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Unsupervised attribute reduction becomes very challenging due to a lack of decision information, which is to select a subset of attributes that can maintain learning ability without decision information. However, most of the existing unsupervised attribute reduction methods are proposed for numerical or nominal attributes, and little research has been done on unsupervised mixed attribute reduction methods. In view of this, this paper proposes a generalized unsupervised mixed attribute reduction model based on fuzzy rough sets . First, based on all single attribute subsets , the significance is defined to indicate the importance of a candidate attribute. Then, a specific fuzzy rough-based unsupervised attribute reduction (FRUAR) algorithm is designed. Finally, the proposed algorithm is compared with the existing algorithms by using thirty public data sets. Experimental results show that the algorithm FRUAR can select fewer attributes to maintain or improve the performance of learning algorithms, and it is suitable for mixed attribute data.

Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary subject, and is referred to as, multi-sensor information fusion which was originated in the 1970s. Nowadays, the types and updates of data are becoming more multifarious and frequent, which bring new challenges for information fusion to deal with the multi-source data. Consequently, the construction of MSIF models suitable for different scenarios and the application of different fusion technologies are the core problems that need to be solved urgently. Rough set theory (RST) provides a computing paradigm for uncertain data modeling and reasoning, especially for classification issues with noisy, inaccurate or incomplete data. Furthermore, due to the rapid development of MSIF in recent years, the methodologies of learning under RST are becoming increasingly mature and systematic, unveiling a framework which has not been mentioned in the literature. In order to better clarify the approaches and application of MSIF in RST research community, this paper reviews the existing models and technologies from the perspectives of MSIF model (i.e., homogeneous and heterogeneous MSIF model), multi-view rough sets information fusion model (i.e., multi-granulation, multi-scale and multi-view decisions information fusion models), parallel computing information fusion model, incremental learning fusion technology and cluster ensembles fusion technology. Finally, RST based MSIF related research directions and challenges are also covered and discussed. By providing state-of-the-art understanding in specialized literature, this survey will directly help researchers understand the research developments of MSIF under RST.

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019. Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this article, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We first maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. The proposed method achieves state-of-the-art performance. Dice similarity coefficients are 0.987 and 0.726 for lung and COVID-19 segmentation, respectively. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively. The proposed network enhances the segmentation ability of the COVID-19 infection, makes the connection with other techniques and contributes to the development of remedying COVID-19 infection.

Establishing and optimizing a distributed parameter thermal path model for transformer windings is an important means to analyze the hot-spot temperature of the windings; furthermore, in the process of improving the thermal path model of transformers and improving the accuracy of their models, the thermal characteristics of the insulation paper are extremely important. A microscopic thermal contact resistance model was established for the contact interface of insulation paper and copper, according to the microscopic morphology parameters of copper and insulation paper observed by atomic force microscopy, the thermal contact resistance under different temperatures was calculated. A dual heat flow test platform was built to test the heat transfer behavior of four different thickness insulation papers under four temperatures, and the thermal contact resistance and thermal bulk resistance with temperature were analyzed. The results show that as the temperature increases, the thermal resistance of the insulation paper becomes larger and the thermal conductivity decreases. The average error between the experimental and theoretical values of the thermal contact resistance under four temperature points in the study is 6.6%. The results provide a data support for establishing and optimizing transformer heat generation and heat transfer models.

In many real-life applications, the data collected from different information sources are always located in diverse sites and characterized by different types of attributes. In this paper, we call this type of data as multi-source hybrid data. Existed rough set models only work well with single source data or multi-source data with one type of attributes, which are not suitable for processing multi-source hybrid data. Furthermore, in dynamic data environment, multi-source hybrid data are typically evolving over time, including the addition of objects and attributes and the revision of missing values in each information source. It will result in the update of information granulation and rough approximation structures. To address these issues, we present an extended rough set model, named as multi-source composite rough sets (MCRS), by integrating different types of attributes and fusing multiple composite relations derived from different information sources. Two novel matrix operators are proposed to construct the matrix-based representation of rough approximations. Then, under the variations of objectives, attributes and attribute values in the multi-source hybrid information system, we present incremental mechanisms based on matrices for the maintenance of approximations by utilizing the previously accumulated matrices information and transmitting location information of part elements in each composite relation matrix. In addition, we develop the incremental algorithms for updating composite rough approximations. A series of comparative experiments show that the proposed algorithm outperforms the static algorithm and other extended incremental algorithms in terms of the efficiency of computation and transmission.

Community detection is the process of detecting communities in complex networks. Communities are important structures that can help us further study the properties of complex networks. In recent years, swarm intelligence algorithms have been applied to community detection and have achieved remarkable results. However, these existing algorithms have limited search ability and easily fall into the problem of local optima. In this paper, we propose a new community detection approach based on an improved whale optimization algorithm (WOA). The WOA is applied to a discrete symbol space in solving the community detection problem, therefore topology structure-based search strategies, adjustment and mergence policies, and evolutionary population method are designed to improve the efficiency and effectiveness of the method. Then, a whale optimization algorithm with evolutionary population for community detection (EP-WOCD) is proposed. Extensive experiments are conducted to compare the EP-WOCD with other state-of-the-art algorithms on both artificial and real-world social networks. Experimental results show that the EP-WOCD is effective and stable.

Dynamic data in real-time application are typically updating in a multi-dimensional manner. In this paper, we introduce a novel approach based on Dominance-based Rough Set Approach (DRSA) to efficiently deal with the multi-dimensional variations of attribute set and attribute values in dynamic Ordered Information Systems (OIS). We improve the original notion of the P-generalized decision domains to make the feature value matrix be dominance symmetrical, and propose an efficient strategy based on the improved notion to obtain the dominance feature matrix. Then, we employ the dominance-feature-matrix-based incremental strategy to avoid repeated comparisons between original attributes, so that to efficiently update rough approximations of DRSA with the simultaneously increased attribute set and varied attribute values. In our approach, the steps based on these two combined strategies can work altogether or separately, not only efficiently dealing with the simultaneously increased attribute set and varied attribute values, but also efficiently dealing with the individually increased attribute set or varied attribute values in dynamic OIS. Efficient algorithm based on the updating strategies is designed and multiple groups of experiments are conducted. Experimental results on different real-world data sets show that the proposed algorithm is much faster than other algorithms for dealing with the multi-dimensional or the single-dimensional variations of attribute set and attribute values.

Purpose The purpose of this study is to explore the effects of review group’s content-related and environment-associated attributes on information adoption intention of information readers. In addition, this study further investigates the effects of these determinants in different websites with different commercial attributes (i.e. online stores and third party forums). Design/methodology/approach This study used a structured online survey to collect data from an online store and a third party forum in China; totally 302 responses were collected. Findings The empirical results confirm that all of the five determinants significantly affect the information adoption intention of information readers. Furthermore, the authors found that four of the determinants have distinct effects in different websites. Originality/value The findings of this study validate the significant moderating role of website attributes in readers’ information processing. Information readers use distinct criteria to evaluate the received review information; electronic word-of-mouth determinants have varying effects on different websites with different commercial attributes.

Feature selection is a meaningful aspect of data mining that aims to select more relevant data features and provide more concise and explicit data descriptions. It is beneficial for constructing an effective learning model and reducing the consumption of memory and time. In real-life applications, imbalanced data are ubiquitous, such as those in medical diagnoses, intrusion detection, and credit ratings. In recent years, feature selection for imbalanced data has attracted increasing research attention. Neighborhood rough set theory has been effectively applied to feature selection when dealing with mixed types of data. In this study, we propose an approach for feature selection for imbalanced data employing neighborhood rough set theory. The significance of features is defined by carefully studying the upper and lower boundary regions. The uneven distribution of the classes is considered during the definition of the feature significance. A discernibility-matrix-based feature selection method, which is a key method in rough set theory, is used; then, a novel algorithm for feature selection (RSFSAID) is proposed. The uncertainty of feature selection resulting from different parameters is investigated, and a particle swarm optimization algorithm is used to determine the optimized parameters in the algorithm. Extensive experiments are performed with public datasets to evaluate the proposed method. Experimental results show that the RSFSAID algorithm can improve the classification performance of imbalanced data compared to four other algorithms.

Dominance-based Rough Set Approach (DRSA) is widely applied in processing the information and data with a preference-order relation. In many real-life Ordered Information Systems (OIS), the attribute values and objects may vary simultaneously with time. Existing incremental updating algorithms in OIS are all single-dimensional, i.e., they only consider the individual variation of attribute values or objects. In addition, all the objects need to be compared with each other during updating. In this paper, we focus on designing specific multi-dimensional algorithms to efficiently update approximations on the simultaneous variation of attribute values and objects in OIS. The properties of the P-generalized decision in DRSA are firstly presented. Then two novel notions e.g., the P-generalized decision lower and upper domains, are defined, respectively. These two notions indicate the practical dominance dependence between different objects in OIS, and provide a simplified definition of the P-generalized decision. The approach based on these two notions can greatly reduce the comparisons between different objects, as well as provide novel strategies which can efficiently obtain the updated P-generalized decision to obtain the updated approximations. Furthermore, we develop two domain-wise algorithms which correspond to two cases of the multi-dimensional variation of attribute values and objects in OIS, respectively. Moreover, in implementing the algorithms, the sorting strategies are integrated to solve the problem of time-consuming traversals. A series of experimental results illustrate that our proposed domain-wise algorithms are not only more efficient than both of the traditional static algorithm and the integrated single-dimensional algorithms for dealing with the multi-dimensional variation of attribute values and objects, but also more efficient than the single-dimensional algorithms for dealing with the individual variation of objects in OIS.

Accurate short-term traffic flow prediction plays an indispensable role for solving traffic congestion. However, the structure of traffic data is nonlinear and complicated. It is a challenge to get high precision. The least square support vector machine (LSSVM) has powerful capabilities for time series and nonlinear regression prediction problems if it can select appropriate parameters. To search the optimal parameters of LSSVM, this paper proposes a hybrid optimization algorithm which combines particle swarm optimization (PSO) with genetic algorithm. The main contributions are twofold: (1) A hybrid optimization method is proposed, which can skip the local optimal pitfall with less learning time by introducing a selection strategy, crossover and mutation operators into PSO; (2) the crossover and mutation operators are controlled by adaptive probability functions. The crossover and mutation probabilities increase when the population fitness is concentrated, and decrease when the fitness is dispersed. It can effectively improve the precision and speed of convergence. The proposed model is verified based on the measured data. The experimental results show that our new model yields better prediction ability and relatively high computational efficiency compared with other related models.

Integrated optical waveguide sensor has characteristics of good insulation, broadband response, small size, and large dielectric strength, which is appropriate for measurement of intensive pulsed electric field. Two types of integrated optics sensors using through and reflective type asymmetric Mach–Zehnder interferometer are designed, fabricated, and implemented. Key parameters of the optical waveguide and antenna are theoretically analyzed and designed. The time domain response, input/output characteristics and frequency response of the sensors are investigated. The temperature stability is evaluated by measuring output power fluctuations of the sensors. Experiment results reveal that the sensitivity of the through type sensor is a little higher than that of the reflective type sensor, while the size (length) of the reflective type sensor is only half of the through type. Besides, as only a single optical fiber is coupled, it is convenient for the reflective type sensor to measure in very small space. Applications of the sensor for measurement of the lightning electromagnetic pulse, nuclear electromagnetic pulse, and pulsed radio frequency fields are demonstrated. The measurement results indicate that the developed integrated optics sensor has powerful potential and is expected to be widely used for measurement of intense pulsed electric fields.

Three-way decisions theory has been developed and applied to decision making in uncertain environments. Decision tasks under ambiguity involving both missing values and hierarchical concept are abundant in real-world applications. In this paper, we study the update problem of three-way decisions with dynamic variation of scales in incomplete multi-scale information systems. The updating mechanisms of decision granules induced by the similarity relation are exploited with the cut refinement and coarsening through the attribute value taxonomies, and the dynamic tendencies of conditional probability are hence estimated with evolving granularity structure. Reasonable updating behaviors of probabilistic parameters are characterized in the framework of decision-theoretic rough sets, in order to relate optimism and pessimism decision making on the basis of the variation in attitudes to losses or costs of decisions during the periodic transformation between coarser granular-scale and refiner granular-scale of data. Then the dynamic tendencies of conditional probability with the updating behaviors of probabilistic parameters together are incorporated into the incremental process of updating three-way decisions. Theoretical justifications and semantic interpretations are provided to guarantee the satisfaction of the proposed method for incremental three-way decisions in multi-scale incomplete information systems. Experimental results on several UCI datasets show that the proposed incremental algorithms efficiently update three-way decisions for handling transformation of data scales caused by the cut refinement and coarsening through the attribute value taxonomies.

Information fusion is capable of fusing and transforming multiple data derived from different sources to provide a unified representation for centralized knowledge mining that facilitates effective decision-making, classification and prediction, etc. Multisource interval-valued data, characterizing the uncertainty phenomena in the data in the form of intervals in different sources, are the most common symbolic data which widely exist in many real-world applications. This paper concentrates on efficient fusing of multisource interval-valued data with the dynamic updating of data sources, involving the addition of new sources and deletion of obsolete sources. We propose a novel data fusion method based on fuzzy information granulation, which translates multisource interval-valued data into trapezoidal fuzzy granules. Given this effectively fusing capability, we develop incremental mechanisms and algorithms for fusing multisource interval-valued data with a dynamic variation of data sources. Finally, extensive experiments are carried out to verify the effectiveness of the proposed algorithms when comparing to six different fusion algorithms. Experimental results show that the proposed fusion method outperforms other related approaches. Furthermore, the proposed incremental fusion algorithms can reduce the computing overhead in comparison with the static fusion algorithm when adding and deleting multiple data sources.

With the rapid deployment of cloud platforms, high service reliability is of critical importance. An industrial cloud platform contains a huge number of disks, and disk failure is a common cause of service unreliability. In recent years, many machine learning based disk failure prediction approaches have been proposed, and they can predict disk failures based on disk status data before the failures actually happen. In this way, proactive actions can be taken in advance to improve service reliability. However, existing approaches treat each disk individually and do not explore the influence of the neighboring disks. In this paper, we propose Neighborhood-Temporal Attention Model (NTAM), a novel deep learning based approach to disk failure prediction. When predicting whether or not a disk will fail in near future, NTAM is a novel approach that not only utilizes a disk’s own status data, but also considers its neighbors’ status data. Moreover, NTAM includes a novel attention-based temporal component to capture the temporal nature of the disk status data. Besides, we propose a data enhancement method, called Temporal Progressive Sampling (TPS), to handle the extreme data imbalance issue. We evaluate NTAM on a public dataset as well as two industrial datasets collected from millions of disks in Microsoft Azure. Our experimental results show that NTAM significantly outperforms state-of-the-art competitors. Also, our empirical evaluations indicate the effectiveness of the neighborhood-ware component and the temporal component underlying NTAM as well as the effectiveness of TPS. More encouragingly, we have successfully applied NTAM and TPS to Microsoft cloud platforms (including Microsoft Azure and Microsoft 365) and obtained benefits in industrial practice.

Liver vessel segmentation is fast becoming a key instrument in the diagnosis and surgical planning of liver diseases. In clinical practice, liver vessels are normally manual annotated by clinicians on each slice of CT images, which is extremely laborious. Several deep learning methods exist for liver vessel segmentation, however, promoting the performance of segmentation remains a major challenge due to the large variations and complex structure of liver vessels. Previous methods mainly using existing UNet architecture, but not all features of the encoder are useful for segmentation and some even cause interferences. To overcome this problem, we propose a novel deep neural network for liver vessel segmentation, called LVSNet, which employs special designs to obtain the accurate structure of the liver vessel. Specifically, we design Attention-Guided Concatenation (AGC) module to adaptively select the useful context features from low-level features guided by high-level features. The proposed AGC module focuses on capturing rich complemented information to obtain more details. In addition, we introduce an innovative multi-scale fusion block by constructing hierarchical residual-like connections within one single residual block, which is of great importance for effectively linking the local blood vessel fragments together. Furthermore, we construct a new dataset containing 40 thin thickness cases (0.625 mm) which consist of CT volumes and annotated vessels. To evaluate the effectiveness of the method with minor vessels, we also propose an automatic stratification method to split major and minor liver vessels. Extensive experimental results demonstrate that the proposed LVSNet outperforms previous methods on liver vessel segmentation datasets. Additionally, we conduct a series of ablation studies that comprehensively support the superiority of the underlying concepts.

In cloud service systems, customers will report the service issues they have encountered to cloud service providers. Despite many issues can be handled by the support team, sometimes the customer issues can not be easily solved, thus raising customer incidents. Quick troubleshooting of a customer incident is critical. To this end, a customer incident should be assigned to its responsible team accurately in a timely manner. Our industrial experiences show that linking customer incidents with detected system incidents can help the customer incident triage. In particular, our empirical study on 7 real cloud service systems shows that with the additional information about the system incidents (i.e., incident reports generated by system monitors), the triage time of customer incidents can be accelerated 13.1× on average. Based on this observation, in this paper, we propose LinkCM, a learning based approach to automatically link customer incidents to monitor reported system incidents. LinkCM incorporates a novel learning-based model that effectively extracts related information from two resources, and a transfer learning strategy is proposed to help LinkCM achieve better performance without huge amount of data. The experimental results indicate that LinkCM is able to achieve accurate link prediction. Furthermore, case studies are presented to demonstrate how LinkCM can help the customer incident triage procedure in real production cloud service systems.

In large-scale cloud systems, unplanned service interruptions and outages may cause severe degradation of service availability. Such incidents can occur in a bursty manner, which will deteriorate user satisfaction. Identifying incidents rapidly and accurately is critical to the operation and maintenance of a cloud system. In industrial practice, incidents are typically detected through analyzing the issue reports, which are generated over time by monitoring cloud services. Identifying incidents in a large number of issue reports is quite challenging. An issue report is typically multi-dimensional: it has many categorical attributes. It is difficult to identify a specific attribute combination that indicates an incident. Existing methods generally rely on pruning-based search, which is time-consuming given high-dimensional data, thus not practical to incident detection in large-scale cloud systems. In this paper, we propose MID (Multi-dimensional Incident Detection), a novel framework for identifying incidents from large-amount, multi-dimensional issue reports effectively and efficiently. Key to the MID design is encoding the problem into a combinatorial optimization problem. Then a specific-tailored meta-heuristic search method is designed, which can rapidly identify attribute combinations that indicate incidents. We evaluate MID with extensive experiments using both synthetic data and real-world data collected from a large-scale production cloud system. The experimental results show that MID significantly outperforms the current state-of-the-art methods in terms of effectiveness and efficiency. Additionally, MID has been successfully applied to Microsoft's cloud systems and helped greatly reduce manual maintenance effort.

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%–40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases., Highlights • We build a system for detecting COVID-19 infection regions. • We build a dataset of COVID-19. • It is easy to distinguish pneumonia from healthy cases.

The problem of finding a minimum dominating set (MinDS) in a graph is a well known combinatorial optimization problem with wide applications. Due to its NP-hardness, for large and hard instances one must resort to heuristic approaches to obtain good solutions within reasonable time. This paper develops an efficient local search algorithm for MinDS, which has two main ideas. The first one is a novel local search framework, while the second is a construction procedure with inference rules. Our algorithm named FastDS is evaluated on 4 standard benchmarks and 3 massive graphs benchmarks. FastDS obtains the best performance for almost all benchmarks. Particularly, FastDS obtains significantly better solutions than state-of-the-art algorithms on massive graphs.

Propositional satisfiability (SAT) is a prominent problem in artificial intelligence with many important applications. Stochastic local search (SLS) is a well-known approach for solving SAT and known to achieve excellent performance on randomly generated, satisfiable instances. However, SLS solvers for SAT are usually ineffective in solving application instances. Here, we propose a highly configurable SLS solver dubbed PbO-CCSAT, which leverages a powerful technique known as configuration checking (CC) in combination with the automatic algorithm design paradigm of programming by optimisation (PbO). Our PbO-CCSAT solver exposes a large number of design choices, which are automatically configured to optimise the performance for specific classes of SAT instances. We present extensive empirical results showing that our PbO-CCSAT solver significantly outperforms state-of-the-art SLS solvers on SAT instances from many applications, and further show that PbO-CCSAT is complementary to state-of-the-art complete solvers.

The maximum satisfiability (MAX-SAT) problem is an important NP-hard problem in theory, and has a broad range of applications in practice. Stochastic local search (SLS) is becoming an increasingly popular method for solving MAX-SAT. Recently, a powerful SLS algorithm called CCLS shows efficiency on solving random and crafted MAX-SAT instances. However, the performance of CCLS on solving industrial MAX-SAT instances lags far behind. In this paper, we focus on experimentally analyzing the performance of SLS algorithms for solving industrial MAX-SAT instances. First, we conduct experiments to analyze why CCLS performs poor on industrial instances. Then we propose a new strategy called additive BMS (Best from Multiple Selections) to ease the serious issue. By integrating CCLS and additive BMS, we develop a new SLS algorithm for MAX-SAT called CCABMS, and related experiments indicate the efficiency of CCABMS. Also, we experimentally analyze the effectiveness of initialization methods on SLS algorithms for MAX-SAT, and combine an effective initialization method with CCABMS, resulting in an enhanced algorithm. Experimental results show that our enhanced algorithm performs better than its state-of-the-art SLS competitors on a large number of industrial MAX-SAT instances.

Multicriteria classification refers to classify objects evaluated by a set of criteria to preference-ordered decision classes. Dominance-based rough set approach has been successfully introduced to express and reason inconsistencies with a dominance principle in multicriteria classification problems. Hierarchical attribute values exist extensively within many real-world applications, which provide a hierarchical form to organize, view and analyze data from different perspectives for accommodating the preference variability. In this study, we consider an extension of dominance-based rough set approach by applying an incremental learning technique for hierarchical multicriteria classification while attribute values dynamically vary across different levels of granulations. We formalize the dynamic characteristics of knowledge granules with the cut refinement and coarsening through attribute value taxonomies in the hierarchical multicriteria decision systems. In consequence, incremental algorithms for computing dominance-based rough approximations of preference-ordered decision classes are developed by applying the resulted prior-knowledge as the input, and only recomputing those outputs which depend on the changed attribute values. This paper presents the theoretical foundation of the proposed approach. Example analysis and experimental evaluation are also provided for illustration of the feasibility and efficiency.

This paper examines how the use of industry standards enable knowledge sharing, process integration, environmental collaboration, and control among supply chain partners, which eventually contribute to the environmental performance of the firms. Survey data were collected from 205 firms in China that implemented RosettaNet standards. Structural equation modeling is used to test the hypotheses related to our research model. The results show that the use of industry standards enhances environmental collaboration and control between supply chain partners, mainly by improving interorganizational knowledge sharing and process integration. In turn, engaging in environmental collaboration and control with supply chain partners improves environmental performances of firms. Further, our empirical analysis indicates that participation in standards consortia positively moderates the effects of industry standards use on knowledge sharing and process integration.

The incremental learning technology has been widely applied in efficient and effective data mining with big data based on granular computing, rough sets and three-way approaches. In real-life applications, the information systems will evolve over time with four levels of variational situations, which can be described by the combination of the variations of attributes, objects, condition attributes values and decision attributes values. Considering updating knowledge with multilevel variations of data, this paper proposes a unified dynamic framework of decision-theoretic rough sets for incrementally updating three-way probabilistic regions, namely, positive region, boundary region and negative region. Through improving the representation of three-way regions based on the well-established Bayesian decision procedure, a novel matrix approach is introduced by the construction of Boolean matrix and specific definition of matrix operation. Subsequently, at the variations of level-1, the fundamental updating propositions, which can induce the corresponding propositions with the variations of level-2, level-3, level-4, respectively, are presented by the matrix updating strategies. Finally, experiments with four incremental algorithms developed for the verification of feasibility and efficiency under multilevel variations of data are conducted by comparison with non-incremental algorithm.

Incremental learning strategy based feature selection approaches can improve the efficiency of reduction algorithm used for datasets with dynamic characteristic, which has attracted increasing research attention. Nevertheless, there is currently no work on incremental feature selection approaches for dynamic interval-valued ordered data. Interval-valued ordered data is a generalized form of single-valued ordered data, which is more widely used in practice. However, the endpoints of the interval numbers are easily polluted by noise, thereby the knowledge granules are very sensitive. Motivated by these two issues, we study incremental feature selection approaches based on a fuzzy dominance neighborhood rough set (FDNRS) for dynamic interval-valued ordered data in this work. First, we propose the FDNRS model for an interval-valued ordered decision system (IvODS) and investigate its related properties. Second, a conditional entropy with robustness is proposed based on the proposed model. This conditional entropy can measure the degree of monotonic consistency of the IvODS, so it is used as a metric and combined with a heuristic feature selection algorithm. Finally, two incremental feature selection algorithms are proposed on the basis of the above researches. Experiments are performed on nine public datasets to evaluate the robustness of the proposed metric and the performance of the incremental algorithms. Experimental results verify that the proposed metric is robust and our incremental algorithms are effective and efficient for updating reducts in dynamic IvODS.

Data in real-world applications are typically changing with time and are often incomplete. To address the challenge of processing such dynamic and incomplete data, we propose a model of dynamic probabilistic rough sets with incomplete data. We introduce incremental methods for estimating the conditional probability and present principles for updating probabilistic approximations when adding and removing objects, respectively. Based on the proposed updating strategies, algorithms are designed for dynamically updating probabilistic approximations with incomplete data. We report experimental evaluations of the efficiency and effectiveness of the proposed incremental algorithms for constructing probabilistic rough set approximations in terms of the size of data and updating ratio by comparing with a non-incremental algorithm. The results show that the new algorithms can effectively utilize the previously acquired knowledge, leading to significantly improved performance over a non-incremental algorithm.

The problem of finding a minimum vertex cover (MinVC) in a graph is a well known NP-hard combinatorial optimization problem of great importance in theory and practice. Due to its NP-hardness, there has been much interest in developing heuristic algorithms for finding a small vertex cover in reasonable time. Previously, heuristic algorithms for MinVC have focused on solving graphs of relatively small size, and they are not suitable for solving massive graphs as they usually have high-complexity heuristics. This paper explores techniques for solving MinVC in very large scale real-world graphs, including a construction algorithm, a local search algorithm and a preprocessing algorithm. Both the construction and search algorithms are based on low-complexity heuristics, and we combine them to develop a heuristic algorithm for MinVC called FastVC. Experimental results on a broad range of real-world massive graphs show that, our algorithms are very fast and have better performance than previous heuristic algorithms for MinVC. We also develop a preprocessing algorithm to simplify graphs for MinVC algorithms. By applying the preprocessing algorithm to local search algorithms, we obtain two efficient MinVC solvers called NuMVC2+p and FastVC2+p, which show further improvement on the massive graphs.

Combinatorial auction, which allows bidders to bid on combinations of items, is an important type of market mechanism. The winner determination problem (WDP) has extensive applications in combinatorial auctions, and attracts more and more attention due to its strong relevance to business. However, this problem is intractable in theory as it has been proven to be NP-hard, and is also a challenging combinatorial optimization problem in practice. This paper is devoted to designing an efficient heuristic algorithm for solving the WDP. This proposed heuristic algorithm dubbed abcWDP is based on an effective yet simple local search framework, and equipped with three novel strategies, i.e., configuration checking, free-bid exploiting, and pseudo-tie mechanism. Extensive computational experiments on a broad range of benchmarks demonstrate that abcWDP performs much better than state-of-the-art algorithms and CPLEX in terms of both revenue and running time. More encouragingly, our abcWDP algorithm as a sequential algorithm even achieves better computational results than the multi-thread implemented algorithm $$\hbox {CA}_\mathrm{RA}$$ , which confirms its efficiency.

Set-valued information systems are important type of data tables in many real applications, where the attribute values are described by sets to characterize uncertain and incomplete information. However, in some real situations, set-values may be depicted by probability distributions, which results in that the traditional tolerance relation based on intersection operation could not reasonably describe the indiscernibility relation of objects. To address this issue, we introduce the concept of probabilistic set-valued information systems (PSvIS), and present the extended variable precision rough set model (VPRS) based on the -tolerance relation in terms of Bhattacharyya distance. Considering the features of information systems will evolve over time in a dynamic data environment, it will lead to the change of information granulation and approximation structures. A matrix representation of rough approximation is presented based on two matrix operators and two vector functions in PSvIS. Then incremental mechanisms by the utilization of previously learned approximation results and region relation matrices for updating rough approximations are proposed, and the corresponding algorithms are developed and analyzed. Experimental results show that the proposed algorithms outperform the static algorithms and related incremental algorithms while inserting into or removing from attributes in PSvIS.

In a dynamic environment, the data collected from real applications varies not only with the amount of objects but also with the number of features, which will result in continuous change of knowledge over time. The static methods of updating knowledge need to recompute from scratch when new data are added every time. This makes it potentially very time-consuming to update knowledge, especially as the dataset grows dramatically. Calculation of approximations is one of main mining tasks in rough set theory, like frequent pattern mining in association rules. Considering the fuzzy descriptions of decision states in the universe under fuzzy environment, this paper aims to provide an efficient approach for computing rough approximations of fuzzy concepts in dynamic fuzzy decision systems (FDS) with simultaneous variation of objects and features. We firstly present a matrix-based representation of rough fuzzy approximations by a Boolean matrix associated with a matrix operator in FDS. While adding the objects and features concurrently, incremental mechanisms for updating rough fuzzy approximations are introduced, and the corresponding matrix-based dynamic algorithm is developed. Unlike the static method of computing approximations by updating the whole relation matrix, our new approach partitions it into sub-matrices and updates each sub-matrix locally by utilizing the previous matrix information and the interactive information of each sub-matrix to avoid unnecessary calculations. Experimental results on six UCI datasets shown that the proposed dynamic algorithm achieves significantly higher efficiency than the static algorithm and the combination of two reference incremental algorithms.

Weighted maximum satisfiability and (unweighted) partial maximum satisfiability (PMS) are two significant generalizations of maximum satisfiability (MAX-SAT), and weighted partial maximum satisfiability (WPMS) is the combination of the two, with more important applications in practice. Recently, great breakthroughs have been made on stochastic local search (SLS) for weighted MAX-SAT and PMS, resulting in several state-of-the-art SLS algorithms CCLS, Dist and DistUP. However, compared to the great progress of SLS on weighted MAX-SAT and PMS, the performance of SLS on WPMS lags far behind. In this paper, we present a new SLS algorithm named CCEHC for WPMS. CCEHC employs an extended framework of CCLS with a heuristic emphasizing hard clauses, called EHC. With strong accents on hard clauses, EHC has three components: a variable selection mechanism focusing on configuration checking based only on hard clauses, a weighting scheme for hard clauses, and a biased random walk component. Extensive experiments demonstrate that CCEHC significantly outperforms its state-of-the-art SLS competitors. Further experimental results on comparing CCEHC with a state-of-the-art complete solver show the effectiveness of CCEHC on a number of application WPMS instances, and indicate that CCEHC might be beneficial in practice. Also, empirical analyses confirm the effectiveness of each component underlying the EHC heuristic.

With the development of the Internet of Thingsź(IoT), more and more hybrid data is being collected by information systems, which are known as Hybrid Information Systemsź(HIS). Based on a new hybrid distance, novel Gaussian kernel Fuzzy Rough Setsź(FRS) for HIS were constructed in our previous study. In real-world applications, with the deepening of cognition and improvements in technology, attribute values in an information system often evolve over time; in particular, there are three cases: when missing values are imputed, error values are corrected, and the values are coarsened or refined. This has posed challenges to developing efficient data analysis algorithms. In this paper, the changing mechanisms of the attribute values and fuzzy equivalence relations in FRS are analyzed. FRS approaches for incrementally updating approximations in HIS are presented. Moreover, two corresponding incremental algorithms are developed. Finally, extensive experiments on eight data sets from the University of California, Irvine (UCI) and an artificial data set show that incremental approaches can effectively improve the performance of updating approximations and not only significantly shorten the computational time, but also increase approximation classification accuracies.

The fuzzy Information System over Two Universes (ISTU) formalizing a data table corresponding to two universes as well as their relations is common in real-world applications, e.g., recommender system and clinical diagnosis system. The fuzzy probabilistic rough sets over two universes (FPRSMTU) can deal with a fuzzy relation and allow a tolerance inaccuracy in the construction of rough approximations in a fuzzy ISTU, which is a generalization of classic rough sets with fuzzy and probabilistic theories. As a necessary step for knowledge discovery based on rough sets, the fuzzy rough approximations of fuzzy ISTU need to be updated efficiently under dynamic data environment. Incremental technique is an efficient approach for dynamic information processing by making full use of previously obtained knowledge. In this paper, incremental approaches for updating approximations of fuzzy ISTU are proposed while some objects adding into or deleting from the two universes, and the corresponding incremental algorithms are designed. Experimental evaluations on real datasets as well as artificial datasets show the effectiveness of the proposed incremental updating method compared with the non-incremental method.

In this paper, we consider the problem of computing the smallest enclosing circle. An efficient cutting plane algorithm is derived. It is based on finding the valid cut and reducing the problem into solving a series of linear programs. The numerical performance of this algorithm outperforms other existing algorithms in our computational experiments.

The amount of data collected from different real-world applications is increasing rapidly. When the volume of data is too large to be loaded to memory, it may be impossible to analyze it using a single computer. Although efforts have been taken to manage big data by using a single computer, the problem may not be solved in an acceptable time frame, making parallel computing an indispensable way to handle big data. In this paper, we investigate approaches to attribute reduction in parallel using dominance-based neighborhood rough sets (DNRS), which take into consideration the partial orders among numerical and categorical attribute values, and can be utilized in a multicriteria decision-making method. We first present some properties of attribute reduction in DNRS, and then investigate principles of parallel attribute reduction in DNRS. Parallelization on different components of attribute reduction are explored in detail. Furthermore, parallel attribute reduction algorithms in DNRS are proposed. Experimental results on UCI data and big data show that the proposed parallel algorithm is both effective and efficient.

Interval-valued Information System (IvIS) is a generalized model of single-valued information system, in which the attribute values of objects are all interval values instead of single values. The attribute set in IvIS is not static but rather dynamically changing over time with the collection of new information, which results in the continuous updating of rough approximations for rough set-based data analysis. In this paper, on the basis of the similarity-based rough set model in IvIS, we develop incremental approaches for updating rough approximations in IvIS under attribute generalization, which refers to the dynamic changing of attributes. Firstly, increment relationships between the original rough approximations and the updated ones when adding or deleting an attribute set are analyzed, respectively. And the incremental mechanisms for updating rough approximations in IvIS are introduced, which carry out the computation using the previous results from the original data set along with new results. Then, the corresponding incremental algorithms are designed based on the proposed mechanisms. Finally, comparative experiments on data sets from UCI as well as artificial data sets are conducted, respectively. Experimental results show that the proposed incremental algorithms can effectively reduce the running time for the computation of rough approximations in comparison with the static algorithm.

Ordered data widely exists in practical problems. Dominance-based Rough Set Approach (DRSA) is an effective mathematical tool to obtain approximations of concepts and discovery knowledge from ordered data. In this paper, we focus on the dynamic DRSA for the multi-dimensional variation of an ordered information system, and propose a novel incremental simplified algorithm which can efficiently update approximations of DRSA when objects and attributes increase simultaneously. Most of existing algorithms can efficiently deal with the single-dimensional variation of an information system. However, multi-dimensional variations often occur in real dynamic data. That is, the object set, the attribute set or attribute values of an information system often vary simultaneously. Although we can directly use the definitions of approximations, or integrate some single-dimensional incremental algorithms to cope with multi-dimensional variations, this always results in complex algorithm architectures and a large amount of inefficient computation. In our works, by simplifying traditional definitions of DRSA and employing the incremental learning strategy, we develop an algorithm to neglect unnecessary parameters and avoid much redundant computation. Then, we present two different storage schemes for our proposed algorithm to solve the problem of memory consumption. Finally, a series of experiments are conducted to evaluate its efficiency. Experimental results clearly show that for the two-dimensional variation of objects and attributes in an ordered information system, our proposed algorithm is much faster not only than the non-incremental algorithm based on traditional definitions, but also than the integration of two single-dimensional incremental algorithms.

© 2019 IEEE. Feature engineering is a crucial step for developing effective machine learning models. Traditionally, feature engineering is performed manually, which requires much domain knowledge and is time-consuming. In recent years, many automated feature engineering methods have been proposed. These methods improve the accuracy of a machine learning model by automatically transforming the original features into a set of new features. However, existing methods either lack ability to perform high-order transformations or suffer from the feature space explosion problem. In this paper, we present Neural Feature Search (NFS), a novel neural architecture for automated feature engineering. We utilize a recurrent neural network based controller to transform each raw feature through a series of transformation functions. The controller is trained through reinforcement learning to maximize the expected performance of the machine learning algorithm. Extensive experiments on public datasets illustrate that our neural architecture is effective and outperforms the existing state-of-the-art automated feature engineering methods. Our architecture can efficiently capture potentially valuable high-order transformations and mitigate the feature explosion problem.

Combinatorial interaction testing (CIT) is a popular approach to detecting faults in highly configurable software systems. The core task of CIT is to generate a small test suite called a t-way covering array (CA), where t is the covering strength. Many meta-heuristic algorithms have been proposed to solve the constrained covering array generating (CCAG) problem. A major drawback of existing algorithms is that they usually need considerable time to obtain a good-quality solution, which hinders the wider applications of such algorithms. We observe that the high time consumption of existing meta-heuristic algorithms for CCAG is mainly due to the procedure of score computation. In this work, we propose a much more efficient method for score computation. The score computation method is applied to a state-of-the-art algorithm TCA, showing significant improvements. The new score computation method opens a way to utilize algorithmic ideas relying on scores which were not affordable previously. We integrate a gradient descent search step to further improve the algorithm, leading to a new algorithm called FastCA. Experiments on a broad range of real-world benchmarks and synthetic benchmarks show that, FastCA significantly outperforms state-of-the-art algorithms for CCAG algorithms, in terms of both the size of obtained covering array and the run time.

Data uncertainty has become increasingly important owing to the dynamic and incomplete characteristics of data. The selection of nonredundant features from such uncertain data is a highly challenging problem. Information theory can help us to make qualitative measures about the uncertainty for analyzing data, which has been widely used to feature selection task. In this paper, an incremental feature selection approach from information-theoretic perspective for dynamic incomplete data is presented. By utilizing the updating mechanisms of classification induced by conditional and decision features, a novel incremental representation of Shannon's entropy for incomplete data is proposed to accelerate the computation of feature significance in heuristic searching process. Efficient incremental feature selection algorithm is developed when the incomplete data increase dynamically in size. Theoretical justifications and illustrative examples are provided to demonstrate the validity of the proposed algorithm.

Minimum vertex cover (MinVC) is a prominent NP-hard problem in artificial intelligence, with considerable importance in applications. Local search solvers define the state of the art in solving MinVC. However, there is no single MinVC solver that works best across all types of MinVC instances, and finding the most suitable solver for a given application poses considerable challenges. In this work, we present a new local search framework for MinVC called MetaVC, which is highly parametric and incorporates many effective local search techniques. Using an automatic algorithm configurator, the performance of MetaVC can be optimized for particular types of MinVC instances. Through extensive experiments, we demonstrate that MetaVC significantly outperforms previous solvers on medium-size hard instances, and shows competitive performance on large application instances. We further introduce a novel, neural network based method for enhancing the automatic configuration process, by identifying and terminating unpromising configuration runs. Our results present that MetaVC with the optimized configuration obtained using this method, called MetaVC2, can achieve improvements in the best known solutions for 16 large application instances.

Due to the development of social media technologies, abundant online platforms have been established to facilitate users’ knowledge exchange behaviors. With frequent user interactions, these platforms gradually evolve into virtual communities, and users subsequently form a sense of commitment. Nevertheless, previous research has ignored the effects of commitment on knowledge-sharing behaviors. This study thus endeavors to fill this research gap by exploring the effect of commitment on knowledge contributors’ sharing intentions in virtual communities in China. By integrating the commitment model and social exchange theory, we demonstrate that user commitment mediates the effect of social-exchange benefits on users’ knowledge exchange behaviors in virtual communities. Our research model is validated using 751 valid responses collected from four prevalent virtual communities through an online survey. The statistical results show that affective and normative commitment can significantly influence users’ knowledge-sharing intention. In addition, relationships, reputation, and reciprocity, as three types of social-exchange benefits, serve as crucial antecedents to user commitment. Our findings contribute to the literature on commitment and virtual communities by shedding important light on the different components of user commitment and their distinctive mediating roles in knowledge sharing.

Maximum Satisfiability (MaxSAT) is the optimization version of the Satisfiability (SAT) problem. Partial Maximum Satisfiability (PMS) is a generalization of MaxSAT which involves hard and soft clauses and has important real world applications. Local search is a popular approach to solving SAT and MaxSAT and has witnessed great success in these two problems. However, unfortunately, local search algorithms for PMS do not benefit much from local search techniques for SAT and MaxSAT, mainly due to the fact that it contains both hard and soft clauses. This feature makes it more challenging to design efficient local search algorithms for PMS, which is likely the reason of the stagnation of this direction in more than one decade. In this paper, we propose a number of new ideas for local search for PMS, which mainly rely on the distinction between hard and soft clauses. The first three ideas, including weighting for hard clauses, separating hard and soft score, and a variable selection heuristic based on hard and soft score, are used to develop a local search algorithm for PMS called Dist. The fourth idea, which uses unit propagation with priority on hard unit clauses to generate the initial assignment, is used to improve Dist on industrial instances, leading to the DistUP algorithm. The effectiveness of our solvers and ideas is illustrated through experimental evaluations on all PMS benchmarks from the MaxSAT Evaluation 2014. According to our experimental results, Dist shows a significant improvement over previous local search solvers on all benchmarks. We also compare our solvers with state-of-the-art complete PMS solvers and a state-of-the-art portfolio solver, and the results show that our solvers have better performance in random and crafted instances but worse in industrial instances. The good performance of Dist has also been confirmed by the fact that Dist won all random and crafted categories of PMS and Weighted PMS in the incomplete solvers track of the MaxSAT Evaluation 2014.

Drawing on the theoretical lens of communication patterns in organizational theory, this research analyzed the longitudinal success of open source software (OSS) projects by employing social network analysis method, based on extensive analyses of empirical data. This study is expected to provide an understanding on how communication patterns established in different roles and different levels. The authors not only measured OSS success from both developers and users' perspectives, but also extended the existing research by including the potential relationships among these success measures in the estimation model. Following the panel data econometric analysis methodology, they evaluated their research hypotheses using the Three-Stage Least Squares model, accounting for both time-period and project fixed effects. The authors' results indicated that according to the objectives of projects, a proper and planned control for the communication among team members is crucial for the success of OSS projects.

Probabilistic rough set model, which is established by incorporating the theory of probability into rough set theory, aims to model imprecise data with the tolerance of decision errors in terms of conditional probability and probabilistic parameters. The volume of data is frequently varied dynamically. It is very time consuming to analyze the updates of data incessantly from computation perspective. Incremental learning technique is desired to improve computational efficiency, which poses an incremental capability for adaptive knowledge maintenance to accommodate varied data. In this paper, we focus on efficient updating of probabilistic approximations with incremental objects in a dynamic information table. The dynamic characteristics of conditional partition and decision classification on the universe are analyzed when the insertion or deletion of objects occurs. Different updating patterns of conditional probability are presented for different combinatorial varieties of the conditional and decision classes. Meanwhile, incremental algorithms for updating probabilistic approximations are proposed, which are proficient to efficiently classify the incremental objects into decision regions by avoiding re-computation efforts. Experiments on benchmark data sets indicate that the proposed algorithms outperform the static algorithm in the presence of dynamic variation of the universe.

Rough set provides a theoretical framework for classification learning in data mining and knowledge discovery. As an important application of rough set, attribute reduction, also called feature selection, aims to reduce the redundant attributes in a given decision system while preserving a particular classification property, e.g., information entropy and knowledge granularity. In view of the dynamic changes of the object set in a decision system, in this paper, we focus on knowledge granularity-based attribute reduction approach when some objects vary dynamically. We first introduce incremental mechanisms to compute new knowledge granularity. Then, the corresponding incremental algorithms for attribute reduction are developed when some objects are added into and deleted from the decision system. Experiments conducted on different data sets from UCI show that the proposed incremental algorithm can achieve better performance than the non-incremental counterpart and incremental algorithm based on entropy.

Decision-theoretic rough sets is a generalized probabilistic model for the expression of uncertainties and the representation of knowledge from data. It provides a semantic explanation and systematically computation of probabilistic thresholds to define probabilistic rough set approximations, which offers a ternary classification framework based on Bayesian decision theory. In practice, data for decision making process resides in a dynamic database whose data is typically evolving through the periodical or occasional updating, e.g., new data are appended and obsolete data are removed. It is impractical to have a maturity decision model, stalled until the preparation of all helpful training data. To address this issue, incremental learning appeared to be a feasible solution for continuous knowledge modeling from evolving data with the incorporation of unlearned knowledge embedded in the updating data. In this paper, we exploit matrix approaches to study incremental decision-theoretic rough set approach for evolving data. Starting from the representation of object subset and indiscernibility relation in matrix form, we obtain a matrix characterization of probabilistic rough set approximations in decision-theoretic rough sets by using matrix properties associated with the multiplication operator. We also develop incremental algorithms for updating probabilistic rough set approximations with respect to the addition/deletion of objects, which enables decision theoretic rough sets to deal gracefully with evolving data. A detailed experimental study is conducted to examine the performance of the proposed incremental algorithms on UCI data sets.

In this paper, we present a composite framework of sequential three-way decisions to deal with hybrid data based on the fusion of different granularities. According to the top-down manner, we construct a multilevel composite granular structure by the addition of a new attribute type, and define a general composite binary relation based on three kinds of fusion strategies. At each level, the particular regions including seven selections are considered to induce the acceptance, non-commitment, and rejection rules. Some uncertain objects may be further investigated by more types of attributes at the next level. In this way, such multilevel processing of hybrid data naturally leads to the composite sequential three-way decisions.

Weighted partial maximum satisfiability (WPMS) is a significant generalization of maximum satisfiability (MAXSAT), weighted maximum satisfiability (weighted MAX-SAT) and unweighted partial maximum satisfiability (PMS), and WPMS can be widely used in real-world application domains. Recently, great breakthroughs have been made on stochastic local search (SLS) for solving MAX-SAT, weighted MAX-SAT, PMS and WPMS, resulting in several state-of-the-art SLS algorithms, such as CCLS, Dist and CCEHC. Indeed, boosting the practical performance on solving WPMS is of great interest, and the performance of SLS algorithms on solving WPMS could be further improved. In this paper, we follow this research direction, and propose a new SLS algorithm named CCHNV for solving WPMS. CCHNV adopts the framework of CCEHC, and employs a new forbidding strategy of configuration checking, named hard neighboring variables based configuration checking (HNVCC). Extensive experiments on a broad range of WPMS instances present that CCHNV pushes forward the state-of-the-art performance of SLS algorithms on solving WPMS, and is complementary to a state-of-the-art complete algorithm for solving WPMS.