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Information fusion refers to derive an overall precise description of data by using certain fusion technique for utilizing the complementary information from multiple sources of data, which can facilitate effective decision-making, prediction and classification, etc. Multi-source homogeneous data, characterizing the data type of variables in the sample in form of one type (i.e., numerical or categorical) in different information sources, which widely exists in many practical applications. This paper concentrates on efficient fusion of multi-source homogeneous data with a data-level fusion model which involves the consolidation of multiple information sources and unsupervised attribute selection of the fused data. A unified description and modeling method of a multi-source homogeneous information system is introduced. The neighborhood rough sets model is used to construct the neighborhood granular structure, which uses the idea of granular computing to build methods of uncertainty measures. Given the uncertainty of fusing multiple information sources, Sup-Inf fusion functions are developed based on the proposed uncertainty measures, which can fuse the multi-source homogeneous information system into a single-source information system. Finally, an unsupervised attribute selection approach is employed to eliminate redundant attribute of the single-source information system. Theoretical analysis and comprehensive experiments on several datasets demonstrate the feasibility and superiority of our method.

The Multi-Granulation Decision-Theoretic Rough Set (MG-DTRS) is an effective method for cost-sensitive decision making from multi-view and multi-level. However, the inherent weak point of MG-DTRS model is to compute three regions with a subjectively given pair of probabilistic parameters (i.e., α and β). To overcome this issue, this paper first proposes a generalized MG-DTRS model called Adaptive Multi-Granulation Decision-Theoretic Rough Sets (AMG-DTRS), which can adaptively obtain a pair of probabilistic thresholds by setting a compensation coefficient ζ. Then, three types of mean AMG-DTRS models are investigated, which provide a novel perspective on multi-granulation method for information fusion. Finally, the connections and differences between the proposed AMG-DTRS and the existing MGRS models are analyzed, which show the advantages and generalization of the AMG-DTRS model. In addition, there are numerous existing MGRS models can be derived explicitly by considering various MG-DTRS, MGRS and VP-MGRS based on our model. These results will be conducive to establishing the framework of information fusion for granular computing.

Mining knowledge from imbalanced data is challenging due to the uneven distribution of classes and increasing dimensionality of data accumulated from real-life applications. Selecting informative features from imbalanced data is especially important for building an effective learning method. The global redundancy and the effect of imbalanced distribution need to be considered simultaneously. In this study, a feature selection method that considers the imbalanced distribution of classes in data is investigated by embedding the weighted constraint on the majority class into the global redundancy minimization GRM framework. Global redundancy minimization is acquired through an objective function that contains a feature redundancy matrix and feature scores. A new form of regularization to a within-class scatter matrix is first presented, which emphasizes the minority class and replaces the redundancy measurement approach. Then, after employing this new form of a within-class scatter matrix in GRM and taking the between-class distance as the GRM input score, a GRM-based discriminant feature selection algorithm (GRM-DFS) is proposed. Comparison studies on a within-class scatter matrix with different forms of regularization indicate that the proposed form of a within-class scatter matrix is effective when dealing with imbalanced data. Experiments on public imbalanced datasets are performed. The experimental results indicate that GRM-DFS is effective.

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.

In order to more accurately simulate the injury of riders and the motion characteristics of two-wheelers, the modeling method and the simulation model of two-wheeler traffic accidents are studied i...

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.

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.

Maximum vertex weight clique problem (MVWCP) and maximum edge weight clique problem (MEWCP) are two significant generalizations of maximum clique problem (MCP), and can be widely used in many real-world applications including molecular biology, broadband network design and pattern recognition. Recently, breakthroughs have been made for solving MVWCP in large graphs, resulting in several state-of-the-art algorithms, such as WLMC, FastWClq and LSCC + BMS. However, less attention has been paid to solving MEWCP in large graphs. In this paper, we present an efficient Stochastic Local Search (SLS) algorithm for MEWCP by combining clique construction, local search and graph reduction, resulting in a new algorithm named ReConSLS. We also propose a new upper bound function for edge weighted graphs which is essential for graph reduction. Extensive experiments on a wide range of large graphs demonstrate that ReConSLS surpasses state-of-the-art SLS competitors on the majority of testing graphs.

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.

The use of containers in microservices has gained popularity as it facilitates agile development, resource governance, and software maintenance. Container reallocation aims to achieve workload balance via reallocating containers over physical machines. It affects the overall performance of microservice-based systems. However, container scheduling and reallocation remain an open issue due to their complexity in real-world scenarios. In this paper, we propose a novel Multi-Phase Local Search (MPLS) algorithm to optimize container reallocation. The experimental results show that our optimization algorithm outperforms state-of-the-art methods. In practice, it has been successfully applied to Microsoft 365 system to mitigate hotspot machines and balance workloads across the entire system.

In cloud systems, an accurate capacity planning is very important for cloud provider to improve service availability. Traditional methods simply predicting "when the available resources is exhausted" are not effective due to customer demand fragmentation and platform allocation constraints. In this paper, we propose a novel prediction approach which proactively predicts the level of resource allocation failures from the perspective of low capacity status. By jointly considering the data from different sources in both time series form and static form, the proposed approach can make accurate LCS predictions in a complex and dynamic cloud environment, and thereby improve the service availability of cloud systems. The proposed approach is evaluated by real-world datasets collected from a large scale public cloud platform, and the results confirm its effectiveness.

There has been a rapidly increasing demand for developing highly configurable software systems, which urgently calls for effective testing methods. In practice, t-wise coverage has been widely recognized as a useful metric to evaluate the quality of a test suite for testing highly configurable software systems, and achieving high t-wise coverage is important for ensuring test adequacy. However, state-of-the-art methods usually cost a fairly long time to generate large test suites for high pairwise coverage (i.e., 2-wise coverage), which would lead to ineffective and inefficient testing of highly configurable software systems. In this paper, we propose a novel local search based sampling approach dubbed LS-Sampling for achieving high t-wise coverage. Extensive experiments on a large number of public benchmarks, which are collected from real-world, highly configurable software systems, show that LS-Sampling achieves higher 2-wise and 3-wise coverage than the current state of the art. LS-Sampling is effective, since on average it achieves the 2-wise coverage of 99.64% and the 3-wise coverage of 97.87% through generating a small test suite consisting of only 100 test cases (90% smaller than the test suites generated by its state-of-the-art competitors). Furthermore, LS-Sampling is efficient, since it only requires an average execution time of less than one minute to generate a test suite with high 2-wise and 3-wise coverage.

Capacity management has always been a great challenge for cloud platforms due to massive, heterogeneous on-demand instances running at different times. To better plan the capacity for the whole platform, a class of cloud computing instances have been released to collect computing demands beforehand. To use such instances, users are allowed to submit jobs to run for a pre-specified uninterrupted duration in a flexible range of time in the future with a discount compared to the normal on-demand instances. Proactively scheduling those pre-collected job requests considering the capacity status over the platform can greatly help balance the computing workloads along time. In this work, we formulate the scheduling problem for these pre-collected job requests under uncertain available capacity as a Prediction + Optimization problem with uncertainty in constraints, and propose an effective algorithm called Controlling under Uncertain Constraints (CUC), where the predicted capacity guides the optimization of job scheduling and job scheduling results are leveraged to improve the prediction of capacity through Bayesian optimization. The proposed formulation and solution are commonly applicable for proactively scheduling problems in cloud computing. Our extensive experiments on three public, industrial datasets shows that CUC has great potential for supporting high reliability in cloud platforms.

Decomposition approach is an important component in multi-objective evolutionary algorithm based on decomposition (MOEA/D), which is a popular method for handing many-objective optimization problems (MaOPs). This paper presents a theoretical analysis on the convergence ability of using the typical weighted sum (WS), Tchebycheff (TCH) or penalty-based boundary intersection (PBI) approach in a basic MOEA/D for solving two benchmark MaOPs. The results show that using WS, the algorithm can always find an optimal solution for any subproblem in polynomial expected runtime. In contrast, the algorithm needs at least exponential expected runtime for some subproblems if using TCH or PBI. Moreover, our analyses discover an obvious shortcoming of using WS, that is, the optimal solutions of different subproblems are easily corresponding to the same solution. In addition, this analysis indicates that if using PBI, a small value of the penalty parameter is a good choice for faster converging to the Pareto front, but it may lose the diversity. This study reveals some optimization behaviors of using three typical decomposition approaches in the well-known MOEA/D framework for solving MaOPs.

Click-through rate (CTR) prediction aims to recall the advertisements that users are interested in and to lead users to click, which is of critical importance for a variety of online advertising systems. In practice, CTR prediction is generally formulated as a conventional binary classification problem, where the clicked advertisements are positive samples and the others are negative samples. However, directly treating unclicked advertisements as negative samples would suffer from the severe label noise issue, since there exist many reasons why users are interested in a few advertisements but do not click. To address such serious issue, we propose a reinforcement learning based noise filtering approach, dubbed RLNF, which employs a noise filter to select effective negative samples. In RLNF, such selected, effective negative samples can be used to enhance the CTR prediction model, and meanwhile the effectiveness of the noise filter can be enhanced through reinforcement learning using the performance of CTR prediction model as reward. Actually, by alternating the enhancements of the noise filter and the CTR prediction model, the performance of both the noise filter and the CTR prediction model is improved. In our experiments, we equip 7 state-of-the-art CTR prediction models with RLNF. Extensive experiments on a public dataset and an industrial dataset present that RLNF significantly improves the performance of all these 7 CTR prediction models, which indicates both the effectiveness and the generality of RLNF.

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.

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.

Networks are ubiquitous in the real world. Link prediction, as one of the key problems for network-structured data, aims to predict whether there exists a link between two nodes. The traditional approaches are based on the explicit similarity computation between the compact node representation by embedding each node into a low-dimensional space. In order to efficiently handle the intensive similarity computation in link prediction, the hashing technique has been successfully used to produce the node representation in the Hamming space. However, the hashing-based link prediction algorithms face accuracy loss from the randomized hashing techniques or inefficiency from the learning to hash techniques in the embedding process. Currently, the Graph Neural Network (GNN) framework has been widely applied to the graph-related tasks in an end-to-end manner, but it commonly requires substantial computational resources and memory costs due to massive parameter learning, which makes the GNN-based algorithms impractical without the help of a powerful workhorse. In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. #GNN is able to efficiently acquire node representation in the Hamming space for link prediction by exploiting the randomized hashing technique to implement message passing and capture high-order proximity in the GNN framework. Furthermore, we characterize the discriminative power of #GNN in probability. The extensive experimental results demonstrate that the proposed #GNN algorithm achieves accuracy comparable to the learning-based algorithms and outperforms the randomized algorithm, while running significantly faster than the learning-based algorithms. Also, the proposed algorithm shows excellent scalability on a large-scale network with the limited resources.

In cloud computing, provisioning virtual machines (VMs) fast and reliably is a fundamental yet challenging problem, particularly in cloud environments of changing workloads and shifting demand patterns. A trivial solution of instantiating a VM from scratch per customer request may not satisfy business SLA's and would degrade customer experience. Hence, provisioning VMs in advance motivated with machine learning (ML) infused into the cloud computing system to predict upcoming VM request demands is an advisable solution. In this paper, we first describe a number of system integration challenges including 1) how to achieve low latency provisioning to quickly adjust to customer demand pattern shifts, 2) how to efficiently scale to serve a large number of VM configurations supported in the cloud environment, and 3) how to reliably consume recommended prediction results for VM provision despite of anticipated operation failures and timeout. We then present the high level solution design with discussions of our developed system to address the aforementioned challenges. Our system has been implemented and deployed successfully in Microsoft Azure exhibiting significant improvements for VM provisioning experience with regards to latency and reliability requirements.

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. A major drawback of existing solvers for CA generation is that they usually need considerable time to obtain a high-quality solution, which hinders its wider applications. In this paper, we describe FastCA, an effective and efficient tool for generating constrained CAs. We observe that the high time consumption of existing meta-heuristic algorithms is mainly due to the procedure of score computation. To this end, we present a much more efficient method for score computation. Thanks to this new lightweight score computation method, FastCA can work in the gradient mode to effectively explore the search space. Experiments on a broad range of real-world benchmarks and synthetic benchmarks show that FastCA significantly outperforms state-of-the-art solvers, in terms of both the size of obtained covering array and the run time. Video: https://youtu.be/-6CuojQIt-k Repository: https://github.com/jkunlin/FastCATool.git

Combinatorial interaction testing (CIT) is an important technique for testing highly configurable software systems with demonstrated effectiveness in practice. The goal of CIT is to generate test cases covering the interactions of configuration options, under certain hard constraints. In this context, constrained covering arrays (CCAs) are frequently used as test cases in CIT. Constrained Covering Array Generation (CCAG) is an NP-hard combinatorial optimization problem, solving which requires an effective method for generating small CCAs. In particular, effectively solving t-way CCAG with t ≥ 4 is even more challenging. Inspired by the success of automated algorithm configuration and automated algorithm selection in solving combinatorial optimization problems, in this paper, we investigate the efficacy of automated algorithm configuration and automated algorithm selection for the CCAG problem, and propose a novel, automated CCAG approach called AutoCCAG. Extensive experiments on public benchmarks show that AutoCCAG can find much smaller-sized CCAs than current state-of-the-art approaches, indicating the effectiveness of AutoCCAG. More encouragingly, to our best knowledge, our paper reports the first results for CCAG with a high coverage strength (i.e., 5-way CCAG) on public benchmarks. Our results demonstrate that AutoCCAG can bring considerable benefits in testing highly configurable software systems.

With millions of customers accessing online service all over the world, ensuring high service availability is very critical for cloud system. In recent years, empowered by advanced data mining and machine learning technology, there emerges extensive study on data-driven solution to detect anomalous system behavior and diagnose the root cause. However, without any surveilance of data generation process, the existing passive data-driven approach may lead to biased analysis result induced by observed and unobserved confounding factors in the dynamic and heterogeneous system, and thus affect service availability with misleading mitigation actions. In this paper, we propose to infuse causal thinking to the current data-driven solution for cloud system. We developed CARE, a causal aware root cause discovery engine, which utilizes Random Control Trial to proactively generate less ambiguous data for further analysis. A case study shows the application of CARE to Microsoft Office365.

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.

Attribute reduction is a significant process of data preprocessing to overcome the challenges posed by multidimensional data analysis. Missing values in the data are usually unavoidable in the real applications, so it is important to select features with high importance efficiently in incomplete data. The theory of rough sets has been widely used in attribute reduction for uncertain data mining. To enable the rough set theory for large-scale incomplete data analysis, this paper develops a novel distributed attribute reduction algorithm based on Apache Spark cluster computing platform. By taking the advantage of positive approximation technique for reducing the data broadcasting gradually while reducing each redundant attribute iteratively, the proposed algorithm can significantly accelerate the attribute reduction in leveraging a computer cluster when processing large-scale incomplete data. Numerical experiments on different UCI data sets evidences the proposed parallel algorithm achieves high performance in terms of extensibility and scalability.

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.

The real-world high-dimensional data lie on low-dimensional manifolds embedded within the high-dimensional space. Therefore, clustering in high-dimensional spaces is a difficult problem. Subspace-based clustering methods are proposed to project the high dimensional data into a low-dimensional space, and then find clusters in this low-dimensional subspaces of the high dimensional data, instead of finding clusters in the entire feature space. In this work, we propose a subspace clustering method called Sparse Convolution Subspace Clustering (SCSC) which is inspired by Sparse Subspace Clustering (SSC). SSC is to find a sparse representations of a data point in terms of other points while SCSC tries to find a sparse convolutional representations of a data point in terms of other points. A group optimization method based alternating direction method of multipliers (ADMM) is used to solve the sparse convolutional representation problem. It should be pointed out that SSC is a special case of SCSC while the convolution kernel size is set as $1 \times 1$. The experimental results on face data show the effectiveness of the proposed SCSC.

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.

Discrete Material Optimization (DMO) has proven to be an effective framework for optimizing the orientation of orthotropic laminate composite panels across a structural design domain. The typical design problem is one of maximizing stiffness by assigning a fiber orientation to each subdomain, where the orientation must be selected from a set of discrete magnitudes (e.g., 0°, ±45°, 90°). The DMO approach converts this discrete problem into a continuous formulation where a design variable is introduced for each candidate orientation. Local constraints and SIMP style penalization are then used to ensure each subdomain is assigned a single orientation in the final solution. The subdomain over which orientation is constant is typically defined as a finite element, ultimately leading to complex orientation layouts that may be difficult to manufacture. Recent literature has introduced threshold projections, originally developed for density-based topology optimization, into the DMO approach in order to influence the manufacturability of solutions. This work takes this idea one step further and utilizes the Heaviside Projection Method within DMO to provide formal control over the minimum length scale of structural features, holes, and patches of uniform orientation. The proposed approach is demonstrated on benchmark maximum stiffness design problems in terms of objective function, solution discreteness, and manufacturability. Numerical results suggest that projection-based methods can play an important role in controlling the manufacturability of optimized material distributions in optimized design and that solutions are near-discrete with performance properties comparable to designs without manufacturing considerations.

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.

Satisfiability (SAT) and Maximum Satisfiability (MaxSAT) are two basic and important constraint problems with many important applications. SAT and MaxSAT are expressed in CNF, which is difficult to deal with cardinality constraints. In this paper, we introduce Extended Conjunctive Normal Form (ECNF), which expresses cardinality constraints straightforward and does not need auxiliary variables or clauses. Then, we develop a simple and efficient local search solver LS-ECNF with a well designed scoring function under ECNF. We also develop a generalized Unit Propagation (UP) based algorithm to generate the initial solution for local search. We encode instances from Nurse Rostering and Discrete Tomography Problems into CNF with three different cardinality constraint encodings and ECNF respectively. Experimental results show that LS-ECNF has much better performance than state of the art MaxSAT, SAT, Pseudo-Boolean and ILP solvers, which indicates solving cardinality constraints with ECNF is promising.

The mobile news application is a brand-new form of expression of new media. It is the latest form of media carrier in the development of mobile network news reading. With its unique advantages, it provides users with continuous, timely and overall news information, and brings great commercial value to businesses. Based on two management models and the new media characteristics of mobile news app this paper constructs the user stickiness of the news apps influence factors model from different perspectives of product factors and user factors; uses the questionnaire survey method, understands the mobile news apps user experience satisfaction aspects and the mobile news apps present existence questions, takes the existing users of mobile Tencent News app as the research object and conducts empirical research. Through measurement model analysis and structural equation model analysis, it explores the influencing factors of user stickiness.

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.

Small pixel pitch with a large array size is highly anticipated for solid-state lidar transmitter and receiver. We demonstrate 2D beam steering with reduced effective pixel pitch by harnessing transitional state of the mirror array.

Incorporating de-sensitization to tolerance in optimization process effectively balances MTF and manufacturability for an F/2 wide angle and broadband camera for space applications for which materials are limited to radiation hard glasses.

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.

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.

Harris corner detection algorithm has been widely used in many computer vision allocations. However, it has low efficiency and accuracy, poor noise immunity and needs to set an artificial threshold. In this paper, an improved algorithm based on Canny edge detection and gray difference preprocessing is proposed. Firstly, Canny edge detection and gray difference preprocessing are used for corner prescreening to improve the detection efficiency, anti-noise, and rotation invariance. Secondly, non-maximum suppression is applied to the screened corners to reduce the number of false corners. Finally, the average of adjacent points method is used to solve the problem of corner cluster, and the detection results are compared the measurement accuracy is improved to sub-pixel level. Experimental results indicate that the proposed algorithm can accurately extract the corners in the image and remove the false corners and corner clusters. It achieves superior performance than existing methods.

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.

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.

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.

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.