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Along with the development of manufacture and services, the problem of distribution network optimization has been growing in importance, thus receiving much attention from the research community. One of the most recently introduced network optimization problems is the Clustered Shortest-Path Tree Problem (CluSTP). Since the problem is NP-Hard, recent approaches often prefer to use approximation algorithms to solve it, several of which used Evolutionary Algorithms (EAs) and have been proven to be effective. However, most of the prior studies directly applied EAs to the whole CluSTP problem, which leads to a great amount of resource consumption, especially when the problem size is large. To overcome these limitations, this paper suggests a method for reducing the search space of the EAs applied to CluSTP by decomposing the original problem into two sub-problems, the solution to one of which is found by an EAs and that to the other is found by another method. The goal of the first sub-problem is to determine a spanning tree which connects among the clusters, while the goal of the second sub-problem is to determine the best spanning tree for each cluster. In addition, this paper proposes a new EAs, which can be applied to solve the first sub-problem and suggests using the Dijkstra's algorithm to solve the second sub-problem. The proposed approach is comprehensively experimented and compared with existing methods. Experimental results prove that our method is more efficient and more importantly, it can obtain results which are close to the optimal results., 27 pages, 7 figures

The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that was shown to have excellent performance for numerous optimization problems. In this paper, a wrapper-feature selection algorithm is proposed based on the Binary Dragonfly Algorithm (BDA). The key component of the BDA is the transfer function that maps a continuous search space to a discrete search space. In this study, eight transfer functions, categorized into two families (S-shaped and V-shaped functions) are integrated into the BDA and evaluated using eighteen benchmark datasets obtained from the UCI data repository. The main contribution of this paper is the proposal of time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation. During the early stages of the optimization process, the probability of changing the position of an element is high, which facilitates the exploration of new solutions starting from the initial population. On the other hand, the probability of changing the position of an element becomes lower towards the end of the optimization process. This behavior is obtained by considering the current iteration number as a parameter of transfer functions. The performance of the proposed approaches is compared with that of other state-of-art approaches including the DA, binary grey wolf optimizer (bGWO), binary gravitational search algorithm (BGSA), binary bat algorithm (BBA), particle swarm optimization (PSO), and genetic algorithm in terms of classification accuracy, sensitivity, specificity, area under the curve, and number of selected attributes. Results show that the time-varying S-shaped BDA approach outperforms compared approaches.

This paper addresses the problem of a finite set of entities which are required to achieve a goal within a predefined deadline. For example, a group of students is supposed to submit a homework by a specified cutoff. Further, we are interested in predicting which entities will achieve the goal within the deadline. The predictive models are built based only on the data from that population. The predictions are computed at various time instants by taking into account updated data about the entities. The first contribution of the paper is a formal description of the problem. The important characteristic of the proposed method for model building is the use of the properties of entities that have already achieved the goal. We call such an approach “Self-Learning”. Since typically only a few entities have achieved the goal at the beginning and their number gradually grows, the problem is inherently imbalanced. To mitigate the curse of imbalance, we improved the Self-Learning method by tackling information loss and by several sampling techniques. The original Self-Learning and the modifications have been evaluated in a case study for predicting submission of the first assessment in distance higher education courses. The results show that the proposed improvements outperform the specified two base-line models and the original Self-Learner, and also that the best results are achieved if domain-driven techniques are utilised to tackle the imbalance problem. We also showed that these improvements are statistically significant using Wilcoxon signed rank test.

This paper develops an integrated trapezoidal interval type-2 fuzzy (TrIT2F) technique for democratic–autocratic multi-criteria group decision making based on best–worstmethod (BWM) and VIKOR (VIsekriterijumska optimizacija i KOm-promisno Resenje), which is called TrIT2F-BW-VIKOR. In this technique, the pairwise comparisons and evaluations are represented by trapezoidal interval type-2 fuzzy sets (TrIT2FSs). The existing definition of TrIT2FS is perfected by adding two rational constraints proposed in this paper. A weight-normalizing theorem is initiated to normalize the TrIT2F weights. To determine the TrIT2F weights of junior decision makers (JDMs) and criteria, the classical BWM is extended into TrIT2F environment, which is called TrIT2F-BWM. In this TrIT2F-BWM, the weight-normalizing theorem is applied to normalize the TrIT2F weights, a consistency ratio is designed to check the reliability of the obtained TrIT2F weights. Based on the determined weights of JDMs and criteria, an extended VIKOR is developed to rank alternatives. The proposed technique can not only effectively retain the inherent fuzzy information of TrIT2FSs, but also flexibly handle different decision situations. The validity of the proposed technique is demonstrated with a makeshift (fangcang) hospital selection example on COVID-19. Some sensitivity and comparison analyses are provided to show the stability, flexibility, and superiorities of the proposed technique.

In this paper, we build a recommender system based on Long-term Cognitive Networks (LTCNs), which are a type of recurrent neural network that allows reasoning with prior knowledge structures. Given that our approach is context-free and that we did not involve human experts in our study, the prior knowledge is replaced with Pearson's correlation coefficients. The proposed architecture expands the LTCN model by adding Gaussian kernel neurons that compute estimates for the missing ratings. These neurons feed the recurrent structure that corrects the estimates and makes the predictions. Moreover, we present an extension of the non-synaptic backpropagation algorithm to compute the proper non-linearity of each neuron together with its activation boundaries. Numerical results using several case studies have shown that our proposal outperforms most state-of-the-art methods. Towards the end, we explain how can we inject expert knowledge to the proposed neural system. The authors would like to thank the anonymous reviewers for their valuable and constructive feedback. This paper was partially supported by the Program FONDECYT de Postdoctorado, Chile through the project 3200284.

This paper deals with a cost sensitive extension of the standard Support Vector Machine (SVM) using an ordered weighted sum of the deviations of misclassified individuals with respect to their corresponding supporting hyperplanes. In contrast with previous heuristic approaches, an exact method that applies the ordered weighted average operator in the classical SVM model is proposed. Specifically, when weights are sorted in non-decreasing order, a quadratic continuous formulation is developed. For general weights, a mixed integer quadratic formulation is proposed. In addition, our results prove that nonlinear kernel functions can be also applied to these new models extending its applicability beyond the linear case. Extensive computational results reported in the paper show that the predictive performance provided by the proposed exact solution approaches are better than the ones provided by the classical models (linear and nonlinear kernel) and similar or better than the previous ones provided by the heuristic solution by Maldonado et al. (2018). © 2021 The Authors, Alfredo Marín has been supported by Spanish Ministry of Science and Innovation under project PID2019-110886RB-I00 . Part of this research was conducted while he was on sabbatical at Universidad de Sevilla, Spain . Luisa I. Martínez-Merino, Antonio M. Rodríguez-Chía and Justo Puerto thank the Agencia Estatal de Investigación (AEI) and the European Regional Development’s funds (ERDF) : project PID2020-114594GB-C21/C22 ; Regional Government of Andalusia : projects FEDER-UCA18-106895 , FEDER-US-1256951 and P18-FR-1422 ; and Fundación BBVA : project NetmeetData (Ayudas Fundación BBVA a equipos de investigación científica 2019). Luisa I. Martínez-Merino has been supported by Subprograma Juan de la Cierva Formación 2019 ( FJC2019-039023-I ) and PAIDI 2020 postdoctoral fellowship financed by European Social Fund and Junta de Andalucía

The traditional literature analysis mainly focuses on the literature metadata such as topics, authors, keywords, references, and rarely pays attention to the main content of papers. However, in many scientific domains such as science, computing, engineering, the methods and datasets involved in the papers published carry important information and are quite useful for domain analysis and recommendation. Method and dataset entities have various forms, which are more difficult to recognize than common entities. In this paper, we propose a novel Method and Dataset Entity Recognition model (MDER), which is able to effectively extract the method and dataset entities from the main textual content of scientific papers. The model is the first to combine rule embedding, a parallel structure of Convolutional Neural Network (CNN) and a two-layer Bi-directional Long Short-Term Memory (BiLSTM) with the self-attention mechanism. We evaluate the proposed model on datasets constructed from the published papers of different research areas in computer science. Our model performs well in multiple areas and features a good capacity for cross-area learning and recognition. Ablation study indicates that building different modules collectively contributes to the good entity recognition performance as a whole. The data augmentation positively contributes to model training, making our model much more robust. We finally apply the proposed model on PAKDD papers published from 2009–2019 to mine insightful results over a long time span. 1

Sequential pattern mining (SPM) has been applied in many fields. However, traditional SPM neglects the pattern repetition in sequence. To solve this problem, gap constraint SPM was proposed and can avoid finding too many useless patterns. Nonoverlapping SPM, as a branch of gap constraint SPM, means that any two occurrences cannot use the same sequence letter in the same position as the occurrences. Nonoverlapping SPM can make a balance between efficiency and completeness. The frequent patterns discovered by existing methods normally contain redundant patterns. To reduce redundant patterns and improve the mining performance, this paper adopts the closed pattern mining strategy and proposes a complete algorithm, named Nettree for Nonoverlapping Closed Sequential Pattern (NetNCSP) based on the Nettree structure. NetNCSP is equipped with two key steps, support calculation and closeness determination. A backtracking strategy is employed to calculate the nonoverlapping support of a pattern on the corresponding Nettree, which reduces the time complexity. This paper also proposes three kinds of pruning strategies, inheriting, predicting, and determining. These pruning strategies are able to find the redundant patterns effectively since the strategies can predict the frequency and closeness of the patterns before the generation of the candidate patterns. Experimental results show that NetNCSP is not only more efficient but can also discover more closed patterns with good compressibility. Furtherly, in biological experiments NetNCSP mines the closed patterns in SARS-CoV-2 and SARS viruses. The results show that the two viruses are of similar pattern composition with different combinations.

Multiple attribute decision making (MADM) problems often include quantitative and qualitative attributes which can be assessed by numerical values and subjective judgements respectively. The evidential reasoning (ER) rule provides a process for dealing with this type of MADM problems of both a quantitative and qualitative nature of uncertainty. In this paper, the ER rule is generalized to dealing with MADM problems in group decision making circumstance where the weights and reliabilities of both experts and attributes are considered. Specifically, the result and process aggregation based ER rules for MADM in group decision making are given respectively, followed by the comparative analysis on the given aggregations. The ER analytical rule for group MADM problems is also provided for the generalization of the ER analytical approach where group decision making is not considered. It is also a development of Yang's ER rule which is a recursive calculation process. Due to the fact that uncertainty and ambiguity are always existent in group decision making, interval weights and reliabilities of experts and attributes should be taken into account in the process of experts’ judgment aggregation. In this paper, several ER based programming models under interval weights and reliabilities are constructed for the generation of global belief degrees in a consistent way. A case study is conducted on the life cycle assessment of electric vehicles to illustrate the applicability of the proposed method and the potential in supporting MADM in group decision making.

This paper focuses on concept drift in business intelligence and data-driven decision support systems (DSSs). The assumption of a fixed distribution in the data renders conventional static DSSs inaccurate and unable to make correct decisions when concept drift occurs. However, it is important to know when, how, and where concept drift occurs so a DSS can adjust its decision processing knowledge to adapt to an ever-changing environment at the appropriate time. This paper presents a data distribution-based concept drift detection method called fuzzy competence model drift detection (FCM-DD). By introducing fuzzy sets theory and replacing crisp boundaries with fuzzy ones, we have improved the competence model to provide a better, more refined empirical distribution of the data stream. FCM-DD requires no prior knowledge of the underlying distribution and provides statistical guarantee of the reliability of the detected drift, based on the theory of bootstrapping. A series of experiments show that our proposed FCM-DD method can detect drift more accurately, has good sensitivity, and is robust.

In this paper, an ontology-supported case-based reasoning approach for computer-aided tolerance specification is proposed. This approach firstly considers the past tolerance specification problems and their schemes as previous cases and the new tolerance specification problems as target cases and uses an ontology to represent previous and target cases. Then certain ontology-based similarity measure is used to assess the similarity between the toleranced features of target and previous cases, the similarity between the part features of target and previous cases, and the similarity between the topological relations of target and previous cases. Based on these similarities, an ontology-based similarity measure for computing the similarity between target and previous cases is designed, and an algorithm for establishing such similarity measure with high accuracy and retrieving similar previous cases for a target case with this similarity measure is presented. This algorithm shows how to linearly combine the similarity of toleranced features, the similarity of part features, and the similarity of topological relations to assess the similarity between target and previous cases to implement retrieval of previous cases under the prerequisite of ensuring the highest accuracy of the similarity measure. The paper also reports a prototype implementation of the proposed approach, provides an example to illustrate how the approach works, and evaluates the approach via theoretical and experimental comparisons.

The recently introduced Marine Predators Algorithm (MPA) exhibits competitive performance in solving optimization problems. However, it often prematurely converges due to an imbalance between its exploration and exploitation capabilities. Therefore, in this paper, an improved MPA variant using a proposed Local Escaping Operator (LEO) is introduced. In the MPA, solution candidates are replaced by better candidates from the previous iteration, mimicking a memory of already visited prey-abundant areas. This indicates a weak inter-dependence between the candidates in the population and possible acceptance of new solution candidates created outside the algorithm without damaging the optimization process. Consequently, in the proposed approach, the worst candidates are replaced with solutions created by the LEO. The LEO is based on representative solutions, taking into account the relationship between predators and the characteristics of their population. The approach is experimentally compared with the state-of-the-art meta-heuristics on 82 test functions, including IEEE Congress on Evolutionary Computation test suites (CEC’14 and CEC’17) and three engineering problems. The results show the superiority of the LEO-MPA over the MPA and recent algorithms. Furthermore, in this paper, the suitability of the hybridization of meta-heuristics with the LEO is discussed and successful attempts with the best algorithms are shown.

The digitalization of the supply chain (SC) enables companies to address customers’ new requirements, the challenges of managing the SC and the expectations, and efficiency improvement. Although digital supply chain (DSC) is a buzzword, very few organizations and decision-makers understand the challenges of transforming from a traditional supply chain to a DSC. Therefore, the purpose of this paper is to develop and implement an integrated knowledge-based system (KBS) and evaluate the overall DSC readiness value (score) of an organization. DSC readiness factors are identified from an extensive literature review and validated by experts from academia and industry. The proposed KBS is based on Fuzzy-AHP​ that establishes a link between DSC readiness factors and their impact on performance and evaluates overall DSC readiness value (score). The proposed KBS has been validated in an Indian manufacturing company, and results show that the overall DSC readiness value (score) of the case company is 0.267. This paper will help organizations evaluate their current position in transforming from traditional supply chain to DSC and make a plan to achieve digitalization completely in their SC operations. Managers, practitioners, and decision-makers who are involved in digitalization can use our study’s findings as a starting point for aiding the transformation.

This paper introduces three hybrid algorithms that help in solving global optimization problems using reinforcement learning along with metaheuristic methods. Using the algorithms presented, the search agents try to find a global optimum avoiding the local optima trap. Compared to the classical metaheuristic approaches, the proposed algorithms display higher success in finding new areas as well as exhibiting a more balanced performance while in the exploration and exploitation phases. The algorithms employ reinforcement agents to select an environment based on predefined actions and tasks. A reward and penalty system is used by the agents to discover the environment, done dynamically without following a predetermined model or method. The study makes use of Q-Learning method in all three metaheuristic algorithms, so-called RL I−GWO , RL Ex−GWO , and RL WOA algorithms, so as to check and control exploration and exploitation with Q-Table. The Q-Table values guide the search agents of the metaheuristic algorithms to select between the exploration and exploitation phases. A control mechanism is used to get the reward and penalty values for each action. The algorithms presented in this paper are simulated over 30 benchmark functions from CEC 2014, 2015 and the results obtained are compared with well-known metaheuristic and hybrid algorithms (GWO, RL GWO , I-GWO, Ex-GWO, and WOA). The proposed methods have also been applied to the inverse kinematics of the robot arms problem. The results of the used algorithms demonstrate that RL WOA provides better solutions for relevant problems.

This paper demonstrates an innovative data analysis for weather using Cloud Computing, integrating both system and application Data Science services to investigate extreme weather events. Identifying five existing projects with ongoing challenges, our aim is to process, analyze and visualize collected data, study implications and report meaningful findings. We demonstrate the use of Cloud Computing technologies, MapReduce and optimization techniques to simulate temperature distributions and analyze weather data. Two major cases are presented. The first case is focused on forecasting temperatures based on studying trends from the historical data of Sydney, Singapore and London to compare the historical and forecasted temperatures. The second case is to use five-step MapReduce for numerical data analysis and eight-step process for visualization, which is used to analyze and visualize temperature distributions in the United States, before, during and after the time of experiencing polar vortex, as well as in the United Kingdom during and after the flood. Optimization was used in experiments involved up to 100 nodes between Cloud and non-Cloud and compared performance with and without optimization. There was an improvement in performance between 20% and 30% under 60 nodes in Cloud. Results, discussion and comparison were presented. We justify our research contributions and explain thoroughly in the paper how the three goals can be met: (1) forecasting temperatures of three cities based on evaluating the trends from the historical data; (2) using five-step MapReduce to achieve shorter execution time on Cloud and (3) using eight-step MapReduce with optimization to achieve data visualization for temperature distributions on US and UK maps.

In this paper, we focus on the problem of context-aware recommendation using tensor factorization. Traditional tensor-based models in context-aware recommendation scenario only consider user-item-context interactions. In this paper, we argue that rating can't be totally explained by the interactions and the rating also influenced by the combined impact of overall mean, user bias, item bias and context bias. Based on this hypothesis, we propose a novel context-aware recommendation model named Bias Tensor Factorization, which take all this factors into account. Additionally, traditional context-aware recommenders with tensor factorization still have three main drawbacks: (1) the model complexity of those models increase exponentially with the number of context features, (2) those models can only handle context features with categorical values and (3) the models fail to select effective features from available context features. To address those problems, we propose a context features auto-encoding algorithm based on regression tree which can both handle numerical features and select effective features. Then we integrate this algorithm with Bias Tensor Factorization. Experiments on a real world contextual dataset and Movielens show that our proposed algorithms outperform the state-of-art context-aware recommendation algorithms, namely tensor factorization and factorization machine.

The original Pawlaks rough set approach based on indiscernibility relation (single granularity) has been extended to multigranulation rough set structure in the recent years. Multigranulation rough set approach has become a flouring research direction in rough set theory. This paper considers rough approximation of a fuzzy concept under the framework of multigranulation over two different universes of discourse, i.e., multigranulation fuzzy rough set models over two universes. We present three types of multigranulation fuzzy rough set over two universes by the constructive approach, respectively. Some interesting properties of the proposed models are discussed and also the interrelationships between the proposed models and the existing rough set models are given. We then propose a new approach to a kind of multiple criteria group decision making problem based on multigranulation fuzzy rough set model over two universes. The decision rules and algorithm of the proposed method are given and an example of handling multiple criteria group decision making problem of clothes ranking illustrates this approach. The main contribution of this paper is twofold. One is to establish the multigranulation fuzzy rough set theory over two universes. Another is to try presenting a new approach to multiple criteria group decision making based on multigranulation fuzzy rough set over two universes. The proposed models not only enrich the theory of multigranulation rough set but also make a tentative to provide a new perspective for multiple criteria group decision making with uncertainty.

The prediction of protein structures directly from amino acid sequences is one of the biggest challenges in computational biology. It can be divided into several independent sub-problems in which protein secondary structure (SS) prediction is fundamental. Many computational methods have been proposed for SS prediction problem. Few of them can model well both the sequence-structure mapping relationship between input protein features and SS, and the interaction relationship among residues which are both important for SS prediction. In this paper, we proposed a deep recurrent encoder–decoder networks called Secondary Structure Recurrent Encoder–Decoder Networks (SSREDNs) to solve this SS prediction problem. Deep architecture and recurrent structures are employed in the SSREDNs to model both the complex nonlinear mapping relationship between input protein features and SS, and the mutual interaction among continuous residues of the protein chain. A series of techniques are also used in this paper to refine the model’s performance. The proposed model is applied to the open dataset CullPDB and CB513. Experimental results demonstrate that our method can improve both Q3 and Q8 accuracy compared with some public available methods. For Q8 prediction problem, it achieves 68.20% and 73.1% accuracy on CB513 and CullPDB dataset in fewer epochs better than the previous state-of-art method.

AGAP : équipe GE2pop; Document Indexing is but not limited to summarizing document contents with a small set of keywords or concepts of a knowledge base. Such a compact representation of document contents eases their use in numerous processes such as content-based information retrieval, corpus-mining and classification. An important effort has been devoted in recent years to (partly) automate semantic indexing, i.e. associating concepts to documents, leading to the availability of large corpora of semantically indexed documents. In this paper we introduce a method that hierarchically clusters documents based on their semantic indices while providing the proposed clusters with semantic labels. Our approach follows a neighbor joining strategy. Starting from a distance matrix reflecting the semantic similarity of documents, it iteratively se- lects the two closest clusters to merge them in a larger one. The similarity matrix is then updated. This is usually done by combining similarity of the two merged clusters, e.g. using the average similarity. We propose in this paper an alternative approach where the new cluster is first semantically annotated and the similarity matrix is then updated using the semantic similarity of this new annotation with those of the remaining clusters. The hierarchical clustering so obtained is a binary tree with branch lengths that convey semantic distances of clusters. It is then post-processed by using the branch lengths to keep only the most relevant clusters. Such a tool has numerous practical applications as it automates the organi- zation of documents in meaningful clusters (e.g. papers indexed by MeSH terms, bookmarks or pictures indexed by WordNet) which is a tedious everyday task for many people. We assess the quality of the proposed methods using a specific benchmark of annotated clusters of bookmarks that were built man- ually. Each dataset of this benchmark has been clustered independently by several users. Remarkably, the clusters automatically built by our method are congruent with the clusters proposed by experts. All resources of this work, including source code, jar file, benchmark files and results are available at this address: http://sc.nicolasfiorini.info .

In this paper we present a system that automatically builds ordered timelines of events from different written texts in English. The system deals with problems such as automatic event extraction, cross-document temporal relation extraction and cross-document event coreference resolution. Its main characteristic is the application of three different types of knowledge: temporal knowledge, lexical-semantic knowledge and distributional-semantic knowledge, in order to anchor and order the events in the timeline. It has been evaluated within the framework of SemEval 2015. The proposed system improves the current state-of-the-art systems in all measures (up to eight points of F1-score over other systems) and shows a significant advance in the Cross-document event ordering task. This paper has been partially supported by the Spanish government, project TIN2015-65100-R and project TIN2015-65136-C2-2-R.

In the sensing as a service paradigm, Internet of Things (IoT) Middleware platforms allow data consumers to retrieve the data they want without knowing the underlying technical details of IoT resources (i.e. sensors and data processing components). However, configuring an IoT middleware platform and retrieving data is a significant challenge for data consumers as it requires both technical knowledge and domain expertise. In this paper, we propose a knowledge driven approach called Context Aware Sensor Configuration Model (CASCOM) to simplify the process of configuring IoT middleware platforms, so the data consumers, specifically non-technical personnel, can easily retrieve the data they required. In this paper, we demonstrate how IoT resources can be described using semantics in such away that they can later be used to compose service work-flows. Such automated semantic-knowledge based IoT resource composition approach advances the current research. We demonstrate the feasibility and the usability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized to any other middleware platform., Comment: Knowledge-Based Systems Journal 2016. arXiv admin note: substantial text overlap with arXiv:1309.1515, arXiv:1309.1811

Class imbalance is an active research area in the machine learning community. However, existing and recent literature showed that class overlap had a higher negative impact on the performance of learning algorithms. This paper provides detailed critical discussion and objective evaluation of class overlap in the context of imbalanced data and its impact on classification accuracy. First, we present a thorough experimental comparison of class overlap and class imbalance. Unlike previous work, our experiment was carried out on the full scale of class overlap and an extreme range of class imbalance degrees. Second, we provide an in-depth critical technical review of existing approaches to handle imbalanced datasets. Existing solutions from selective literature are critically reviewed and categorised as class distribution-based and class overlap-based methods. Emerging techniques and the latest development in this area are also discussed in detail. Experimental results in this paper are consistent with existing literature and show clearly that the performance of the learning algorithm deteriorates across varying degrees of class overlap whereas class imbalance does not always have an effect. The review emphasises the need for further research towards handling class overlap in imbalanced datasets to effectively improve learning algorithms’ performance.

Crowdsourcing services provide an easy means of acquiring labeled training data for supervised learning. However, the labels provided by a single crowd worker are often unreliable. Repeated labeling can be used to solve this problem. After multiple labels have been acquired by repeated labeling for each instance, in general consensus methods are used to obtain the integrated labels of instances. Although consensus methods are effective in practice, it cannot be denied that a level of noise still exists in the set of integrated labels. In this study, an attempt was made to employ noise filters to delete the noise in integrated labels, and consequently, enhance the training data and model quality. In fact, noise handling is a relatively mature field in the machine learning community, and many noise filters for deleting label noise have been presented in the past. However, to the best of our knowledge, in very few studies was noise filtering used to improve crowdsourcing learning. Therefore, in this study we empirically investigated the performance of noise filters in terms of improving crowdsourcing learning. Thus, in this paper some existing noise filters presented in previous papers are reviewed and their experimental application to crowdsourcing learning tasks is described. Experimental results based on 14 benchmark UCI data sets and three real-world data sets show that these noise filters can significantly reduce the noise level in integrated labels and thereby considerably enhance the performance of target classifiers.

Trust can help participants in online social communities to make decisions; however, it is a challenge for systems to map trust into computational models because of its subjective properties. Also, many online social communities are sparsely connected. Therefore, it is necessary to introduce mechanisms which can infer indirect trust among participants who are not directly connected. We provide a survey of existing trust management systems for online social communities. We also list four types of attacks, and analyze existing systems' vulnerabilities. Compared with previous surveys, our survey takes trust modeling, trust inference, and attacks into account. Although there are several survey papers about global trust/reputation related attacks, the main contribution of this paper is that we consider trust inference and potential local trust related attacks.

While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic.

International audience; This paper deals with linguistic models that may prove useful for representing the information during decision making.Data extraction is a complex problem, especially when dealing with information coming from human beings (linguistic assertions, preferences, feelings, etc.) and several models have emerged to overcome difficulties in expressing the data. Among those models, we are interested in two of them: the 2-tuple semantic model and the 2-tuple symbolic model.In this paper we stress on a comparison between both models and we prove that links can be made between them. An interesting result is obtained: the 2-tuple semantic model can generate a partitioning that is identical to the one that would be generated thanks to the symbolic 2-tuple model. This permits to compare the models and mix the two when there is a need to use one model or another, depending on the applications, and then to reach a consensus. In closing, an example of the use is given to demonstrate the value of the method.

This paper follows the 25years of development of methods and systems for knowledge-based neural network systems and more specifically the recent evolving connectionist systems (ECOS). ECOS combine the adaptive/evolving learning ability of neural networks and the approximate reasoning and linguistically meaningful explanation features of symbolic representation, such as fuzzy rules. This review paper presents the classical now hybrid expert systems and evolving neuro-fuzzy systems, along with new developments in spiking neural networks, neurogenetic systems, and quantum inspired systems, all discussed from the point of few of their adaptability, model interpretability and knowledge discovery. The paper discusses new directions for the integration of principles from neural networks, fuzzy systems, bio- and neuroinformatics, and nature in general.

This paper describes, formalises and implements an approach to computational creativity based on situated interpretation. The paper introduces the notions of framing and reframing of conceptual spaces based on empirical studies as the driver for this research. It uses concepts from situated cognition, and situated interpretation in particular, to be the basis of a formal model of the movement between conceptual spaces. This model is implemented using rules within interacting neural networks. This implementation demonstrates behaviour similar to that observed in studies of human designers.

Multi-attribute resource allocation problems involve the allocation of resources on the basis of several attributes, therefore, the definition of a fairness method for this kind of auctions should be formulated from a multi-dimensional perspective. Under such point of view, fairness should take into account all the attributes involved in the allocation problem, since focusing on just a single attribute may compromise the allocations regarding the remainder attributes (e.g. incurring in delayed or bad quality tasks). In this paper, we present a multi-dimensional fairness approach based on priorities. For that purpose, a recurrent auction scenario is assumed, in which the auctioneer keeps track of winner and losers. From that information, the priority methods are defined based on the lost auctions number, the number of consecutive loses, and the fitness of their loser bids. Moreover, some methods contain a probabilistic parameter that enables handling wealth ranking disorders due to fairness. We test our approach in real-data based simulator which emulates an industrial production environment where several resource providers compete to perform different tasks. The results pointed that multi-dimensional fairness incentives agents to remain in the market whilst it improves the equity of the wealth distribution without compromising the quality of the allocation attributes This research project has been partially funded through the BR10/18 Scholarship granted to the first author of the paper. Work developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016) and the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R)

Recently, recommender systems have attracted increased attention because of their ability to suggest appropriate choices to users based on intelligent prediction. As one of the most popular recommender system techniques, Collaborative Filtering (CF) achieves efficiency from the similarity measurement of users and items. However, existing similarity measurement methods have reduced accuracy due to problems such as data correlation and data sparsity. To overcome these problems, this paper introduces the Grey Forecast (GF) model for recommender systems. First, the Cosine Distance method is used to compute the similarities between items. Then, we rank the items, which have been rated by an active user, according to their similarities to the target item, which has not yet been rated by the active user; we use the ratings of the first k items to construct a GF model and obtain the required prediction. The advantages of the paper are threefold: first, the proposed method introduces a new prediction model for CF, which, in turn, yields better performance of the model; second, it is able to alleviate the well-known sparsity problem as it requires less data in constructing the model; third, the model will become more effective when strong correlations exist among the data. Extensive experiments are conducted and the results are compared with several CF methods including item based, slope one, and matrix factorization by using two public data sets, namely, MovieLens and EachMovie. The experimental results demonstrate that the proposed algorithm exhibits improvements of over 20% in terms of the mean absolute error (MAE) and root mean square error (RMSE) when compared with the item based method. Moreover, it achieves comparative, or sometimes even better, performance when compared to the matrix factorization methods in terms of accuracy and F-measure metrics, even with small k.

Three-way concept analysis is a newly proposed area of formal concept analysis from which one can obtain both the inclusion decision and the exclusion decision. In general, given a context, some attributes may not be essential in three-way concept analysis, such as forming three-way concept lattice. So in this paper, we study the attribute reductions of three-way concept lattices in order to make the data easily be understood. Firstly, based on different criteria generated from object-induced three-way concept (OE-concept), four kinds of attribute reductions are proposed. The four reductions together embody different characteristics of a formal context and can be used in different occasions. Secondly, we discuss their relationships, including their advantages and disadvantages and the relationships among consistent sets and among the cores. Thirdly, based on attribute-induced three-way concept (AE-concept), we also give four attribute-induced three-way attribute reductions and discuss their relationships. Finally, the approaches to computing these attribute reductions are presented and the obtained results are demonstrated and verified by an empirical case. In this paper, we systematically investigate the attribute reductions of three-way concept lattices which enriches the study of formal concept analysis.

Gradient boosted trees (GBTs) are ubiquitous models used by researchers, machine learning (ML) practitioners, and data scientists because of their robust performance, interpretable behavior, and ease-of-use. One critical challenge in training GBTs is the tuning of their hyperparameters. In practice, selecting these hyperparameters is often done manually. Recently, the ML community has advocated for tuning hyperparameters through black-box optimization and developed state-of-the-art systems to do so. However, applying such systems to tune GBTs suffers from two drawbacks. First, these systems are not \textit{model-aware}, rather they are designed to apply to a \textit{generic} model; this leaves significant optimization performance on the table. Second, using these systems requires \textit{domain knowledge} such as the choice of hyperparameter search space, which is an antithesis to the automatic experimentation that black-box optimization aims to provide. In this paper, we present SigOpt Mulch, a model-aware hyperparameter tuning system specifically designed for automated tuning of GBTs that provides two improvements over existing systems. First, Mulch leverages powerful techniques in metalearning and multifidelity optimization to perform model-aware hyperparameter optimization. Second, it automates the process of learning performant hyperparameters by making intelligent decisions about the optimization search space, thus reducing the need for user domain knowledge. These innovations allow Mulch to identify good GBT hyperparameters far more efficiently -- and in a more seamless and user-friendly way -- than existing black-box hyperparameter tuning systems.

Although traditional symbolic reasoning methods are highly interpretable, their application in knowledge graph link prediction is limited due to their low computational efficiency. In this paper, we propose a new neural symbolic reasoning method: RNNCTPs, which improves computational efficiency by re-filtering the knowledge selection of Conditional Theorem Provers (CTPs), and is less sensitive to the embedding size parameter. RNNCTPs are divided into relation selectors and predictors. The relation selectors are trained efficiently and interpretably, so that the whole model can dynamically generate knowledge for the inference of the predictor. In all four datasets, the method shows competitive performance against traditional methods on the link prediction task, and can have higher applicability to the selection of datasets relative to CTPs., Comment: 16 pages, 8 figures,5 tables

Emotional Support Conversation (ESConv) aims to reduce help-seekers'emotional distress with the supportive strategy and response. It is essential for the supporter to select an appropriate strategy with the feedback of the help-seeker (e.g., emotion change during dialog turns, etc) in ESConv. However, previous methods mainly focus on the dialog history to select the strategy and ignore the help-seeker's feedback, leading to the wrong and user-irrelevant strategy prediction. In addition, these approaches only model the context-to-strategy flow and pay less attention to the strategy-to-context flow that can focus on the strategy-related context for generating the strategy-constrain response. In this paper, we propose a Feedback-Aware Double COntrolling Network (FADO) to make a strategy schedule and generate the supportive response. The core module in FADO consists of a dual-level feedback strategy selector and a double control reader. Specifically, the dual-level feedback strategy selector leverages the turn-level and conversation-level feedback to encourage or penalize strategies. The double control reader constructs the novel strategy-to-context flow for generating the strategy-constrain response. Furthermore, a strategy dictionary is designed to enrich the semantic information of the strategy and improve the quality of strategy-constrain response. Experimental results on ESConv show that the proposed FADO has achieved the state-of-the-art performance in terms of both strategy selection and response generation. Our code is available at https://github.com/Thedatababbler/FADO., Accepted on Knowl. Based Syst. (SCI I)

Financiado para publicar en acceso aberto. Universidade da Coruña/CISUG [Abstract]: In recent years, mainly due to the application of smartphones in this area, research in human activity recognition (HAR) has shown a continuous and steady growth. Thanks to its wide range of sensors, its size, its ease of use, its low price and its applicability in many other fields, it is a highly attractive option for researchers. However, the vast majority of studies carried out so far focus on laboratory settings, outside of a real-life environment. In this work, unlike in other papers, progress was sought on the latter point. To do so, a dataset already published for this purpose was used. This dataset was collected using the sensors of the smartphones of different individuals in their daily life, with almost total freedom. To exploit these data, numerous experiments were carried out with various machine learning techniques and each of them with different hyperparameters. These experiments proved that, in this case, tree-based models, such as Random Forest, outperform the rest. The final result shows an enormous improvement in the accuracy of the best model found to date for this purpose, from 74.39% to 92.97%. Xunta de Galicia; ED431G 2019/01 Xunta de Galicia; ED481A 2020/003 Xunta de Galicia; ED431C 2022/46 Xunta de Galicia; ED431C 2018/49 Xunta de Galicia; ED431C 2021/53 This research was partially funded by MCIN/AEI/10.13039/ 501100011033, NextGenerationEU/PRTR, FLATCITY-POC , Spain [grant number P DC2021-121239-C31]; MCIN/AEI/10.13039/ 501100011033 MAGIST, Spain [grant number P ID2019-105221RB-C41]; Xunta de Galicia/FEDER-UE, Spain [grant numbers ED431G 2019/01 , ED481A 2020/003 , ED431C 2022/46 , ED431C 2018/49 and ED431C 2021/53 ]. Funding for open access charge: Universidade da Coruña/CISUG.

Data availability: The code used in this paper is available at: https://github.com/mxt0607/Two_Stage_RUL_Prediction. This article puts forward a two-stage integrated method to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). At the first stage, a convolutional neural network (CNN) is employed to preliminarily estimate the cycle life of each testing LIB, where the network structure of the CNN is carefully designed to extract the discharge capacity features. By analyzing the cycle lives, an LIB which has the most similar degradation mode to each testing LIB is chosen from the training dataset. The capacities of the selected LIB are identified based on a double exponential model (DEM). At the second stage, the identified DEM is utilized as the initial mean function of the Gaussian process regression (GPR) algorithm. The GPR algorithm is then applied to early RUL prediction of each testing LIB in a personalized manner. To verify the efficacy of the proposed method, four LIBs with long-term cycle lives are selected as the testing dataset. Experimental results show the superior performance of the proposed method over the standard CNN-based RUL prediction method and the standard GPR-based RUL prediction method. This work was supported in part the National Natural Science Foundation of China under Grants 62273264, 51905197 and 61933007.

This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection. GNNs are a deep learning approach for graph-based data that incorporate graph structures into learning to generalise graph representations and output embeddings. As network flows are naturally graph-based, GNNs are a suitable fit for analysing and learning network behaviour. The majority of current implementations of GNN-based Network Intrusion Detection Systems (NIDSs) rely heavily on labelled network traffic which can not only restrict the amount and structure of input traffic, but also the NIDSs potential to adapt to unseen attacks. To overcome these restrictions, we present Anomal-E, a GNN approach to intrusion and anomaly detection that leverages edge features and graph topological structure in a self-supervised process. This approach is, to the best our knowledge, the first successful and practical approach to network intrusion detection that utilises network flows in a self-supervised, edge leveraging GNN. Experimental results on two modern benchmark NIDS datasets not only clearly display the improvement of using Anomal-E embeddings rather than raw features, but also the potential Anomal-E has for detection on wild network traffic., Comment: Accepted by Knowledge-Based Systems

Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In the field of gene expression analysis, several matrix factorization-based dimensionality reduction methods have been developed. However, such methods can still be improved in terms of efficiency and reliability. In this paper, an innovative approach to feature selection, called Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy (DR-FS-MFMR), is introduced. The major focus of DR-FS-MFMR is to discard redundant features from the set of original features. In order to reach this target, the primary feature selection problem is defined in terms of two aspects: (1) the matrix factorization of data matrix in terms of the feature weight matrix and the representation matrix, and (2) the correlation information related to the selected features set. Then, the objective function is enriched by employing two data representation characteristics along with an inner product regularization criterion to perform both the redundancy minimization process and the sparsity task more precisely. To demonstrate the proficiency of the DR-FS-MFMR method, a large number of experimental studies are conducted on nine gene expression datasets. The obtained computational results indicate the efficiency and productivity of DR-FS-MFMR for the gene selection task.

Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input. However, in real applications, a complete and correct past motion sequence can be too expensive to achieve. In this paper, we propose a novel approach to predicting future human motions from a much weaker condition, i.e., a single image, with mixture density networks (MDN) modeling. Contrary to most existing deep human motion prediction approaches, the multimodal nature of MDN enables the generation of diverse future motion hypotheses, which well compensates for the strong stochastic ambiguity aggregated by the single input and human motion uncertainty. In designing the loss function, we further introduce the energy-based formulation to flexibly impose prior losses over the learnable parameters of MDN to maintain motion coherence as well as improve the prediction accuracy by customizing the energy functions. Our trained model directly takes an image as input and generates multiple plausible motions that satisfy the given condition. Extensive experiments on two standard benchmark datasets demonstrate the effectiveness of our method in terms of prediction diversity and accuracy.

Few-shot point cloud semantic segmentation learns to segment novel classes with scarce labeled samples. Within an episode, a novel target class is defined by a few support samples with corresponding binary masks, where only the points of this class are labeled as foreground and others are regarded as background. In the tasks involving multiple target classes, since the meanings of background are diverse for different target classes, background ambiguities appear: Some points labeled as background in one support sample may be of other target classes. It will result in incorrect guidance and damage model's segmentation performance. However, previous methods in the literature do not consider this problem. In this paper, we propose a simple yet effective approach to tackle background ambiguities, which adopts the entropy of predictions on query samples to the training objective function as an additional regularization. Besides, we design a feature transformation operation to reduce the feature differences between support and query samples. With our proposed approach, fine-tuning, a weak baseline method for few-shot segmentation, gains significant performance improvement (e.g., 7.48% and 7.04% in 2-way-1-shot and 3-way-1-shot tasks of S3DIS, respectively) and outperforms current state-of-the-art methods in all the task settings of S3DIS and ScanNet benchmark datasets. Ministry of Education (MOE) National Research Foundation (NRF) This work was supported by National Natural Science Foundation of China (NSFC) 61876208, Tip-top Scientific and Technical Innovative Youth Talents of Guangdong Special Support Program (2019TQ05X200), 2022 Tencent Wechat Rhino-Bird Focused Research Program Research (Tencent WeChat RBFR2022008) and the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-RP-2018-003), the Ministry of Education, Singapore, under its Academic Research Fund Tier 2 (MOE-T2EP20220-0007) and Tier 1 (RG95/20).

This paper presents a batch-wise density-based clustering approach for local outlier detection in massive-scale datasets. Unlike the well-known traditional algorithms, which assume that all the data is memory-resident, our proposed method is scalable and processes the input data chunk-by-chunk within the confines of a limited memory buffer. A temporary clustering model is built at the first phase; then, it is gradually updated by analyzing consecutive memory loads of points. Subsequently, at the end of scalable clustering, the approximate structure of the original clusters is obtained. Finally, by another scan of the entire dataset and using a suitable criterion, an outlying score is assigned to each object called SDCOR (Scalable Density-based Clustering Outlierness Ratio). Evaluations on real-life and synthetic datasets demonstrate that the proposed method has a low linear time complexity and is more effective and efficient compared to best-known conventional density-based methods, which need to load all data into the memory; and also, to some fast distance-based methods, which can perform on data resident in the disk.

Automatic code review (ACR), which can relieve the costs of manual inspection, is an indispensable and essential task in software engineering. To deal with ACR, existing work is to serialize the abstract syntax tree (AST). However, making sense of the whole AST with sequence encoding approach is a daunting task, mostly due to some redundant nodes in AST hinder the transmission of node information. Not to mention that the serialized representation is inadequate to grasp the information of tree structure in AST. In this paper, we first present a new large-scale Apache Automatic Code Review (AACR) dataset for ACR task since there is still no publicly available dataset in this task. The release of this dataset would push forward the research in this field. Based on it, we propose a novel Simplified AST based Graph Convolutional Network (SimAST-GCN) to deal with ACR task. Concretely, to improve the efficiency of node information dissemination, we first simplify the AST of code by deleting the redundant nodes that do not contain connection attributes, and thus deriving a Simplified AST. Then, we construct a relation graph for each code based on the Simplified AST to properly embody the relations among code fragments of the tree structure into the graph. Subsequently, in the light of the merit of graph structure, we explore a graph convolution networks architecture that follows an attention mechanism to leverage the crucial implications of code fragments to derive code representations. Finally, we exploit a simple but effective subtraction operation in the representations between the original and revised code, enabling the revised difference to be preferably learned for deciding the results of ACR. Experimental results on the AACR dataset illustrate that our proposed model outperforms the state-of-the-art methods., 15 pages

Federated learning (FL) is a privacy-preserving paradigm that collaboratively train machine learning models with distributed data stored in different silos without exposing sensitive information. Different from most existing FL approaches requiring data from different parties share either the same feature space or sample ID space, federated transfer learning (FTL), which is a recently proposed FL concept, is designed for situations where data from different parties differ not only in samples but also in feature space. However, like most traditional FL approaches, FTL methods also suffer from issues caused by insufficiency of overlapping data. In this paper, we propose a novel FTL framework referred to as Semi-Supervised Federated Heterogeneous Transfer Learning (SFHTL) to leverage on the unlabeled non-overlapping samples to reduce model overfitting as a result of insufficient overlapping training samples in FL scenarios. Unlike existing FTL approaches, SFHTL makes use of non-overlapping samples from all parties to expand the training set for each party to improve local model performance. Through extensive experimental evaluation based on real-world datasets, we demonstrate significant advantages of SFHTL over state-of-the-art approaches. Nanyang Technological University National Research Foundation (NRF) This research is supported, in part, by the National Natural Science Foundation of China under Grant NSFC 62106167; Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions; the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-RP-2020-019); the RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund (No. A20G8b0102), Singapore; Nanyang Assistant Professorship (NAP); the Joint NTUWeBank Research Centre on Fintech (Award No: NWJ-2020-008), Nanyang Technological University, Singapore; and Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) (NSC-2019- 011). Qiang Yang is supported in part by China National Key Research and Development Program of China under Grant No. 2018AAA0101100.

Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based representation.However, current NMF methods do not always generate sparse solutions.In this paper, we propose a new NMF method with log-norm imposed on the factor matrices to enhance the sparseness.Moreover, we propose a novel column-wisely sparse norm, named $\ell_{2,\log}$-(pseudo) norm to enhance the robustness of the proposed method.The $\ell_{2,\log}$-(pseudo) norm is invariant, continuous, and differentiable.For the $\ell_{2,\log}$ regularized shrinkage problem, we derive a closed-form solution, which can be used for other general problems.Efficient multiplicative updating rules are developed for the optimization, which theoretically guarantees the convergence of the objective value sequence.Extensive experimental results confirm the effectiveness of the proposed method, as well as the enhanced sparseness and robustness.

Bayesian transfer learning (BTL) is defined in this paper as the task of conditioning a target probability distribution on a transferred source distribution. The target globally models the interaction between the source and target, and conditions on a probabilistic data predictor made available by an independent local source modeller. Fully probabilistic design is adopted to solve this optimal decision-making problem in the target. By successfully transferring higher moments of the source, the target can reject unreliable source knowledge (i.e. it achieves robust transfer). This dual-modeller framework means that the source's local processing of raw data into a transferred predictive distribution -- with compressive possibilities -- is enriched by (the possible expertise of) the local source model. In addition, the introduction of the global target modeller allows correlation between the source and target tasks -- if known to the target -- to be accounted for. Important consequences emerge. Firstly, the new scheme attains the performance of fully modelled (i.e. conventional) multitask learning schemes in (those rare) cases where target model misspecification is avoided. Secondly, and more importantly, the new dual-modeller framework is robust to the model misspecification that undermines conventional multitask learning. We thoroughly explore these issues in the key context of interacting Gaussian process regression tasks. Experimental evidence from both synthetic and real data settings validates our technical findings: that the proposed BTL framework enjoys robustness in transfer while also being robust to model misspecification.

In this paper, a novel unsupervised low-rank representation model, i.e., Auto-weighted Low-Rank Representation (ALRR), is proposed to construct a more favorable similarity graph (SG) for clustering. In particular, ALRR enhances the discriminability of SG by capturing the multi-subspace structure and extracting the salient features simultaneously. Specifically, an auto-weighted penalty is introduced to learn a similarity graph by highlighting the effective features, and meanwhile, overshadowing the disturbed features. Consequently, ALRR obtains a similarity graph that can preserve the intrinsic geometrical structures within the data by enforcing a smaller similarity on two dissimilar samples. Moreover, we employ a block-diagonal regularizer to guarantee the learned graph contains $k$ diagonal blocks. This can facilitate a more discriminative representation learning for clustering tasks. Extensive experimental results on synthetic and real databases demonstrate the superiority of ALRR over other state-of-the-art methods with a margin of 1.8\%$\sim$10.8\%.