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This paper aims to design and develop a Distributed Virtual Geographic Environment (DVGE) system. A DVGE system is an Internet-based virtual 2D and 3D environment that provides users with a shared space and a collaborative platform for publishing multidimensional geo-data, and for simulating and analyzing complex geo-phenomena. Users logging into the system from different clients can share distributed geo-information resources, including geo-data and geo-models, and can complete collaborative tasks. Web service technology provides effective solutions for constructing DVGE systems because of its ability to support multi-platform, multi-architecture, and multi-program-language interoperability on the Internet, but also because of its ability to share programs, data, and software. This paper analyzes the characteristics, relevant technologies, and specifications of web services, such as grid services, Open Geo-data Interoperability Specifications (OpenGIS), and Geography Markup Languages (GML). The architecture and working mechanisms of the DVGE system based on web services are then elaborated. To demonstrate DVGE systems based on web services, we examine a case study of water pollution in Yangzhou City, Jiangsu Province, China, using a prototype DVGE system that is developed with Jbuilder9.0 and Java3D 1.0 packages, and the Weblogic platform 8.1.

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

This paper examines a decision-tree framework for instance-space decomposition. According to the framework, the original instance-space is hierarchically partitioned into multiple subspaces and a distinct classifier is assigned to each subspace. Subsequently, an unlabeled, previously-unseen instance is classified by employing the classifier that was assigned to the subspace to which the instance belongs. After describing the framework, the paper suggests a novel splitting-rule for the framework and presents an experimental study, which was conducted, to compare various implementations of the framework. The study indicates that using the novel splitting-rule, previously presented implementations of the framework, can be improved in terms of accuracy and computation time.

Many existing grid resource allocation and scheduling algorithms mainly focus on isolated layers of the grid architecture. The inflexibility of the strict layering structure results in an inefficient utilization of the grid resources. This paper takes a system view of the computational grid and aims to jointly optimize global QoS by adopting cross-layer design. Cross-layer design is based on information exchange and joint optimization among multiple grid layers. Parameters from different layers are provided to a cross-layer optimizer, which selects the values of the layer specific parameters maximizing joint global QoS. The objective of the paper is to jointly optimize the parameters of all layers in a decentralized optimization problem and decompose joint QoS optimization into three sub problems at fabric layer, collective layer and application layer. In simulation part, we compare the performance of the global joint QoS optimization approach with application layer local optimization and resource layer local optimization approach, respectively.

Because of the widespread increasing application of Web services and autonomic computing, self-adaptive software is an area gaining increasing importance. Control theory provides a theoretical foundation for self-adaptive software. In this paper, we propose the use of the supervisory control theory of discrete event dynamic systems (DEDS) to provide a rigorous foundation for designing software for reactive systems. This paper focuses in particular on design of software with an attractivity requirement. It studies this problem using the polynomial dynamic system (PDS) model of DEDS. A necessary and sufficient condition for software existence and two algorithms for such software design are presented.

The paper considers a wireless information system, wherein various pieces of information represented in XML are broadcast via wireless channels, and mobile clients access the broadcast stream using energy-restricted portable devices. In this paper, we propose a wireless XML streaming method designed to provide energy-efficient access to a wireless stream. We construct two hierarchical structures to represent the XML data and their index information, called the XML data tree and XML index tree, respectively. The wireless XML stream is generated by traversing these two structures with some replications. We design three data/index replication strategies (PP, TT, and TP) in the streaming method. We compare the proposed streaming method with a [email protected]?ve method called the (1,X) method both analytically and experimentally. Also, based on our analysis results, we determine the optimal method of replication.

Fusion of sensor and communication data currently can only be performed at a late processing stage after sensor and textual information are formulated as logical statements at appropriately high level of abstraction. Contrary to this it seems, the human mind integrates sensor and language signals seamlessly, before signals are understood, at pre-conceptual level. Learning of conceptual contents of the surrounding world depends on language and vice versa. The paper describes a mathematical technique for such integration. It combines fuzzy dynamic logic with dual cognitive-language models. The paper briefly discusses relationships between the proposed mathematical technique, working of the mind and applications to understanding-based search engines.

This paper defines two suites of metrics, which address static and dynamic aspects of component assembly. The static metrics measure complexity and criticality of component assembly, wherein complexity is measured using Component Packing Density and Component Interaction Density metrics. Further, four criticality conditions namely, Link, Bridge, Inheritance and Size criticalities have been identified and quantified. The complexity and criticality metrics are combined to form a Triangular Metric, which can be used to classify the type and nature of applications. Dynamic metrics are collected during the runtime of a complete application. Dynamic metrics are useful to identify super-component and to evaluate the degree of utilization of various components. In this paper both static and dynamic metrics are evaluated using Weyuker's set of properties. The result shows that the metrics provide a valid means to measure issues in component assembly. We relate our metrics suite with McCall's Quality Model and illustrate their impact on product quality and to the management of component-based product development.

This paper expands and consolidates the use of analogies in thermodynamics to explore concepts in the characterization of information systems. The analogy spans the range of information systems to include databases, knowledge bases and model bases. It includes but is not limited to pressure, expressiveness, temperature, tractability, degrees of order, systems of liquid-liquid equilibrium and disjunction in information-systems integration. By taking advantage of the isomorphism that exists between states of matter and states of information, we can understand new ways to characterize and measure information systems. This paper is the fourth in a series describing new aspects of ''infodynamics.''.

The goal of this paper is to investigate the relation between object-oriented design choices and defects in software systems, with focus on a real-time telecommunication domain. The design choices are measured using the widely accepted metrics suite proposed by Chidamber and Kemerer for object oriented languages [S.R. Chidamber, C.F. Kemerer, A metrics suite for object oriented design, IEEE Transactions on Software Engineering 20 (6) (1994) 476-493]. This paper reports the results of an extensive case study, which strongly reinforces earlier, mainly anecdotal, evidence that design aspects related to communication between classes can be used as indicators of the most defect-prone classes. Statistical models applicable for the non-normally distributed count data are used, such as Poisson regression, negative binomial regression, and zero-inflated negative binomial regression. The performances of the models are assessed using correlations, dispersion coefficients and Alberg diagrams. The zero-inflated negative binomial regression model based on response for a class shows the best overall ability to describe the variability of the number of defects in classes.

This paper presents an ontology-based holonic diagnostic system (OHDS) that combines the holonic paradigm with multi-agent system technology and ontology design, for the organization of unstructured biomedical research into structured disease information. We use ontologies as 'brain' for the holonic diagnostic system to enhance its ability to structure information in a meaningful way and share information fast. To integrate distributed heterogeneous knowledge available on the Web we use intelligent agents augmented by the mechanisms of the technology of fuzzy sets, which automatically structures the information in the adequate ontology template. Our vision of how this system implementation should be supported by a solid security shield that ensures the privacy and safety of medical information concludes the paper. udes the paper.

Traditional topic classification usually adopts the closed-world assumption that all the test topics have been seen in training. However, in open dynamic environments, the potential new topics may appear in testing due to the evolution of text data over time. Considering the uncertainty and multi-granularity of dynamic text data, such open topic classification needs to detect unseen topics by mining the boundary region continually, and incrementally update the previous models by knowledge accumulation. To address these challenge issues, this paper introduces a unified framework of three-way multi-granularity learning to open topic classification based on the fusion of three-way decision and granular computing. First, we propose the multilevel granular structure of tasks from the temporal-spatial multi-granularity perspective. Then, we construct an adaptive decision boundary and use the centroids and the corresponding radius to discover unknowns by the reject option. Subsequently, we further explore the unknown topics by three-way enhanced clustering and the uncertain instances will be re-investigated in the next stage. Besides, we design a built-in knowledge base represented as the centroid of each topic to store the topic knowledge. Finally, the experiments are conducted to compare the performances of proposed models and the efficiency of knowledge accumulation with classic models.

Aspect-level sentiment classification aims to obtain fine-grained sentiment polarities of different aspects in one sentence. Most existing approaches handle the classification by acquiring the importance of context words towards each given aspect individually, and ignore the benefits brought by aspect relations. Since the sentiment of one aspect can be deduced through their relationship according to other aspects, in this paper, we propose a novel relation construction multi-task learning network (RMN), which is the first attempt to extract aspect relations as an auxiliary classification task. RMN generates aspect representations through graph convolution networks with a semantic dependency graph and utilizes the bi-attention mechanism to capture the relevance between the aspect and the context. Unlike conventional multi-task learning methods that need extra datasets, we construct an auxiliary relation-level classification task that extracts aspect relations from the original dataset with shared parameters. Extensive experiments on five public datasets from SemEval 14, 15, 16 and MAMS show that our RMN improves about 0.09% to 0.8% on accuracy and about 0.04% to 1.19% on F1 score, compared to several comparative baselines.

Since multimodal imaging technology is able to provide multiple perspectives on the lesion, it has become increasingly important in clinical diagnosis and treatment planning. In this paper, a novel medical image fusion using gradient domain-guided filter random walk (GDGFRW) and side window filtering (SWF) in the framelet transform (FT) domain is presented. Firstly, FT is performed on the original multimodal source images to obtain the corresponding approximate and residual representations. Secondly, a novel model—GDGFRW, which combines the superiorities of both gradient domain guided filtering and random walks—is constructed to interpret the approximate sub-bands, while the residual sub-bands are fused by SWF. Finally, the fused approximate sub-bands and residual sub-bands undergo inverse FT to generate the final fused image. To verify its effectiveness, the proposed method was tested on different categories of multimodal medical image fusion issues, in more than 40 pairs of source images. The proposed method outperforms the current representative ones in terms of both subjective visual performance and objective assessment.

In this paper, the issues of both fault estimation and accommodation are studied for a class of continuous-time Markov jump linear systems under actuator fault, sensor fault and external disturbance in the framework of finite frequency domain. The imprecise statistic of modes transitions are considered here, which means that the transition rates are uncertain. Firstly, the sensor fault is defined as a new state, and then an adaptive observer is developed to estimate the actuator fault and the newly defined state with the aid of the descriptor system approach. By using the obtained fault estimation, a novel fault accommodation scheme is proposed based on the sample point controller design approach to satisfy the stochastic stability requirement of the closed-loop system with a prescribed H ∞ performance bound. Moreover, for the convenience of design, the presented sample point controller design is successfully transformed into resolving a class of input-delay control issues. Finally, two numerical examples, including a practical F-404 aircraft engine system, are illustrated to show the effectiveness and applicability of the developed design methods.

Time-series data are widespread in real-world industrial scenarios. To recover and infer missing information in real-world applications, the problem of time-series prediction has been widely studied as a classical research topic in data mining. Deep learning architectures have been viewed as next-generation time-series prediction models. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks. In this study, we prospectively examine the problem of time-series prediction adversarial attacks and propose an attack strategy for generating an adversarial time series by adding malicious perturbations to the original time series to deteriorate the performance of time-series prediction models. Specifically, a perturbation-based adversarial example generation algorithm is proposed using the gradient information of the prediction model. In practice, unlike the imperceptibility to humans in the field of image processing, time-series data are more sensitive to abnormal perturbations and there are more stringent requirements regarding the amount of perturbations. To address this challenge, we craft an adversarial time series based on the importance measurement to slightly perturb the original data. Based on comprehensive experiments conducted on real-world time-series datasets, we verify that the proposed adversarial attack methods not only effectively fool the target time-series prediction model LSTNet, they also attack state-of-the-art CNN-, RNN-, and MHANET-based models. Meanwhile, the results show that the proposed methods achieve a good transferability. That is, the adversarial examples generated for a specific prediction model can significantly affect the performance of the other methods. Moreover, through a comparison with existing adversarial attack approaches, we can see that much smaller perturbations are sufficient for the proposed importance-measurement based adversarial attack method. The methods described in this paper are significant in understanding the impact of adversarial attacks on a time-series prediction and promoting the robustness of such prediction technologies.

The missing data problem - attributed to malfunctioning detectors, packet loss during transmission, or data removed by quality control procedures - is unavoidable in most traffic-related datasets. However, this problem has adversely affected traffic engineering applications as they heavily rely on accurate and comprehensive data. This study aims to impute missing loop detector data in order to improve the estimation results of traffic flow analysis. This paper presents a statistically principled methodology that focuses not only on proposing a computationally efficient imputation approach, but also on assessing the uncertainty associated with imputed values. The proposed methodology quantifies the accuracy of imputation and estimation of uncertainty for a range of challenging patterns of missing loop detector data, and compares them with existing methods. The results of the analysis demonstrate that the performance of the proposed approach remains unaffected by the presence of a large number of missing patterns and reflects the true statistical properties of the principal data. The proposed approach is also comparatively less computationally complex than the existing methods. Further, the comparative analysis of the proposed estimator shows that the generated prediction intervals are reasonably accurate and conform to the desired confidence levels with relatively small interval width.

The pattern recognition of surface electromyography (sEMG) signal is an important application in the realization of human-machine interface. However, due to the disturbance of human body, sensors and environment, sEMG signal usually contains lots of noise, which brings great challenges to the high-accuracy sEMG pattern recognition. In addition, embedded human wearable devices are becoming more and more popular nowadays. How to realize the sEMG recognition method with low power consumption and high noise immunity has also become a difficult and very meaningful research topic. In this paper, a spiking neural network (SNN) classification method based on second-order information bottleneck training is proposed. Firstly, the training loss function for classification neural networks is constructed based on the proposed second-order information bottleneck. The method is used to train the conventional continuous-valued neural network and convert it into an SNN model with equivalent structure and connection weights. Then, the converted SNN is used to classify the sEMG signal patterns. Through a series of theoretical analysis and experimental results, it is proved that this method has significant advantages in terms of generalization of network determination and computational efficiency. The experimental code can be accessed from https://github.com/anvien/2OIB-for-sEMG-Recognition.

Cloud service selection (CSS) remains a strategically significant decision and has a substantial impact on an organization’s competitive edge. Despite considerable research, the literature lacks a comprehensive unified approach to consensual CSS. Recognizing the significance of CSS decisions, in this paper, we propose a novel Cloud Service Scrutinization and Selection Framework (C3SF) that includes four interdependent phases: (1) requirements elicitation, (2) scrutinization, (3) evaluation, and (4) ranking & selection. As part of C3SF, we use conjunctive screening to scrutinize cloud services. We also propose a novel multi-criteria decision-making (MCDM) approach called the modified Best-Worst Method (MBWM), which computes the weights of criteria using early-stage consensus among decision-makers. In addition, we introduce an innovative two-step consensus process for ranking services using leading MCDM methods followed by an aggregation of ranks using a Markov chain-based approach. Moreover, to develop a broader consensus, we propose another two-stage novel mechanism comprising multi-aggregation and synthesis/fusion of rank information using a partially ordered set. We validate the performance and effectiveness of C3SF through a CSS case study using real-world data followed by a comprehensive analysis. The results show that C3SF is robust, practical, and suitable for well-informed decision-making.

In recent years, many applications are using various forms of deep learning models. Such methods are usually based on traditional learning paradigms requiring the consistency of properties among the feature spaces of the training and test data and also the availability of large amounts of training data, e.g., for performing supervised learning tasks. However, many real-world data do not adhere to such assumptions. In such situations transfer learning can provide feasible solutions, e.g., by simultaneously learning from data-rich source data and data-sparse target data to transfer information for learning a target task. In this paper, we survey deep transfer learning models with a focus on applications to text data. First, we review the terminology used in the literature and introduce a new nomenclature allowing the unequivocal description of a transfer learning model. Second, we introduce a visual taxonomy of deep learning approaches that provides a systematic structure to the many diverse models introduced until now. Furthermore, we provide comprehensive information about text data that have been used for studying such models because only by the application of methods to data, performance measures can be estimated and models assessed. publishedVersion

Cooperative problems under continuous control have always been the focus of multi-agent reinforcement learning. Existing algorithms suffer from the problem of uneven learning degree with the increase of the number of agents. In this paper, a new structure for a multi-agent actor critic is proposed, and the self-attention mechanism is applied in the critic network and the value decomposition method used to solve the uneven problem. The proposed algorithm makes full use of the samples in the replay memory buffer to learn the behavior of a class of agents. First, a new update method is proposed for policy networks that promotes learning efficiency. Second, the utilization of samples is improved, at the same time reflecting the ability of perspective-taking among groups. Finally, the ”deceptive signal” in training is eliminated and the learning degree among agents is more uniform than in the existing methods. Multiple experiments were conducted in two typical scenarios of a multi-agent particle environment. Experimental results show that the proposed algorithm can perform better than the state-of-the-art ones, and that it exhibits higher learning efficiency with an increasing number of agents.

In this paper, we propose adversarial predictive coding (APC), a novel method for detecting abnormal events. Abnormal event detection (AED) is to identify unobserved events from a given training dataset consisting of normal events, and it is considered as one of the most important objectives in developing intelligent surveillance systems. Given videos and motion flows of normal events, the APC derives a normal event model by applying an adversarial prediction approach on the jointly learnt latent feature space from the videos and motion flows. Since latent space requires more abstracted and noise-free information than the raw data space, the APC can derive a more discriminative model for normal events compared with other deep learning-based AED methods which directly apply uni-modal losses such as mean square error and cross-entropy to low-level data such as video frames. We demonstrate the effectiveness of our method in detecting abnormal events using UCSD-Ped, Avenue, and UCF-Crime datasets. The experimental results show that the APC surpass the existing state-of-the-art AED methods by deriving a more discriminative model for normal events.

This paper addresses the event-triggered control problem of discrete-time uncertain networked control systems under random deception attacks. In order to deal with a network resource constraint, a novel two-event-generator scheme over both sensor-to-controller and controller-to-actuator channels is proposed to reduce the transmission frequency of system states and control commands, while maintaining desired system performance. More specifically, one of the event-generators is a time-varying memory-based event-triggered scheme (METS), and the other is the traditional event-triggered scheme (ETS). On the other hand, in order to incorporate network security , a new model of memory-based NCSs is established under random deception attacks. Then, sufficient conditions for preserving the asymptotic stability of uncertain NCSs are proposed by using the Lyapunov functional method. Furthermore, co-design criteria are derived for determining both the memory-based state feedback controller gains and event-triggering parameter matrices. Finally, the effectiveness of the proposed methods is verified by a quarter-car suspension system.

This paper aims to solve sample inefficiency in Asynchronous Advantage Actor-Critic (A3C). First, we design a new off-policy actor-critic algorithm, which combines actor-critic with experience replay to improve sample efficiency. Next, we study the sampling method of experience replay for trajectory experience and propose a familiarity-based replay mechanism which uses the number of replay times of experience as the probability weight of sampling. Finally, we use the GAE-V method to correct the bias caused by off-policy learning. We also achieve better performance by adopting a mechanism that combines off-policy learning and on-policy learning to update the network. Our results on Atari and MuJoCo benchmarks show that each of these innovations contributes to improvements in both data efficiency and final performance. Furthermore, our approach keeps a fast coverage speed and the same parallel feature as A3C, and also has better performance on exploration.

Vacating room after encryption (VRAE) is a popular framework of reversible data hiding for encrypted images (RDHEI). Most VRAE based RDHEI methods do not make a desirable payload. To address this issue, this paper proposes a novel data hiding technique using adaptive difference recovery (ADR) and exploits this novel technique to design an efficient VRAE based RDHEI method. Specifically, the proposed ADR-based data hiding technique adaptively determines the range of original difference between a cover pixel and a reference pixel with correlation according to the marked difference generated by bit substitution technique. The cover pixel can be recovered losslessly with the range of original difference and the reference pixel. In the proposed RDHEI method, the encrypted image is generated by block permutation and block-based modulation so that spatial redundancy within encrypted image blocks is preserved. Then the ADR-based data hiding technique is applied to the encrypted blocks. Experiment results show that the proposed RDHEI method outperforms some state-of-the-art RDHEI methods in payload. The average embedding rates of the proposed RDHEI method on the BOSSbase, BOWS-2 and UCID datasets are 3.2045, 3.1145 and 2.4633 bpp, respectively.

Classification is one of the fundamental supervised learning tasks which learns classifiers from the given training data and related labels. The quality of labels is important in classification tasks. However, in many real-world scenarios, data annotation is often corrupted, especially when the annotation process is done by humans. Vaguely labeled data is one of the common problems caused by limited domain knowledge or partial data observation. In this paper, a novel method is proposed to classify vaguely labeled data based on evidential fusion. Vaguely labeled data are divided into several small data groups by the proposed valid label-set cover assignment algorithm. Evidence theory is applied to vaguely labeled data classification by regarding each base classifier on a small data group as one piece of evidence. This gives the chance of classifying unseen precise labeled data from related vague labels. Note that our approach is not restricted to any specific classifiers. It can be generalized to any off-the-shelf classification methods with probabilistic outputs. Finally, experiments are conducted on both synthetic data and real-world data with different base classifiers. Experimental results show that the proposed method achieves superior performance against compared methods.

Fuzzy concept has been an important methodology for data analysis, especially in the classification research. Particularly, fuzzy concept could directly process the continuous data through contrasting the numerical data into the membership degree of object to attribute. However, the classical fuzzy concept only focuses on the positive information, that is, the information about membership degree, while ignoring non-membership degree. Meanwhile, since the limitations of individual cognition and cognitive environment, the concept learning is progressive. Inspired by these thoughts, we design an incremental learning mechanism based on progressive fuzzy three-way concept for object classification in dynamic environment. In this paper, the object and attribute learning operators are first defined to obtain fuzzy three-way concept. Then, a progressive fuzzy three-way concept and its corresponding concept space are learned considering the progressive process of concept learning. Moreover, the object classify mechanism and dynamic update mechanism based on the progressive concept space are proposed, and their effectiveness is verified by numerical experiments. Finally, an incremental learning mechanism is further designed for dynamic increased data and compared with other fuzzy classify methods. All the experimental results carried on ten datasets from UCI and KEEL illustrate the proposed learning mechanism is an excellent object classify algorithm.

This paper presents a new facial feature descriptor called Fused Cross Lattice Pattern of Phase Congruency (FCLPPC) for high accuracy homogeneous and heterogeneous illumination invariant intra/inter-modality face recognition. Using the dimensionless phase congruency features, an effective homogeneous and heterogeneous illumination invariant local feature extractor has been devised. Finally, a novel multi-directional binary pattern named Cross Lattice Pattern (CLP) has been proposed. CLP is applied to the previously extracted invariant phase congruency feature map to generate the CLPPC images. Weighted alpha-blending has been performed on the CLPPC maps to generate Fused CLPPC (FCLPPC) map. Recognition results on Extended Yale-B, TUFTS, CMU-PIE, and CASIA NIR-VIS dataset has been presented to depict the superiority of the proposed scheme over other state-of-the-art methods. Additionally, the proposed FCLPPC has been combined with a lightweight Convolutional Neural Network to further augment the recognition accuracy.

Feature selection plays a crucial role in machine learning and data mining , and improves the performance of learning models by selecting a distinguishing feature subset and eliminating irrelevant features. Existing feature selection methods are mainly used for single-label learning and multi-label learning; however, there are only a few feature selection methods for label distribution learning. Label distribution learning has the “curse of dimensionality” problem, similar to that in multi-label learning. In label distribution learning, the related labels of each sample have different levels of importance. Therefore, multi-label feature selection algorithms can not be directly applied to label distribution data, and discretizing the label distribution data into multi-label data would result in the loss of some important supervised information . To solve this problem, a novel feature selection algorithm for label distribution learning is proposed in this paper. The proposed method utilizes neighborhood granularity to explore feature similarity, and it uses a correlation coefficient to generate the label correlations. In addition, sparse learning is used to improve the robustness and control complexity. Experimental results indicate that our proposed method is more effective than five state-of-art feature selection algorithms on twelve datasets, with respect to six representative evaluation measures.

Quality function deployment (QFD) is a team-based product development technique for determining critical engineering characteristics based on customer requirements to realize higher customer satisfaction. Given its simplicity and visibility, the QFD has been broadly utilized in a variety of industries for product or service planning. However, traditional QFD method assumes that the behavior of domain experts is completely rational, and is only suitable for product development problems with a small number of experts. In this paper, we aim to develop a new large group QFD approach based on an extended TODIM (Portuguese acronym for interactive multi-criteria decision making) method under interval type-2 fuzzy context. The interval type-2 fuzzy sets are utilized to describe the imprecise and uncertain correlation assessment information between customer requirements and engineering characteristics. The normal TODIM is modified and used to derive the priority of engineering characteristics considering the psychological factors of experts. Also, an entropy weighting method was adopted to derive the weights of customer requirements objectively. Finally, we provide an electric vehicle product development case and perform a comparison analysis with previous methods to reveal the effectiveness and practicability of the proposed large group QFD approach.

Recommender system has recently received a lot of attention in the information service community. In many application scenarios, such as Internet of Things (IoTs) environments, item multimodal auxiliary information (such as text and image) can be obtained to expand their feature representation and to increase user satisfaction with recommendations. Motivated by this fact, this paper introduces a novel two-stage embedding model (TSEM), which adequately leverage item multimodal auxiliary information to substantially improve recommendation performance. Specifically, it encompasses two sequential stages: graph convolutional embedding (GCE) and multimodal joint fuzzy embedding (MJFE). In the former, we first generate a bipartite graph for user-item interactions, and then utilize it to construct user and item backbone features via a spatial-based graph convolutional network (SGCN). While in the latter, by employing item multimodal auxiliary information, we integrate multi-task deep learning, deterministic Softmax, and fuzzy Softmax into a convolutional neural network (CNN)-based learning framework, which is optimized to obtain user backbone features and item semantic-enhanced fuzzy (SEF) features accurately. After TSEM converges, user backbone features and item SEF features can be utilized to calculate user preferences on items via Euclidean distance . Extensive experiments over two real-world datasets show that the proposed TSEM model significantly outperforms the state-of-the-art baselines in terms of various evaluation metrics .

Recent works on salient object detection (SOD) mainly focus on pixel-level classification by leveraging fully convolutional network (FCN)-based encoder-decoder models. In this paper, considering that the context relation plays a critical role in defining a salient object appearing in a scene, we propose a local-to-global context-aware feature augmentation network, namely LGCNet. A two-branch attention-based context relation modeling structure is designed by considering global context-aware information based on foreground/background cues and global feature representations. A pixel-wise self-attention mechanism is then incorporated for both branches to propagate global context information to local feature representations. As a result, a coarse-to-fine salient object detection model is formulated. The whole framework can be trained in an end-to-end manner under a deeply supervised framework. Experimental results demonstrate the effectiveness of key components proposed in our LGCNet. Our LGCNet achieves promising results in comparison with 18 state-of-the-art methods on six widely-used benchmark datasets.

Multi-modal brain imaging data reflect brain structural and functional information from different aspects, which have been widely used in brain disease diagnosis, including epilepsy and Alzheimer's disease . In practice, it is difficult to obtain all the modalities of each subject due to high cost or equipment limitation. Therefore, it is highly essential to fuse incomplete multi-modality data to improve the diagnostic accuracy . The traditional methods need to perform data cleansing and discard incomplete subjects from the data, which leads to inefficient training and poor robustness. For addressing this problem, this paper proposes an incomplete multi-modality data fusion method based on low-rank representation for the diagnosis of epilepsy and its subtypes. Specifically, we designed an objective function that simultaneously learns the low-rank representation of the complete modality part, and recovers the incomplete modality by the correlation between different modalities. The proposed model can be optimized by using alternating direction method of multipliers . Extensive evaluation of the proposed method on epilepsy classification task with incomplete DTI and fMRI data showed that our method can achieve promising classification results in identifying epilepsy and its subtypes.

This paper investigates an event triggered control problem of connected vehicles under multiple cyber-attacks, including denial-of-service attacks (DoS) and deception attacks, which occur aperiodically. Then, a novel event triggered control scheme is proposed in the presence of multiple cyber-attacks. The upper bound of attack frequency, attack length rate of DoS attacks and the conditions of controller gains are obtained. It is proven that the proposed distributed control scheme can realize the asymptotic tracking performance of connected vehicles while avoiding Zeno behavior. A string stability analysis under multiple cyber-attacks is also provided. Simulation results confirm the effectiveness of the event triggered control schemes under multiple cyber-attacks.

Given that the three-way decision (3WD) theory provides a scientific solution and reasonable semantic interpretation for solving multi-criteria decision-making (MCDM) problems, this paper presents a new 3WD model and applies it to realistic MCDM problems. Since the fuzzy neighborhood operator is effective in handling uncertain data, we first define a pair of probabilistic approximation operators via the constructed α -fuzzy neigh borhood class. Then, a 3WD model is explored in decision information systems to address the related issues in the proposed fuzzy probabilistic rough set model. In light of the above works, we establish a novel approach to classify and rank applicants for enterprise talent recruitment problems. Instead of the rule of tie-breaking, a way is given to separate applicants into three parts by combining decision attributes , and the feasibility of the approach is confirmed as well. Finally, by using a data set in the UCI database, the results of comparative and experimental analyses demonstrate that the constructed MCDM approach owns better performances in terms of effectiveness and stability.

This paper investigates the fault-tolerant load frequency control (LFC) design of interconnected wind power systems . For this, a doubly-fed induction generator (DFIG)-based wind farm is integrated into the interconnected power systems . Distinct from the existing works, the communication time-delay is taken into the area control error (ACE) signals of interconnected wind power systems. Also, compared with the traditional LFC scheme, a fault-tolerant H ∞ LFC scheme is presented with known and unknown actuator fault against load disturbances and wind speed fluctuations. Then, the asymptotic stability conditions are derived in the linear matrix inequalities (LMIs) form by utilizing the Lyapunov–Krasovskii functional (LKF) method and Wirtinger-based inequality technique under the designed LFC scheme with H ∞ performance. Finally, numerical examples are given to validate the applicability and superiority of the designed LFC scheme.

Task scheduling is an important research direction in cloud computing. The current research on task scheduling considers mainly the design of scheduling strategies and algorithms and rarely gives attention to the influences of uncertain factors, such as the network bandwidth and millions of instructions per second (MIPS), on the scheduling process. The network bandwidth and MIPS directly affect the performance of a virtual machine (VM), which further influences the scheduling performance. In this paper, uncertain factors are transformed into interval parameters. The make-span, scheduling cost, load balance, and task completion rate are simultaneously considered in the scheduling process. Then, an interval many-objective cloud task scheduling optimization (I-MCTSO) model is designed to simulate real cloud computing task scheduling. To implement this model, an interval many-objective evolutionary algorithm (InMaOEA) is proposed. An interval credibility strategy is employed to improve the convergence performance. The hyper-volume and degree of overlap based on the interval congestion distance strategy are used to increase the population diversity. Simulation results demonstrate the effectiveness and superior performance of InMaOEA in comparision with other algorithms. The proposed approaches can provide decision-makers with an efficient allocation plan for cloud task scheduling.

As a powerful tool for data streams processing, the vast majority of existing evolving intelligent systems (EISs) learn prediction models from data in a supervised manner. However, high-quality labelled data can be difficult to obtain in many real-world classification applications concerning data streams, though unlabelled data is plentiful. To overcome the labelling bottleneck and construct a stronger classification model, a novel semi-supervised EIS is proposed in this paper. After being primed with a small amount of labelled data, the proposed method is capable of continuously self-developing its system structure and self-updating the meta-parameters from unlabelled data streams chunk-by-chunk in a non-iterative, exploratory manner by exploiting a novel pseudo-labelling strategy. Thanks to its transparent prototype-based structure and human-understandable reasoning process, the proposed method can provide users high explainability and interpretability while achieving great classification precision. Experimental investigation demonstrates the superior performance of the proposed method.

This paper proposes an adaptive memetic differential evolution with multi-niche sampling and neighborhood crossover strategies for global optimization. In the proposed algorithm, a multi-niche sampling strategy is designed to sample a subpopulation for evolution at each generation. In this strategy, the entire population is firstly divided into multiple niches by employing a certain niching strategy at each generation. A subpopulation is then dynamically sampled from the resulting niches such that supporting a diverse search at the early stage of evolution while an intensive search towards the end of evolution. The above strategy will be further coupled with a neighborhood crossover, which is devised to encourage high potential solutions for exploitation while low potential solutions for exploration, thus appropriately searching the solution space. Additionally, an adaptive local search (ALS) scheme along with an adaptive elimination operation (AEO) have been designed. The ALS aims to appropriately fine-tune promising solutions in the sampled subpopulation while the AEO tends to adaptively eliminate unpromising individuals in the population during evolution. The performance of the proposed algorithm has been evaluated on CEC’2015 benchmark functions and compared with related methods. Experimental results show that our algorithm can achieve a superior performance and outperform related methods. The results also confirm the significance of devised strategies in the proposed algorithm.

Nowadays, an increasing amount of data has been sent to the cloud for analysis and storage, and data security in the cloud has been widely concerned. Among them, CP-ABE is regarded as one of the most promising technologies to protect outsourced data. However, in most CP-ABE schemes, attackers may obtain user privacy information from policy plaintext. More likely, partial policy hiding programme neither satisfies full policy hiding nor applies to the needs of the large universe. In this paper,we hide both attribute values and names in policy by using private set intersection (PSI). The CP-ABE programme not only supports a complete hiding policy, but also can calculate the authorization relationship and mapping relationship between user passwords and keys. We use a polynomial-based PSI and a recursive algorithm to calculate the authorization relationship. And with the help of the algorithm and the label vector, the mapping is determined under communication restrictions. Through the outsourcing index, we achieve an efficient hiding strategy and effectively reduce the user's computing overhead. Finally, theoretical analysis and experiments show that our model has better performance while effectively protecting sensitive attributes.

With the increasing scale of obtained multi-view data, how to deal with large-scale multi-view data quickly and efficiently is a significant problem. In this paper, a novel supervised multi-view feature selection method based on locally sparse regularization and block computing is proposed to solve the problem. Specifically, the multi-view dataset is firstly divided into sub-blocks according to classes and views. Then with the aid of the Alternating Direction Method of Multipliers (ADMM), a sharing sub-model is proposed to perform feature selection on each class by integrating each view’s locally sparse regularizers and shared loss that makes all views share a common penalty and regresses samples to their labels. Finally, all the sharing sub-models are fused to form the final general additive feature selection model, in which each sub-block adjusts its corresponding variables to perform block-based feature selection. In the optimization process, the proposed model can be decomposed into multiple separate subproblems, and an efficient optimization algorithm is proposed to solve them quickly. The comparison experiments with several state-of-the-art feature selection methods show that the proposed method is superior in classification accuracy and training speed.

In the Internet age, analyzing the behavior of online users can help webstore owners understand customers’ interests. Insights from such analysis can be used to improve both user experience and website design. A prominent task for online behavior analysis is clickstream mining, which consists of identifying customer browsing patterns that reveal how users interact with websites. Recently, this task was extended to consider weights to find more impactful patterns. However, most algorithms for mining weighted clickstream patterns are serial algorithms, which are sequentially executed from the start to the end on one running thread. In real life, data is often very large, and serial algorithms can have long runtimes as they do not fully take advantage of the parallelism capabilities of modern multi-core CPUs. To address this limitation, this paper presents two parallel algorithms named DPCompact-SPADE ( D epth load balancing P arallel Compact-SPADE) and APCompact-SPADE ( A daptive P arallel Compact-SPADE) for weighted clickstream pattern mining. Experiments on various datasets show that the proposed parallel algorithm is efficient, and outperforms state-of-the-art serial algorithms in terms of runtime, memory consumption, and scalability.

Ontology verification refers to the activity where an ontology is tested against its ontology requirements to ensure that it is built correctly in compliance with its ontology requirements specification. Therefore, it is an important activity that should be performed in any ontology development process. Since manual verification can be a time-consuming and repetitive task, testing processes to automatically verify an ontology facilitate this activity. Moreover, the involvement of not only ontology engineers during the ontology verification process , but also domain experts and users, can provide valuable feedback to avoid misunderstandings and lack of information. This paper proposes a method for ontology verification that defines the testing activities to be performed. The method uses a testing language based on lexico-syntactic patterns to facilitate the definition of tests and an ontology to store and publish such tests. Moreover, this verification testing method proposes an online tool to execute tests on one or more ontologies. The method was compared in terms of time and errors by user evaluation with other tools for ontology verification; the evaluation showed that the tools that use testing languages had better results in terms of reducing errors in the verification activity compared to the tools that do not.

The k -nearest-neighbor rule is a popular classification technique , and rough set theory is an effective mathematical tool to deal with the uncertainty of data. Rough set models based on k -nearest-neighbor relations have a strong ability to approximate decisions, but the calculation is very time-consuming. In this paper, we model the overlap degree of objects from different categories in advance to accelerate the attribute reduction and improve the classification performance of the selected attributes. Firstly, we define the coincidence degree ( CD ) and distance ( DIS ) of objects from different categories to measure the coverage and distance of between-class objects. Secondly, we combine CD and DIS to define the overlap degree ( OD ) to pre-sort attributes, then use k -nearest-neighbor rough sets to filter inconsistent and redundant attributes. The pre-sort operation based on OD can greatly reduce the number of searches for attributes and ensure that the attributes with high separability should be selected first. Furthermore, we design a fast reduction algorithm ( OD & KNN ) to obtain a reduct with the ability to approximate decisions as well as the original attributes but with lower OD . Comparing experimental results and time complexity of OD & KNN with state-of-the-art algorithms, OD & KNN is more efficient for high-dimensional data while ensuring classification accuracy .

During the past decade, data warehousing has been widely adopted in the business community. It provides multi-dimensional analyses on cumulated historical business data for helping contemporary administrative decision-makings. However, many data warehousing query language in present only provides on-line analytical processing (OLAP) for numeric data. For example, MDX (Multi-Dimensional expressions) has been proposed as a query language to allow describing multi-dimensional queries over databases with OLAP capabilities. Nevertheless, it is believed there is only about 20% information can be extracted from data warehouses concerning numeric data only, the other 80% information is hidden in non-numeric data or even in documents. Therefore, many researchers now advocate it is time to conduct research works on document warehousing to capture complete business intelligence. Document warehouses, unlike traditional document management systems, include extensive semantic information about documents, cross-document feature relations, and document grouping or clustering to provide a more accurate and more efficient access to text-oriented business intelligence. In this paper, we extend the structure of MDX into a new one containing complete constructs for querying document warehouses. The traditional MDX only contains SELECT, FROM, and WHERE clauses, which is not rich enough for document warehousing. In this paper, we present how to extend the language constructs to include GROUP BY, HAVING, and ORDER BY to design an SQL-like query language for document warehousing. The work is essential for establishing an infrastructure to help combining text processing with numeric OLAP processing technologies. Hopefully, the combination of data warehousing and document warehousing will be one of the most important kernels of knowledge management and customer relationship management applications.

In recent years, extensive research has been carried out by using association rules to build more accurate classifiers. The idea behind these integrated approaches is to focus on a limited subset of association rules. This paper aims to contribute to this integrated framework by adapting the Classification Based on Associations (CBA) algorithm. CBA was adapted by coupling it with another measurement of the quality of association rules: i.e. intensity of implication. The new algorithm has been implemented and empirically tested on an authentic financial dataset for purposes of bankruptcy prediction. We validated our results with an association ruleset, with C4.5, with original CBA and with CART by statistically comparing its performance via the area under the ROC-curve. The adapted CBA algorithm presented in this paper proved to generate significantly better results than the other classifiers at the 5% level of significance.

Feed-forward neural networks used for pattern classification generally have one input layer, one output layer and several hidden layers. The hidden layers in these networks add extra non-linearity for realization of precise functional mapping between the input and the output layers, but semantic relations of the hidden layers with their predecessor and successor layers cannot be justified. This paper presents a novel scheme for supervised learning on a fuzzy Petri net that provides semantic justification of the hidden layers, and is capable of approximate reasoning and learning from noisy training instances. An algorithm for training a feed-forward fuzzy Petri net and an analysis of its convergence have been presented in the paper. The paper also examines the scope of the learning algorithm in object recognition from 2D geometric views.

This paper studies the issue of assigning orientations to all links of an undirected network, which is called the link-orientation problem (LOP). First, we describe some practical variations and address some complexity analysis in LOPs for general networks. Then, we design efficient algorithms to solve these LOPs issues. The proposed approach employs the recursive shortest path rules and the efficient cost cores scheme to resolve the LOP issues for some weighted network systems. These classes of networks include multi-square, convex-bipartite, and convex-split networks. Next, we use the cost core algorithm for adjusting LOP and obtain the more efficient LOP for the weighted convex-bipartite and convex-split networks. In general, the LOP scheme can support the actual applications requirements to realize a WFQ (weighted fair queuing) task for multicast routing in real networks. Finally, the findings of this paper can be mainly applied to some network applications, including efficient revision of load balancing in a ATM switch network, quick selection of a firm's PCS (Proxy Cache Server) on the Web, and dynamic adjusting of link's changing conditions in a workflow system on the Internet.

This paper proposes a gradual formation of a spatial pattern for a homogeneous robot group. The autonomous formation of spatial pattern is one of key technologies for the advancement of cooperative robotic systems because a pattern formation can be regarded as function differentiation of a multi-agent system. When multiple autonomous robots without a given local task cooperatively work for a global objective, the function differentiation is the first and indispensable step. For example, each member of cooperative insects or animals can autonomously recognize own local tasks through mutual communication with local members. There were a lot of papers that reported a spatial pattern formation of multiple robots, but the global information was supposed to be available in their approaches. It is however almost impractical assumption for a small robot to be equipped with an advanced sensing system for global localization due to robot's scale and sensor size. The local information-based algorithm for the pattern formation is desired even if each robot is not equipped with a global localization sensor.We therefore propose a gradual pattern formation algorithm, i.e., a group of robots improves complexity of their pattern from to a simple pattern to a goal pattern like a polygon. In the algorithm, the Turing diffusion-driven instability theory is used so that it could differentiate roles of each robot in a group based only on local information. In experiment, we demonstrate that robots can make a few polygon patterns from a circle pattern by periodically differentiating robot's roles into a vertex or a side. We show utilities of the proposed gradual pattern formation algorithm for multiple autonomous robots based on local information through some experiments.

In the General Packet Radio Service (GPRS)/Universal Mobile Telecommunications System (UMTS) short message service (SMS) network, the serving GPRS support node (SGSN) stores the message in a queue until it is successfully transferred to the mobile device or its maximum waiting time expires. The queued message is forwarded to the new SGSN and is immediately removed from the waiting queue of the old SGSN, if the corresponding mobile device leaves the old SGSN area before the valid queueing time expires. Once the SGSN's buffer is full, all incoming messages must be discarded and retransmitted later. Under this situation, the messages, especially the forwarded messages, suffer from a longer delay before being successfully delivered. To prevent messages from being excessively delayed, this paper proposes an SGSN overload control scheme called the N-overload scheme. According to this scheme, a newly arriving message is discarded if the buffer is full upon its arrival. A forwarded message, however, is queued in an overload space of size N. Taking the forwarding and the expiration effects of queued messages into account, this paper also develops an analytical model to study the performance for the SGSN using the N-overload scheme. On the basis of the analysis, the optimal value of N is numerically determined so that the loss probability for the forwarded messages is minimized.