics in newswire articles need to consider time dynamics. Intuitively there will be different reviewers and reporters for different topics during different time periods. Traditional approaches used graph-based link structure by using keywords based matching and ignored semantic information, while topic modeling considered semantics-based information without conferences influence (richer text semantics and relationships between authors) and time information simultaneously. Consequently they result in not finding appropriate experts for different time periods. We propose a novel Temporal-Expert-Topic (TET) approach based on Semantics and Temporal Information based Expert Search (STMS) for temporal expert finding, which simultaneously models conferences influence and time information. Consequently, topics (semantically related probabilistic clusters of words) occurrence and correlations change over time, while the meaning of a particular topic almost remains unchanged. By using Bayes Theorem we can obtain topically related experts for different time periods and show how experts’ interests and relationships change over time. Experimental results on scientific literature dataset show that the proposed generalized time topic modeling approach significantly outperformed the non-generalized time topic modeling approaches, due to simultaneously capturing conferences influence with time information. [Copyright &y& Elsevier]

Abstract: In view of the particularity of the value domains of mapping functions in soft sets, this paper presents data analysis approaches of soft sets under incomplete information. For standard soft sets, the decision value of an object with incomplete information is calculated by weighted-average of all possible choice values of the object, and the weight of each possible choice value is decided by the distribution of other objects. For fuzzy soft sets, incomplete data will be predicted based on the method of average-probability. Results of comparison show that comparing to other approaches for dealing with incomplete data, these approaches presented in this paper are preferable for reflecting actual states of incomplete data in soft sets. At last, an example is provided to illuminate the practicability and validity of the data analysis approach of soft sets under incomplete information. [Copyright &y& Elsevier]

Abstract: This paper describes a new knowledge acquisition method using a generic design environment where context-sensitive knowledge is used to build specific DSS for rural business. Although standard knowledge acquisition methods have been applied in rural business applications, uptake remains low and familiar weaknesses such as obsolescence and brittleness apply. We describe a decision support system (DSS) building environment where contextual factors relevant to the end users are directly taken into consideration. This “end user enabled design environment” (EUEDE) engages both domain experts in creating an expert knowledge base and business operators/end users (such as farmers) in using this knowledge for building their specific DSS. We document the knowledge organisation for the problem domain, namely a dairy industry application. This development involved a case-study research approach used to explore dairy operational knowledge. In this system end users can tailor their decision-making requirements using their own judgement to build specific DSSs. In a specific end user’s farming context, each specific DSS provides expert suggestions to assist farmers in improving their farming practice. The paper also shows the environment’s generic capability. [Copyright &y& Elsevier]

Abstract: The notion of knowledge artifact has rapidly gained popularity in the fields of general knowledge management and more recently knowledge-based systems. The main goal on this paper is to propose and discuss a methodology for the design and implementation of knowledge-based systems founded on knowledge artifacts. We advocate that the systems built according to this methodology can be effective to convey the flow of knowledge between different communities of practice. Our methodology has been developed from the ground up, i.e. we have built some concrete systems based on the abstract notion of knowledge artifact and synthesized our methodology based on reflections upon our experiences building these systems. In this paper, we also describe the most relevant systems we have built and how they have guided us to the synthesis of our proposed methodology. [Copyright &y& Elsevier]

Abstract: Constructing accurate classifier based on association rules is an important and challenging task in data mining and knowledge discovery. In this paper, a novel combination strategy for multi-class classification (CSMC) based on multiple rules is proposed. In CSMC, rules are regarded as classification experts, after the calculation of the basic probability assignments (bpa) and evidence weights, Yang’s rule of combination is employed to combine the distinct evidence bodies to realize an aggregate classification. A numerical example is shown to highlight the procedure of the proposed method at the end of this paper. The comparison with popular methods like CBA, C4.5, RIPPER and MCAR indicates that CSMC is a competitive method for classification based on association rule. [Copyright &y& Elsevier]

Abstract: The paper describes an application of artificial neural networks (ANN) for natural language text reasoning. The task of knowledge discovery in text from a database, represented with a database file consisting of sentences with similar meanings but different lexico-grammatical patterns, was solved with ANNs which recognize the meaning of the text using training files with limited dictionary. The paper features recognition algorithms of text meaning from a selected source using 3-layer ANNs. Tests of the new method have also been described. [Copyright &y& Elsevier]

Abstract: This paper describes a system which incorporates natural language technologies, database manipulation and educational theories in order to offer learners a Negotiated Learner Model, for integration into an Intelligent Tutoring System. The system presents the learner with their learner model, offering them the opportunity to compare their own beliefs regarding their capabilities with those inferred by the system. A conversational agent, or “chatbot” has been developed to allow the learner to negotiate over the representations held about them using natural language. The system aims to support the metacognitive goals of self-assessment and reflection, which are increasingly seen as key to learning and are being incorporated into UK educational policy. The paper describes the design of the system, and reports a user trial, in which the chatbot was found to support users in increasing the accuracy of their self-assessments, and in reducing the number of discrepancies between system and user beliefs in the learner model. Some lessons learned in the development have been highlighted and future research and experimentation directions are outlined. [Copyright &y& Elsevier]

Abstract: The inference engine (IE) is a crucial component of expert systems (ES). As the management environment has become rapidly digitalized with the advent of the Internet, the traditional IE is now facing a severe criticism – i.e., that it cannot effectively provide an agile, knowledge-based decision support suitable for a wide variety of problems. The objective of this paper is to propose a new type of ES called IMIXAO (Integer-Matrix-driven Inference based on an eXtended AND–OR graph), with an integer-matrix-driven IE based on an extended AND–OR graph framework. Traditionally, the AND–OR graph is used for making inferences in the ES area. Theoretical backgrounds of IMIXAO are presented in this paper, focusing on what an extended AND–OR graph is in terms of inference. The experiment conducted in this study, with an illustrative example of dermatological diseases classification, revealed that the proposed IMIXAO can make precise and agile inferences in a complex situation. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach for inducing decision trees based on Variable Precision Rough Set Model. The presented approach is aimed at handling uncertain information during the process of inducing decision trees and generalizes the rough set based approach to decision tree construction by allowing some extent misclassification when classifying objects. In the paper, two concepts, i.e. variable precision explicit region, variable precision implicit region, and the process for inducing decision trees are introduced. The authors discuss the differences between the rough set based approaches and the fundamental entropy based method. The comparison between the presented approach and the rough set based approach and the fundamental entropy based method on some data sets from the UCI Machine Learning Repository is also reported. [Copyright &y& Elsevier]

Motivated by the recent effort on scenario-based context question answering (QA), this paper investigates the role of discourse processing and its implication on query expansion for a sequence of questions. Our view is that a question sequence is not random, but rather follows a coherent manner to serve some information goals. Therefore, this sequence of questions can be considered as a mini discourse with some characteristics of discourse cohesion. Understanding such a discourse will help QA systems better interpret questions and retrieve answers. Thus, we examine three models driven by Centering Theory for discourse processing: a reference model that resolves pronoun references for each question, a forward model that makes use of the forward looking centers from previous questions, and a transition model that takes into account the transition state between adjacent questions. Our empirical results indicate that more sophisticated processing based on discourse transitions and centers can significantly improve the performance of document retrieval compared to models that only resolve references. This paper provides a systematic evaluation of these models and discusses their potentials and limitations in processing coherent context questions. [Copyright &y& Elsevier]

Purpose: This paper proposes a real-time knowledge support framework for the development of an RFID-multi-agent based process knowledge-based system which has the ability to solve dynamic logistics process management problems. Design/methodology/approach: The proposed system is developed with “real-time process management” capability which automatically identifies current process status, performs the process logic checking/reasoning, and, provides process knowledge support to staff members when they are tackling logistics activity problems. The unique feature of this on-line knowledge-based system, which enables it to enhance the performance of logistics organizations, is a process management engine incorporating radio-frequency identification (RFID) and multi-agent (MA) technologies. Findings: The capability of the proposed system is demonstrated through an application case study in Eastern Worldwide Company Limited. The result reveals that both performance of operations and the utilization of resources have improved significantly. Originality/value: The proposed system is a novel approach which leverages logistics performance and facilitates the creation of a learning organization through the provision of real-time knowledge support for those who handle logistics operations. Article type: Research Paper. [Copyright &y& Elsevier]

In order to build a heterogeneous multi-robot system that can be regarded as a primitive prototype of a future symbiotic autonomous human-robot system, this paper presents a knowledge model-based heterogeneous multi-robot system implemented by a software platform. With using frame-based knowledge representation, a knowledge model is constructed to describe the features of heterogeneous robots as well as their behaviors according to human requests. The required knowledge for constructing a heterogeneous multi-robot system can be therefore integrated in a single model and shared by robots and users. Based on the knowledge model, the heterogeneous multi-robot system is defined in the Software Platform for Agents and Knowledge Management (SPAK) by use of XML format. With the use of SPAK, the cooperative operation of heterogeneous multi-robot system can be flexibly carried out. The proposed system not only can integrate heterogeneous robots and various techniques for robots, but also can automatically perform human-robot interaction and plan robot behaviors taking into account different intelligence of robots corresponding to human requests. In this paper, an actual heterogeneous multi-robot system comprised by humanoid robots (Robovie, PINO), mobile robot (Scout) and entertainment robot dog (AIBO) is built and the effectiveness of the proposed system is verified by experiment. [Copyright &y& Elsevier]

Commitment management is a key issue in service provisioning in the context of virtual organisations (VOs). A service provider, which may be a single agent acting within an organisation, or the VO acting as a collective whole, manages particular resources, and commits these resources to meet specific goals. Commitments can be modelled as constraints on resources. Such constraints are often soft: they can be broken if necessary. The goal of the work described in this paper is to create an open, reusable commitment management service (CMS) based on Semantic Web standards. The chief requirement is that the CMS should be reusable in different domains, able to manage commitments over services described in a wide range of domain-specific service ontologies. This paper presents open Semantic Web representations for (1) expressing individual commitments as constraints over service descriptions, (2) capturing a set of commitments as a soft constraint satisfaction problem, and (3) representing and communicating the solution to a soft CSP. A reference implementation of a constraint solver able to operate on (1) and (2) to produce (3) is described, and its reuse is demonstrated in two distinct domains: e-commerce and e-response. [Copyright &y& Elsevier]

Abstract: The problem treated in this paper is the slow response of inference engines, especially in a multi-user environment. In this paper, we present a solution that caches all the possible answers to all the possible consultations, by means of translating the entire knowledge base into a set of HTML documents. We prove that the consumption of memory in this case is acceptable (depends linearly on the number of rules in the initial knowledge base), and that the translational algorithm has polynomial complexity. This solution outperforms any other possible solution, since in this case the infering time becomes equal to zero. [Copyright &y& Elsevier]

Yoga is a traditional Indian exercise. It specifies various body postures called asanas , practicing them is beneficial for the physical, mental, and spiritual well-being. To support the yoga practitioners, there is a need of an expert yoga asanas recognition system that can automatically analyze practitioner's postures and could provide suitable posture correction instructions. This paper proposes YogNet, a multi-person yoga expert system for 20 asanas using a two-stream deep spatiotemporal neural network architecture. The first stream utilizes a keypoint detection approach to detect the practitioner's pose, followed by the formation of bounding boxes across the subject. The model then applies time distributed convolutional neural networks (CNNs) to extract frame-wise postural features, followed by regularized long short-term memory (LSTM) networks to give temporal predictions. The second stream utilizes 3D-CNNs for spatiotemporal feature extraction from RGB videos. Finally, the scores of two streams are fused using multiple fusion techniques. A yoga asana recognition database (YAR) containing 1206 videos is collected using a single 2D web camera for 367 min with the help of 16 participants and contains four view variations i. e. front, back, left, and right sides. The proposed system is novel as this is the earliest two-stream deep learning-based system that can perform multi-person yoga asanas recognition and correction in realtime. Simulation result reveals that YogNet system achieved 77.29%, 89.29%, and 96.31% accuracies using pose stream, RGB stream, and via fusion of both streams, respectively. These results are impressive and sufficiently high for recommendation towards general adaption of the system. [ABSTRACT FROM AUTHOR]

In group decision making (GDM) with intuitionistic fuzzy preference relations (IFPRs), the consistency and consensus are two key issues. This paper develops a novel method for checking and improving the consistency of individual IFPRs and the consensus among experts. To measure the consistency degree of IFPRs, a consistency index is introduced and then an acceptable consistency is defined. For an IFPR with unacceptable consistency, a mathematical programming approach is developed to improve its consistency. To evaluate the consensus degree among experts, a consensus measure is presented by the proximity degree between one expert and other experts. When several individual IFPRs are unacceptable consistent or consensus is unacceptable, a goal program is built to improve the consistency and consensus simultaneously. By the consistency and proximity degrees of individual IFPRs, experts’ objective weights are determined. Combining the experts’ subjective weights, the experts’ comprehensive weights are derived. Then, an intuitionistic fuzzy geometric weighted mean (IFGWM) operator is proposed to integrate individual IFPRs into a collective one. Moreover, an attractive property is proved that the collective IFPR is acceptable consistent if all individual IFPRs are acceptable consistent. Two examples are provided to illustrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

In subway systems, the automatic train operation (ATO) is gradually replacing manual driving for its high punctuality and parking accuracy. But the existing ATO systems have some drawbacks in riding comfort and energy-consumption compared with the manual driving by experienced drivers. To combine the advantages of ATO and manual driving, this paper proposes a Smart Train Operation (STO) approach based on the fusion of expert knowledge and data mining algorithms. First, we summarize the domain expert knowledge rules to ensure the safety and riding comfort. Then, we apply a regression algorithm named as CART (Classification And Regression Tree) and ensemble learning methods (i.e. Bagging and LSBoost) to obtain the valuable information from historical driving data, which are collected in the Beijing subway Yizhuang line. Besides, a heuristic train station parking algorithm (HSA) by using the positioning data storage in balises is proposed to realize precisely parking. By combing the expert knowledge, data mining algorithms and HSA, two comprehensive STO algorithms, i.e., STO B and STO L are developed for subway train operations. The proposed STO algorithms are tested by comparing both ATO and manual driving on a real-world case of the Beijing subway Yizhuang line. The results indicate that the developed STO approach is better than ATO in energy consumption and riding comfort, and it also outperforms manual driving in punctuality and parking accuracy. Finally, the flexibility of STO L and STO B is verified with extensive experiments by considering different kinds of disturbances in real-world applications. [ABSTRACT FROM AUTHOR]

Software requirements engineering is a critical discipline in the software development life cycle. The major problem in software development is the selection and prioritization of the requirements in order to develop a system of high quality. This research analyzes the issues associated with existing software requirement prioritization techniques. One of the major issues in software requirement prioritization is that the existing techniques handle only toy projects or software projects with very few requirements. The current techniques are not suitable for the prioritization of a large number of requirements in projects where requirements may grow to the hundreds or even thousands. The research paper proposes an expert system, called the Priority Handler (PHandler), for requirement prioritization. PHandler is based on the value-based intelligent requirement prioritization technique, neural network and analytical hierarchical process in order to make the requirement prioritization process scalable. The back-propagation neural network is used to predict the value of a requirement in order to reduce the extent of expert biases and make the PHandler efficient. Moreover, the analytical hierarchy process is applied on prioritized groups of requirements in order to enhance the scalability of the requirement prioritization process. [ABSTRACT FROM AUTHOR]

The final testing process ensures the quality of the products in the semiconductor manufacturing factory. Scheduling for the final testing process is crucial to the economic efficiency of production. In this paper, an effective knowledge-based multi-agent evolutionary algorithm (KMEA) is proposed for solving the semiconductor final testing scheduling problem (SFTSP). In the KMEA, each agent is represented by a solution, which is a combination of the operation sequence vector and the machine assignment vector. A hybrid initialization mechanism is proposed to balance the diversity and the quality of the initial agents. In each iteration of evolution, the agents evolve by mutual-learning and competition based on the model of agent lattice. Moreover, a knowledge base is employed to store the useful information during the search process. The knowledge base is used to generate new agents in the competition phase. The computational complexity of the KMEA is analyzed, and the influence of parameter setting is also investigated. Finally, numerical simulation and comparisons are provided to demonstrate the effectiveness and efficiency of the KMEA in solving the test instances. [ABSTRACT FROM AUTHOR]

Robust modelling is significant to deal with complex systems with uncertainties. This paper aims to develop a novel learning algorithm for training regularized local random weights networks (RWNs). The learner model, terms as RL-RWN, is built on regularized moving least squares method and generalizes the solution obtained from the standard least square technique. Simulations are carried out using two benchmark datasets, including Auto-MPG data and surface reconstruction data. Results demonstrate that our proposed RL-RWN outperforms the original RWN and radial basis function networks. [ABSTRACT FROM AUTHOR]

How to express an expert’s or decision maker’s preference for alternatives is an open issue. Consistent fuzzy preference relation (CFPR) is with big advantages to handle this problem due to it can be construed via a smaller number of pairwise comparisons and satisfies transitivity property. However, the CFPR is incapable of dealing with the cases involving uncertain and incomplete information. In this paper, a D numbers extended consistent fuzzy preference relation (D-CFPR) is proposed to overcome the weakness. The D-CFPR extends the classical CFPR by using a new model of expressing uncertain information called D numbers. The D-CFPR inherits the merits of classical CFPR and can totally reduce to the classical CFPR. This study can be integrated into our previous study about D-AHP (D numbers extended AHP) model to provide a systematic solution for multi-criteria decision making (MCDM). [ABSTRACT FROM AUTHOR]

An intelligent tutoring system plays vital role in education and its importance is constantly increasing, meanwhile the key challenge in the teaching learning process is assessing students’ learning efficiently. In this paper, a hybrid assessment based-on ACT-R cognitive learning theory, combining ontology knowledge map with skills is proposed. In order to assess how well students master knowledge structure, an ontology knowledge map is constructed to describe declarative knowledge; and in order to assess how well students master knowledge skills, a problem solving process is constructed to describe procedural knowledge based on ACT-R. Finally, a student’s mastery of knowledge is assessed through both the knowledge map and skills in the problem solving process, as well as auxiliary indicators like time usage, prior knowledge level, self-assessment, etc. This method is implemented in a geometric intelligent assessment system and is evaluated in a junior high school. Experiments show that the assessment results are consistent with students’ actual learning levels. The hybrid cognitive assessment method can not only obtain the score of students’ mastery of knowledge points and the structure through knowledge map, but also assess the learning skills in problem solving process through exercises quantitatively. [ABSTRACT FROM AUTHOR]

Cold start problem is challenging because no prior knowledge can be used in recommendation. To address this cold start scenario, rating elicitation is usually employed, which profiles cold user or item by acquiring ratings during an initial interview. However, how to elicit the most valuable ratings is still an open problem. Intuitively, category labels which indicate user preferences and item attributes are quite useful. For example, category information can be served as a guidance to generate a set of queries which can largely capture the interests of cold users, and thus appealing recommendation lists are more likely to be returned. Therefore, we exploit category labels as supervised information to select discriminative queries. Furthermore, by exploring the correlation between users and items, a dual regularization is developed to jointly select optimal representatives. As a consequent, a novel Dual Discriminative Selection (DualDS) framework for rating elicitation is proposed in this paper, by integrating discriminative selection with dual regularization. Experiments on two real-world datasets demonstrate the effectiveness of DualDS for cold start recommendation. [ABSTRACT FROM AUTHOR]

With the development of the Semantic Web (SW), the creation of ontologies to formally conceptualize our understanding of various domains has widely increased in number. However, the conceptual and terminological differences (a.k.a semantic heterogeneity problem ) between ontologies form a major limiting factor towards their use/reuse and full adoption in practical settings. A key solution to addressing this problem can be through identifying semantic correspondences between the entities (including concepts, relations, and instances) of heterogeneous ontologies, and consequently achieving interoperability between them. This process is also known as ontology alignment . The output of this process can be further exploited to merge ontologies into a single coherent ontology. Indeed, this is widely regarded as a crucial, yet difficult task, specifically when dealing with heavyweight ontologies that consist of hundreds of thousands of concepts. To address this issue, various ontology merging approaches have been proposed. These approaches can be classified into three categories: single-strategy-based approaches, multiple-strategy-based approaches, and approaches based on exploiting external semantic resources. In this paper, we first discuss the strengths and limitations of each of these approaches, and then present our framework for addressing the semantic heterogeneity problem through merging domain-specific ontologies based on multiple external semantic resources. The novelty of the proposed approach is mainly based on employing knowledge represented by multiple external resources (knowledge bases in our work) to make aggregated decisions on the semantic correspondences between the entities of heterogeneous ontologies. Other important issues that we attempt to tackle in the proposed framework are: (i) Identifying and handling inconsistency of semantic relations between the ontology concepts and, (ii) Handling the issue of missing background knowledge (such as concepts and instances) in the exploited knowledge bases by utilizing an integrated statistical and semantic technique. Additionally, the proposed solution soundly enriches the knowledge bases with missing background knowledge, and thus enables the reuse of the newly obtained knowledge in future ontology merging tasks. To validate our proposal, we tested the framework using the OAEI 2009 benchmark and compared the produced results with state-of-the-art syntactic and semantic based systems. In addition, we utilized the proposed techniques to merge three heavyweight ontologies from the environmental domain. [ABSTRACT FROM AUTHOR]