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Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages.

Surrogate-assisted evolutionary algorithms (SAEAs), which use efficient surrogate models or meta-models to approximate the fitness function in evolutionary algorithms (EAs), are effective and popular methods for solving computationally expensive optimization problems. During the past decades, a number of SAEAs have been proposed by combining different surrogate models and EAs. This paper dedicates to providing a more systematical review and comprehensive empirical study of surrogate models used in single-objective SAEAs. A new taxonomy of surrogate models in SAEAs for single-objective optimization is introduced in this paper. Surrogate models are classified into two major categories: absolute fitness models, which directly approximate the fitness function values of candidate solutions, and relative fitness models, which estimates the relative rank or preference of candidates rather than their fitness values. Then, the characteristics of different models are analyzed and compared by conducting a series of experiments in terms of time complexity (execution time), model accuracy, parameter influence, and the overall performance when used in EAs. The empirical results are helpful for researchers to select suitable surrogate models when designing SAEAs. Open research questions and future work are discussed at the end of the paper.

Experton theory, a generalization of probabilistic set theory, that is of great usefulness to group decision analysis, was first proposed as a means of improving the processing and analysis of opinions issued by experts. This theory produces an information-fusion mathematical object , the experton, which can be used in predictive problems to justify decisions based on well-constructed reasoning. The aim of this paper is to present an aggregative method of several expertons, with the idea that some of the groups of experts involved in producing these expertons may have more influence than others in the decision-making process. In this article, we carry out an aggregation analysis of expertons, not experts, which culminates in the creation of a new mathematical object. This object, which is called the weighted average multiexperton, is coherent with an experton-type object created from a weighting of the initial data provided by all experts. Since the aggregation method presented has been devised to represent the decision-maker’s attitude regarding the importance of different groups of experts, this approach represents a new tool for dealing with group decision-making problems. Additionally, the study presents some properties of the new object. Finally, the paper ends with an application for business decision-making.

In association rules mining from data that have numeric-valued attributes, automatically adjusting the attribute intervals at the time of the mining process without a preprocess is very critical for preventing data loss and attribute interactions. In this paper, differential evolution and sine cosine algorithm based novel hybrid multi-objective evolutionary optimization methods are proposed for rapidly and directly mining the reduced high-quality numerical association rules by simultaneously adjusting the relevant intervals of related attributes without finding the frequent itemsets. These algorithms perform a global search and find the high-quality rules set in only one execution by modeling the rule mining task as a multi-objective problem that simultaneously meets different conflicting metrics. The algorithms proposed in this paper ensure the discovered rules to have high confidence and support and to be comprehensible. The proposed methods automate the rule mining process by directly finding the minimum intervals for the attributes and eliminating the need for minimum confidence and minimum support determined beforehand for each data set. The performances of new algorithms proposed in this study were tested with those of the state-of-the-art algorithms. The results show superiority of the proposed methods on the data sets that contain fewer attributes and higher number of instances.

Acute hypotensive episode (AHE) is a common serious postoperative complication in ICU, which may raise multiple system failure (especially of cardiac and respiratory kinds), and even cause death. Timely and effective clinical intervention is obviously vital to the saving of patients. AHE detection involves physiological time-series monitoring, processing and prediction technologies, which can offer insights to neuroscientists, biologists, and even provide support for clinicians. This paper presents a hybrid artificial intelligence model combined with CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise, a typical method for physiological signal decomposition), deep learning, multiple gene expression programming and fuzzy expert system for AHE detection. In this paper, the physiological signal is selected from a benchmark dataset, for example MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care II), which collects large scale real patients’ data for clinical research. In the hybrid model, a typical signal decomposition method is employed for AHE signal processing, and an autoencoder based deep neural network is established for feature extraction. Finally, a reliable and explainable classifier is presented by fusing gene expression programming and the fuzzy method. Experimental results based on real data set demonstrate that the proposed method outperforms state-of-the-art AHE detection methods by achieving the prediction accuracy of 88.14% in 2866 records.

Highlights • The minimum cost consensus model is proposed to coordinate the opinions of experts. • A new method of LSGDM clustering is proposed to improve CRP efficiency. • A consensus index CI reflecting the harmony degree between experts is devised. • A robust counterpart of robust optimization consensus is implemented., Recently, large-scale group decision making (LSGDM) in social network comes into being. In the practical consensus of LSGDM, the unit adjustment cost of experts is difficult to obtain and may be uncertain. Therefore, the purpose of this paper is to propose a consensus model based on robust optimization. This paper focuses on LSGDM, considering the social relationship between experts. In the presented model, an expert clustering method, combining trust degree and relationship strength, is used to classify experts with similar opinions into subgroups. A consensus index, reflecting the harmony degree between experts, is devised to measure the consensus level among experts. Then, a minimum cost model based on robust optimization is proposed to solve the robust optimization consensus problem. Subsequently, a detailed consensus feedback adjustment is presented. Finally, a case study and comparative analysis are provided to verify the validity and advantage of the proposed method.

Considering that most popular models solving aspect-level sentiment classification problems mainly focus on designing complicated neural networks to scale the importance of each word in the sentence, this paper addresses this problem from the view of semantic space. Motivated by the fact that the senses of a word can be sophisticatedly embedded into the semantic space using a distributed representation, this paper hypothesizes that each sense of a word can be represented by one or more specific dimensions, and thus the target of aspect-level sentiment classification can be simplified into searching the related dimensions for the aspects and sentiments concerned. Particularly, an Attention Vector (ATV) based on attention mechanism is designed for each aspect in terms of a specific task, which involves two sub-vectors, i.e., a Dimension Attention Vector (DATV) and a Sentiment Attention Vector (SATV). The DATV determines the significances of different dimensions based on their correlations with an aspect; and the SATV allocates weights for the attributes of words, which are decided by sentiment polarities and part-of-speech (PoS) tagging. Given a sub-dataset related to an aspect, the ATV will be optimized by an Artificial Bee Colony (ABC) algorithm with a Support Vector Machine (SVM) classifier, the objective of which is to maximize classification accuracy. Intrinsically, the DATV can reduce the ambiguity existing in polysemy, meanwhile, the SATV is an auxiliary means for the optimization of the DATV, which can help eliminate the misunderstandings caused by antonyms. Then, the optimized DATV will be applied on a Convolutional Neural Network (CNN) model via simply scaling the pretrained word embeddings as inputs (named as ATV-CNN model). Experimental results show that the ATV-CNN model can have substantial advantages when compared with state-of-the-art models.

The comprehensive logistics distribution center location selection (CLDCLS) problem is a multiattribute group decision-making (MAGDM) problem in which multiple commodity preference weights are considered. To better describe the preference information and expert evaluation information, this paper utilizes 2-dimensional linguistic (2DL) information to express the preference information of various commodities and the expert evaluation, which can represent not only the evaluation information of experts but also the reliability of the evaluation information. Additionally, for solving the CLDCLS problem, this paper puts forward improved operational rules a score function, a distance formula and a correlation coefficient measure. Based on the 2DL information and the improved operational rules, we propose a 2-dimensional linguistic similarity-degree-based clustering analysis method, the 2-dimensional linguistic partitioned Maclaurin symmetric mean (2DLPMSM) operator, and the 2-dimensional linguistic weighted partitioned Maclaurin symmetric mean (2DLWPMSM) operator. The corresponding properties and special cases are demonstrated. By using these proposed methods, this paper constructs a MAGDM solution framework for the CLDCLS problem. A practical case of the CLDCLS problem is presented to demonstrate the effectiveness, rationality, robustness and superior performance of the proposed method.

Bot detection is a fundamental and crucial task for tracing and mitigating cyber threats in the Internet. This paper aims to address two major limitations of current bot detection systems. First, existing flow-based bot detection approaches ignore structural information of botnets, which lead to false detection. Second, they cannot identify the interactive behavioral patterns among heterogeneous botnet objects. In this paper, we propose a novel bot detection framework, namely Bot-AHGCN, which models fine-grained network flow objects (e.g., IP, response) as a multi-attributed heterogeneous graph and transforms bot detection problem into a semi-supervised node classification task on the graph. Particularly, we first build a multi-attributed heterogeneous information network (AHIN) to model the interdependent relationships among botnet objects. Second, we present a weight-learning based node embedding method, which learns the interactive behavioral patterns among bots and integrates them into weighted similarity graphs. Finally, we perform graph convolution on the learned similarity graphs to characterize more comprehensive and discriminative features of bots, and feed them into a forward neural network to identify bots. The overall experimental results on two real-world datasets confirm that Bot-AHGCN outperforms the existing state-of-the-art approaches, and presents better interpretability by introducing meaningful meta-paths and meta-graphs.

Association Rule Mining (ARM) is a significant task for discovering frequent patterns in data mining. It has achieved great success in a plethora of applications such as market basket, computer networks, recommendation systems, and healthcare. In the past few years, evolutionary computation-based ARM has emerged as one of the most popular research areas for addressing the high computation time of traditional ARM. Although numerous papers have been published, there is no comprehensive analysis of existing evolutionary ARM methodologies. In this paper, we review emerging research of evolutionary computation for ARM. We discuss the applications on evolutionary computations for different types of ARM approaches including numerical rules, fuzzy rules, high-utility itemsets, class association rules, and rare association rules. Evolutionary ARM algorithms were classified into four main groups in terms of the evolutionary approach, including evolution-based, swarm intelligence-based, physics-inspired, and hybrid approaches. Furthermore, we discuss the remaining challenges of evolutionary ARM and discuss its applications and future topics.

In the smart grid, efficient power supplies require near-real-time users’ electricity usage metering data, but these data might leak users’ private information, e.g., living habits. To address this problem, a number of privacy-preserving data aggregation schemes have been proposed in the literature. In this paper, we present a new type of attack, called malicious data mining attack, by which the adversary can infer a target user’s electricity usage data. When considering this attack, the majority of existing data aggregation schemes have one of the following two shortcomings. In one aspect, the schemes based on homomorphic encryption can output an accurate aggregation result, but most of them are vulnerable to this attack. In another aspect, the schemes based on differential privacy able to withstand this attack, but the random noises introduced prevent accurate aggregation results from being computed. In this paper, we propose a novel data aggregation scheme that is not only secure against the malicious data mining attack, but also capable of outputting an accurate aggregation result. Detailed security analyses indicate that the proposed scheme satisfies the desirable properties for privacy-preserving data aggregation in the smart grid, and the simulated results demonstrate that our proposed scheme enjoys low computation and communication overhead.

Nowadays, computer science has the ability of assisting experts in different application areas. Recently there has been increasing attention in the health area, and there exist different approaches based on artificial intelligence that have been proposed in the diagnosis of several kinds of diseases. In particular, fuzzy systems have been successfully used as Diagnosis Systems, in this way helping doctors to realize a faster and more accurate diagnosis. However, with the emergence of Type-2 Fuzzy Systems, there have been important improvements in handling the uncertainty with respect to traditional Fuzzy Systems (now called Type-1 Fuzzy Systems) in different kinds of problems. In the present paper, a new approach to Fuzzy Diagnosis based on Type-2 Fuzzy Systems is proposed and compared with respect to Type-1 Fuzzy Systems on a set of diagnosis problems, in order to evaluate the relevance of the uncertainty handling in this kind of problems. On the other hand, the paper is also aiming at observing the accuracy behavior in Fuzzy Diagnosis Systems by changing the uncertainty level in the models. Finally, a comparison of Interval Type-2 Fuzzy Systems with respect to General Type-2 Fuzzy Systems for a set of diagnosis problems is presented.

Cyber code analysis is fundamental to malware detection and vulnerability discovery for defending cyber attacks. Traditional approaches resorting to manually defined rules are gradually replaced by automated approaches empowered by machine learning. This revolution is accelerated by big code from open source projects which support machine learning models with outstanding performance. In the context of a data-driven paradigm, this paper reviews recent analytic research on cyber code of malicious and common software by using a set of common concepts of similarity, correlation and collective indication. Sharing security goals in recognizing anomalous code that may be malicious or vulnerable. The ability to do so is not determined in isolation, rather drawn for code correlation and context awareness. This paper demonstrates a new research methodology of data driven cyber security (DDCS) and its application in cyber code analysis. The framework of the DDCS methodology consists of three components, i.e., cyber security data processing, cyber security feature engineering, and cyber security modeling. Some challenging issues are suggested to direct the future research.

In the field of mobile user behavior analysis, clustering algorithm is used to do the user classification . Usually the mobile user’s dataset is mixed, which contains both numerical and categorical type of data. It leads to inaccurate results when doing the user classification using traditional algorithms like K-Means and which are affected by the initialization process extremely. On the other hand, K-Prototypes algorithm is used to process the mixed data. It is difficult to ascertain the coefficient of classification attribute weight. Based on the above problems, this paper proposes a mixed attributes oriented dynamic SOM fuzzy cluster algorithm (D-SOMFCM-OMA) for mobile user classification. Firstly, the algorithm proposed in this paper gives the primary clustering using Self-Organizing feature Map (SOM) to get the initial clustering parameters. As the preprocessing of clustering, this step reduces effects caused by inappropriate initialization. Then, the algorithm utilizes the improved dynamic fuzzy K-Prototypes cluster method in the second-time clustering to classify users dynamically. It calculates weight of all kinds of attributes according to the proportion of the attribute and uses fine-tuned coefficient to adjust the attribute weight. For improving the clustering effect, the algorithm uses Jaccard distance to calculate the distance in the mixed attribute variables. Further, this paper defines the user mean membership threshold which is an indicator to determine whether different groups need to be added. Finally, some comparison experiments are conducted on the UCI standard datasets to show the advantage of the improved fuzzy clustering algorithm oriented the mixed attributes (IFCM-OMA). Also the other experiment using dataset of UCI verify the validity of the algorithm of D-SOMFCM-OMA.

Class imbalanced datasets are common across different domains including health, security, banking and others. A typical supervised learning algorithm tends to be biased towards the majority class when dealing with imbalanced datasets. The learning task becomes more challenging when there is also an overlap of instances from different classes. In this paper, we propose an undersampling framework for handling class imbalance in binary datasets by removing potential overlapped data points. Our methods are designed to identify and eliminate majority class instances from the overlapping region. Accurate identification and elimination of these instances maximise the visibility of the minority class instances and at the same time minimises excessive elimination of data, which reduces information loss. Four methods based on neighbourhood searching with different criteria to identify potential overlapped instances are proposed in this paper. Extensive experiments using simulated and real-world datasets were carried out. Results show comparable performance with state-of-the-art methods across different common metrics with exceptional and statistically significant improvements in sensitivity.

A kernel-based one-class classifier is mainly used for outlier or novelty detection. Kernel ridge regression (KRR) based methods have received quite a lot of attention in recent years due to its non-iterative approach of learning. In this paper, KRR-based one-class classifier (KOC) has been extended for learning using privileged information (LUPI) framework. LUPI-based KOC method is referred to as KOC+ in this paper. This privileged information is available as feature/features of the dataset, but only during training (not during testing). KOC+ utilizes privileged features information differently compared to other features information. It uses this information in KOC+ by the help of so-called correction function . This information helps KOC+ in achieving better generalization performance . Existing and proposed classifiers are evaluated on the datasets taken from UCI machine learning repository and MNIST dataset. Moreover, experimental results exhibit that KOC+ outperforms KOC and other LUPI-based state-of-the-art one-class classifiers. Source code of this paper is provided on the corresponding author’s GitHub homepage: https://github.com/Chandan-IITI/KOCPlus_or_OCKELMPlus_or_OCLSSVMPlus

Internet of Things (IoT) is expanding rapidly and so is the number of devices, sensors and actuators joining this world. IoT devices are an important part of the data collection process in Big Data systems, so by protecting them we support and improve the security of the whole system. ZigBee is a secure communication system for the underlying Internet of Things (IoT) infrastructure. Even though ZigBee has a strong security stack built on a variety of secret keys, ZigBee devices are vulnerable to the side-channel and key extraction attacks. Due to the low cost and limited resources, most ZigBee devices store their secret keys in plaintext. In this paper, we focus on protecting the storage of ZigBee secret keys and show how Physical Unclonable Functions (PUFs) can help the ZigBee devices to be robust tamper-resistant against the physical attacks. The proposed schemes include PUF-based key storage protection and key generation. The experiments in this paper were done using SRAM-PUF. Furthermore, two algorithms were proposed to overcome the defects in the randomness of keys generated using SRAM-PUF and, at the same time, to increase the reliability of these keys. We were able to significantly improve the hardware security of ZEDs by protecting their keying materials using costless, high secure, random, stable and volatile PUF-based secret keys.

This paper proposes a novel top-down approach to rule-based fuzzy systems, one that begins with the product—an equation—and then addresses the unique features of the product, without requiring the reader to know anything about fuzzy sets and systems. The "products" are adaptive variable-structure basis function expansions , where "adaptive variable-structure" means that different subsets of its basis functions are active (non-zero) in different regions of the state space, something that occurs automatically by virtue of the structure of the basis functions, so that the products can be said to "adapt" to locations in the state space. These products are novel candidates for machine learning . Unique features of all products are: (1) Number of basis functions is no longer a variable and is established locally through type-1 or type-2 uncertainty partitioning of each variable; (2) Both coarse and fine sculpting of the state space are achieved, and are described in terms of first- and second-order partitions of the state space, respectively; and (3) Linguistic interpretability is obtained, which may be of value to an end-user. Learning about rule-based fuzzy systems can be greatly compressed by using the top-down approach that is described in this paper.

The big data samples are important source for analytics. However, its relevant/irrelevant information, unspecified variables/scales, noise/null, and so forth impose huge challenges on the analysis of relevance, feature, cause, and evaluation. This paper proposes an evidential analytics to disclose buried information in big data samples. Technically, it models memberships composed of relevant preference and replaces data with these priors. Its operations include generating analytics baselines, reducing variables, identifying sparse features, and inducing rules by taking advantage of evidence. In illustration, a case study of semiconductor manufacturing in UCI secom is presented. It discloses relevant signals, key factors, variables’ thresholds, sparse characteristics, and causal effect of damages buried in normal samples. The contribution of this paper not only contains these achievements but provides priori data for inference.

The choice of the most promising algorithm to model and predict a particular phenomenon is one of the most prominent activities of the temporal data forecasting. Forecasting (or prediction), similarly to other data mining tasks , uses empirical evidence to select the most suitable model for a problem at hand since no modeling method can be considered as the best. However, according to our systematic literature review of the last decade, few scientific publications rigorously expose the benefits and limitations of the most popular algorithms for time series prediction. At the same time, there is a limited performance record of these models when applied to complex and highly nonlinear data. In this paper, we present one of the most extensive, impartial and comprehensible experimental evaluations ever done in the time series prediction field. From 95 datasets, we evaluate eleven predictors, seven parametric and four non-parametric, employing two multi-step-ahead projection strategies and four performance evaluation measures. We report many lessons learned and recommendations concerning the advantages, drawbacks, and the best conditions for the use of each model. The results show that SARIMA is the only statistical method able to outperform, but without a statistical difference, the following machine learning algorithms : ANN , SVM , and k NN-TSPI. However, such forecasting accuracy comes at the expense of a larger number of parameters. The evaluated datasets, as well detailed results achieved by different indexes as MSE , Theil’s U coefficient, POCID, and a recently-proposed multi-criteria performance measure are available online in our repository. Such repository is another contribution of this paper since other researchers can replicate our results and evaluate their methods more rigorously. The findings of this study will impact further research on this topic since they provide a broad insight into models selection, parameters setting, evaluation measures, and experimental setup.

With the continued growth of interoperable software developed for Internet of Things (IoT), there is a growing demand to predict software defect at various testing and operational phases. This paper solves the software defect prediction problem by proposing a novel model with the help of a local tangent space alignment support vector machine (LTSA-SVM) algorithm. The model employs the SVM algorithm as the basic classifier of software defect distribution prediction model. Then, the model parameters are optimized by combining a grid search method and ten-fold cross validation. In the traditional dimensionality reduction algorithms, data loss caused by the poor attributes of data nonlinearity reduces the accuracy of SVM. Aiming at this problem, this paper uses a LTSA algorithm to extract the intrinsic structure of low-dimensional data and performs effective dimension reduction. The SVM algorithm is trained by the reduced dimension data. Finally, the feasibility of the prediction model is verified. Compared with the single SVM and the LLE-SVM prediction algorithm, the prediction model in this paper improves the prediction accuracy and F-measure by 1–4%.

Mapping nodes in multi-layer and heterogeneous networks to low-dimensional vectors has wide applications in community detection, node classification and link prediction, etc. In this paper, a generalized graph representation learning framework is proposed for information aggregation in various multi-layer and heterogeneous networks. Specifically, an aggregation network is firstly obtained by graph transformation, generating potential information links based on the network structure on different layers. A comprehensive measurement of the similarity between different nodes in the aggregation network is then carried out by aggregating the information of nodes’ identities of structure, nearness and attributes etc. Based on the comprehensive similarity values the nodes have, a context graph can be generated using a simple edge percolation method, which provides a basis facilitating some important downstream work such as classification, clustering and prediction etc. We demonstrate the effectiveness of the new framework in identifying subnetworks in a cyberspace network, where it significantly outperforms all the existing baselines. Ministry of Education (MOE) G.Y. and Y.K. were supported by NSSFC 2019-SKJJ-C-005. G.X. was supported by the Ministry of Education (MOE), Singapore, under contract RG19/20.

Leaks in water distribution networks cause a loss of water that needs to be compensated to ensure a continuous supply for all customers. This compensation is achieved by increasing the flow of the network, which entails an undesirable economical expense as well as negative consequences for the environment. For these reasons, detecting and fixing leaks is a relevant task for water distribution companies. This paper proposes a water leak detection method based on a self-supervised classification of flow time series. The aim is to detect the leaks in the network, providing a low false positive rate. The proposed method is applied to two water distribution networks and compared to two other methods in the literature, obtaining the best balance between the number of false positives and detected leaks.

In this paper, we study the propagation characteristics of rumors in an institution and develop the rumor dissemination model inside the institution. Different from the traditional model, a person in the proposed model can make a judgement about the message and decide to propagate it or refute it, and most people inside the institution can refute the rumor spreaders and propagate the genuine messages to uninformed people when they have confirmed the message is a rumor. Then, we split all the people into two institutions (inside and outside). Since the rumors and genuine messages from the institution can have a non-negligible impact on people outside the institution, we put forward a new double-institution rumor propagation model , and the model considers the impact of messages on the inside and outside of the institution simultaneously. Based on the two proposed models, the basic reproduction numbers are obtained respectively, and the local and global stability of the rumor-free equilibrium points are discussed separately. We numerically simulate the propagation of rumors in small-world networks. The simulation is carried out to verify the validity of the proposed model, and our model is closer to the reality than traditional models.

The Internet of Things (IoT) is commonly deployed with devices of limited power and computation capability. A centralized IoT architecture provides a simplified management for IoT system but brings redundancy by the unnecessary data traffic with a data center. A decentralized IoT reduces the cost on data traffic and is resilient to the single-point-of-failure. The blockchain technique has attracted a large amount of research, which is redeemed as a perspective of decentralized IoT system infrastructure. It also brings new privacy challenges for that the blockchain is a public ledger of all digital events executed and shared among all participants. The decentralized IoT system relies on the leader election deeply to implement the decentralized communications among the distributed nodes. The conventional leader election must have a centralized authority, contrasting to the decentralization. As an alternative, self-tallying type schemes have been proposed in the literature for decentralized systems. These schemes suffer from adaptive and abortive issues. Also, some additional factors should be considered, such as the availability of candidate nodes. If the candidate node is unavailable after the voting phase due to being offline or ongoing tasks, the next available candidate should be elected. To accommodate such a need, in this paper, we propose a new leader election paradigm called voluntary-tallying leader election, which achieves the core requirements such as ballet secrecy, voter privacy and the additional feature of voluntary-tallying. We formalize the system and security models for this new election paradigm and present a secure and practical construction.

Nowadays, crowdsourcing is a widespread and effective method to gather the crowd wisdom. At the same time, label aggregation is used to aggregate the noisy and biased data generated by the crowd. In the real-world crowdsourcing tasks, most workers only answer a small fraction of questions, which makes the collected answer sparse. However, the existing label aggregation approaches often build upon some probabilistic modeling procedures which is sensitive to the data sparsity. In this paper, we exploit the predicted answers to improve the performance of label aggregation and propose PLA (Prediction-based Label Aggregation) to intelligently aggregate the crowd wisdom. With PLA, we firstly learn representations to capture the characteristics of the workers and questions. Then we deploy a neural network model to predict the answer given by different workers. After that we add the most valuable predicted answers to the answer set. Finally, we use the augmented answer set to enhance representative label aggregation algorithms. To validate our proposed PLA, we compare it with other 6 existing methods on 8 real-world datasets. Our results show that PLA can enhance the performance of different aggregation algorithms in crowdsourcing tasks and achieves up to 16% performance improvement.

The data clustering problem can be described as the task of organizing data into groups, where in each group the objects share some similar attributes. Most of the problems clustering algorithms address do not have a prior solution. This paper addresses the algorithm selection challenge for data clustering, while taking the difficulty in evaluating clustering solutions into account. We present a new meta-learning method for recommending the most suitable clustering algorithm for a dataset. Based on concepts from the isolation forest algorithm, we propose a new similarity measure between datasets. Our proposed dataset characterization methods generate an embedding for a dataset using this similarity measure, which is then used to improve the quality of the problem’s characterization. The method utilizes landmarking concepts to characterize the dataset and then, inspired by the DeepFM algorithm, applies meta-learning to rank the candidate algorithms that are expected to perform the best for the current dataset. This ranking could, among other things, support AutoML systems. Our approach is evaluated on a corpus of 100 publicly available benchmark datasets. We compare our method’s ranking performance to that of existing meta-learning methods and show the dominance of our method in terms of predictive performance and computational complexity.

This paper presents an efficient graph semisupervised learning (GSSL) method that meets the criterion of optimization without iterations. Most existing GSSL methods require iterative optimization to achieve a preset objective because they consider data points to be in peer-to-peer relationships. Additionally, existing GSSL methods must learn from scratch for unseen data because graph structures are specifically built for a given dataset. By leveraging the partial order relationships induced by the local density and distances between data, we developed a novel label propagation algorithm based on the data structure of an optimal leading forest (OLeaF). The time complexity of our method is O(N) for both labeling unclassified data and labeling new data from a dataset after an OLeaF is constructed. Therefore, the two main weaknesses of traditional GSSL are addressed. Additionally, the constructed leading forest offers good interpretability for learning results. We scale the proposed method to accommodate big data by utilizing the block distance matrix technique and locality-sensitive hashing. Extensive experiments on datasets with different characteristics demonstrate the superior efficiency and competitive accuracy of the proposed method.

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.

Network embedding has been successfully used for a variety of tasks, e.g., node clustering, community detection, link prediction and evolution analysis on complex networks. For a given network, embedding methods are usually designed based on first-order proximity, second-order proximity, community constraints, etc. However, they are incapable of capturing the structural similarity of nodes. The bridge nodes with small proximity and located in different communities, should be similar in embedding space since they have the same surrounding structure. In this paper, these structural features are referred to as lower-order information, which could reveal and modify the structural similarity of nodes in the embedding space. Specifically, we propose to construct the feature matrix with the lower-order information of the network. In order to effectively fuse the structural features of nodes into embedding space, an intuitive, interpretable and feasible method named LONE-NMF is proposed, which adopts the representation learning framework based on non-negative matrix factorization. It can effectively learn the representation vectors of nodes in the network via preserving the proximity and lower-order information. Moreover, an optimization algorithm is designed for LONE-NMF. Extensive experiments based on clustering and link prediction show that the proposed method achieves significant performance improvement comparing with some baselines. Finally, we validate the principle and advantage of LONE-NMF through a case study.

Deep learning technology is widely used in many modern fields and a number of models and software frameworks have been proposed. However, it is still very difficult to process deep learning tasks efficiently on traditional high performance computing (HPC) systems. In this paper, we propose swFLOW: a large-scale distributed framework for deep learning on Sunway TaihuLight. Based on the performance analysis results of convolutional neural network (CNN), we optimize the convolutional layer , and get 10.42× speedup compared to the original version. As for distributed training, we use elastic averaging stochastic gradient descent (EASGD) algorithm to reduce communication. On 512 processes, we get a parallel efficiency of 81.01% with communication period τ = 8 . Particularly, a decentralized implementation of distributed swFLOW system is presented to alleviate bottleneck of the central server. By using distributed swFLOW system, we can scale the batch size up to 4096 among 1024 concurrent processes for cancerous region detection algorithm . The successful application on swFLOW reveals the great opportunity for joint combination of deep learning and HPC system.

The vehicle fuel consumption gauge is a vehicle’s basic device that usually records the instantaneous as well as average fuel consumption of the vehicle, which brings a lot of convenience during the driving process. The individual real-time fuel consumption data are meaningless, but its continuous real-time fuel consumption data contains some information that may reveal the user’s privacy, which is sensitive information for users. There are hot topics on privacy protection of sensitive user information, however, most of the studies have focused on privacy protection at the time of data release and the need for a trusted third party. In this paper, we propose a negative survey-based approach that can be utilized to protect aggregated vehicle fuel consumption data against time-series-based differential attacks. Its anonymous algorithm on the user side and estimation algorithm on the server side are able to protect the user’s privacy regardless of changes in the user’s fuel consumption data. The experiment results show that our method can achieve better privacy protection with better application prospects in a simpler and more effective way.

In this paper, a hybrid model with both clustering mechanism and regression mechanism is built with aid of an allocation of information granularity . Some numeric prototypes constructed by clustering methods are expanded into granular prototypes. The regression module accepts granular prototypes and outcomes information granules. Some advantages of this idea can be concluded: 1). The constructed granular prototypes are best able to represent original data with comparable smaller quantity and high quality. 2). The granular output could be used to predict a new sample’s real number output by giving a rational range. 3). It can be used to solve clustering and regression problems simultaneously. Two different granularity allocation strategies are proposed and experimented while constructing granular prototypes: non-uniformly allocation of information granularity to each cluster and non-uniformly allocation to each feature. A comprehensive objective function is defined considering specificity and generality of the output. The allocation of granularity itself is in fact a multiple-parameters optimization problem which invokes the usage of an evolutionary method. Two popular methods (GA and PSO) are tried and compared with a real data set’s experiment. Three data sets are collected from UCI website to testify the effectiveness of our approach in the experimental part.

The tree ensemble model has been widely employed as a loan evaluation method in credit risk assessment due to its high accuracy and robustness. However, the tree ensemble model is complex and incomprehensible, which restricts its adoption for decision-making in loan evaluation. In this paper, we propose a novel rule extraction method for improving the ensemble model by balancing predictive performance and interpretability in two stages: a local rule extraction method followed by a global rule extraction method. The local method simplifies each rule by removing its redundant constraints, while the global method optimizes the complete rule set based on the multiobjective optimization method. An interpretable rule-based model is extracted from the tree ensemble model via the proposed method. Comparing the performance to six other methods on three loan evaluation datasets, the proposed method shows superior interpretability and realizes similar predictive performance to the tree ensemble model. For practical loan evaluation, the proposed method provides decision-makers with an interpretable rule-based model, which could replace the opaque tree ensemble model in high-stakes decision-making. In addition, the proposed method could facilitate decision-makers in explaining the tree ensemble model by analyzing the important and valuable rules that are extracted from the original opaque model.

The increasing usage of machine-learning models in critical domains has recently stressed the necessity of interpretable machine-learning models. In areas like healthcare, finary – the model consumer must understand the rationale behind the model output in order to use it when making a decision. For this reason, it is impossible to use black-box models in these scenarios, regardless of their high predictive performance. Decision forests, and in particular Gradient Boosting Decision Trees (GBDT), are examples of this kind of model. GBDT models are considered the state-of-the-art in many classification challenges, reflected by the fact that the majority of Kaggle’s recent winners used GBDT methods as a part of their solution (such as XGBoost). But despite their superior predictive performance, they cannot be used in tasks that require transparency. This paper presents a novel method for transforming a decision forest of any kind into an interpretable decision tree. The method extends the tool-set available for machine learning practitioners, who want to exploit the interpretability of decision trees without significantly impairing the predictive performance gained by GBDT models like XGBoost. We show in an empirical evaluation that in some cases the generated tree is able to approximate the predictive performance of a XGBoost model while enabling better transparency of the outputs.

In the field of machine learning , collected data always have additional features which are always referred as privileged information. Privileged information learning is mainly used to help train the classifier in the training process, and predict the unseen example by the learned classifier. In this paper, we propose a new method named twin support vector machines with privileged information (TWSVM-PI). In the proposed method, we first introduce the privileged information into twin SVMs so as to construct a model for prediction, and then utilize the Lagrangian multiplier method to optimize the proposed objective function. Thus, we obtain two nonparallel classification hyperplanes by solving two smaller sized quadratic programming problems (QPPs), which can shorter the computational time and improve the accuracy of the prediction. Finally, we conduct extensive experiments to evaluate the performance of the proposed TWSVM-PI method. The results have shown that our proposed method can obtain a better performance compared with state-of-the-art methods.

Fundamental information extraction tasks, such as relation extraction and event detection, suffer from a data imbalance problem. To alleviate this problem, existing methods rely mostly on well-designed loss functions to reduce the negative influence of imbalanced data. However, this approach requires additional hyper-parameters and limits scalability. Furthermore, these methods can only benefit specific tasks and do not provide a unified framework across relation extraction and event detection. In this paper, a Classifier-Adaptation Knowledge Distillation (CAKD) framework is proposed to address these issues, thus improving relation extraction and event detection performance. The first step is to exploit sentence-level identification information across relation extraction and event detection, which can reduce identification errors caused by the data imbalance problem without relying on additional hyper-parameters. Moreover, this sentence-level identification information is used by a teacher network to guide the baseline model’s training by sharing its classifier. Like an instructor, the classifier improves the baseline model’s ability to extract this sentence-level identification information from raw texts, thus benefiting overall performance. Experiments were conducted on both relation extraction and event detection using the Text Analysis Conference Relation Extraction Dataset (TACRED) and Automatic Content Extraction (ACE) 2005 English datasets, respectively. The results demonstrate the effectiveness of the proposed framework.

Early warning is a vital component of emergency repsonse systems for infectious diseases. However, most early warning systems are centralized and isolated, thus there are potential risks of single evidence bias and decision-making errors. In this paper, we tackle this issue via proposing a novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts, aiming to crowdsource early warning tasks to distributed channels including medical institutions, social organinzations, and even individuals. Our framework supports two surveillance modes, namely, medical federation surveillance based on federated learning and social collaboration surveillance based on the learning markets approach, and fuses their monitoring results on emerging cases to alert. By using our framework, medical institutions are expected to obtain better federated surveillance models with privacy protection, and social participants without mutual trusts can also share verified surveillance resources such as data and models, and fuse their surveillance solutions. We implemented our proposed framework based on the Ethereum and IPFS platforms. Experimental results show that our framework has advantages of decentralized decision-making, fairness, auditability, and universality, and also has potential guidance and reference value for the early warning and prevention of unknown infectious diseases.

Materialized view selection (MVS) is the problem of selecting an appropriate set of views to be materialized to speed up analytical query processing of data warehouses. Online analytical processing (OLAP) of queries is an essential application of the MVS problem, in which, the response times of the queries are reduced by storing the selected views. Views are intermediate results of query processing and are selected in the MVS problem to be stored and will then be exploited in answering process of several queries. Views are usually organized as a view representation structure in the MVS problem. Multiple Views Processing Plan (MVPP) is a standard structure used for view representation in the MVS problem. Due to the tremendous amount of data, constructing the MVPP is a challenge in the big data applications. The MR-MVPP (Map-Reduce-based construction of the MVPP) is the proposed method of this paper to address this problem. The MR-MVPP performs a set similarity join (similarity-based join) on the base relations and views using the map-reduce model and the hashing technique. The MVPP construction time in the proposed method is reduced by avoiding redundant calculations in the process of creating the MVPP. The performance of the proposed method is empirically evaluated. According to the results of the experiments, the execution time of the MR-MVPP method is better than the other methods. The average time improvement is about 26.5 units. This improvement is better than the other similar researches in this area and is significant due to the high volume of data in real applications. Moreover, the proposed method works well in terms of the effectiveness of the created MVPP and has about a 50% coverage rate for view selection methods. Deterministic methods are more accurate than hashing methods and can be utilized for set similarity join as future work to probably improve the effectiveness of the constructed MVPP.

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context. Different approaches have been proposed in supervised and unsupervised domains. In most cases, supervised learning provides superior WSD performance. Since sense-annotated corpora can be difficult or time-consuming to obtain, which must be repeated for new domains, languages, and sense inventories, semi-supervised learning (SSL) methods, that combine a small amount of sense-annotated data, start to be pre-eminent. In SSL, graph-based methods are common, because they capture the relationships between terms using an undirected graph. This paper aims to investigate semi-supervised WSD by considering different graph-based SSL algorithms with features generated by word embeddings from Word2Vec, FastText, GloVe, BERT and ELECTRA models combined with parts-of-speech tags and word context. We test several combinations of word-embedding models, similarity measures for graph construction and SSL classification algorithms to disambiguate classical lexical sample WSD datasets. The results indicate our SSL algorithms achieved competitive results compared to supervised ones and the ELECTRA models performed better than other embeddings for SSL.

A high-quality recommender system (RS) can effectively alleviate information overload by producing recommendations. The quality of the recommender system not only depends on the recommendation algorithm but also on the quality of collected data. Since users are often affected by environmental and accidental factors during the rating process, natural noise is probably brought into the data of RS by non-malicious users, which will lead to deviations in prediction results. In this paper, we propose a scheme based on fuzzy theory to manage the natural noise in RS. We first classify the ratings into three fuzzy categories with variable boundaries. Then, the fuzzy profiles of users and items are built to detect the natural noise in ratings. Finally, once the noisy ratings are detected, we replace them with the rating threshold values according to the Maximum membership principle. The proposed scheme is tested in two benchmark datasets and experimental results verify that the scheme can significantly improve the recommendation quality and has higher efficiency than the schemes based on re-predication.

Subgraph searching is an essential problem in graph databases, but it is also challenging due to the involved subgraph isomorphism NP-Complete sub-problem. Filter-Then-Verify (FTV) methods mitigate performance overheads by using an index to prune out graphs that do not fit the query in a filtering stage, reducing the number of subgraph isomorphism evaluations in a subsequent verification stage. Subgraph searching has to be applied to very large databases (tens of millions of graphs) in real applications such as molecular substructure searching. Previous surveys have identified the FTV solutions GraphGrepSX (GGSX) and CT-Index as the best ones for large databases (thousands of graphs), however they cannot reach reasonable performance on very large ones (tens of millions graphs). This paper proposes a generic approach for the distributed implementation of FTV solutions. Besides, three previous methods that improve the performance of GGSX and CT-Index are adapted to be executed in clusters. The evaluation shows how the achieved solutions provide a great performance improvement (between 70% and 90% of filtering time reduction) in a centralized configuration and how they may be used to achieve efficient subgraph searching over very large databases in cluster configurations This work has been co-funded by the Ministerio de Economía y Competitividad of the Spanish government, and by Mestrelab Research S.L. through the project NEXTCHROM (RTC-2015-3812-2) of the call Retos-Colaboración of the program Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad. The authors wish to thank the financial support provided by Xunta de Galicia under the Project ED431B 2018/28 SI

High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task of HUSPM is to find all subsequences, within a quantitative sequential database, that have high utility with respect to a user-defined minimum utility threshold. However, it is difficult to specify the minimum utility threshold, especially when database features, which are invisible in most cases, are not understood. To handle this problem, top- k HUSPM was proposed. Up to now, only very preliminary work has been conducted to capture top-k HUSPs, and existing strategies require improvement in terms of running time, memory consumption, unpromising candidate filtering, and scalability. Moreover, no systematic problem statement has been defined. In this paper, we formulate the problem of top-k HUSPM and propose a novel algorithm called TKUS. To improve efficiency, TKUS adopts a projection and local search mechanism and employs several schemes, including the Sequence Utility Raising, Terminate Descendants Early, and Eliminate Unpromising Items strategies, which allow it to greatly reduce the search space. Finally, experimental results demonstrate that TKUS can achieve sufficiently good top-k HUSPM performance compared to state-of-the-art algorithm TKHUS-Span.

Three-dimensional (3D) point cloud semantic segmentation is an essential part of computer vision for scene comprehension. Nevertheless, due to their loss of detail, existing networks lack the ability to recognize complex scenes. This paper proposes a novel network architecture , called the ring grouping neural network with attention module (RGAM), which presents four improvements over the existing networks. First, novel multi-scale ring grouping learning is designed to extract the multi-scale neighborhood features without overlapped sampling, allowing the network to adapt to objects of different scales. Second, neighborhood information fusion is defined as the weighted sum of multiple neighborhood features, enabling the representation of each point to be considered in different neighborhoods. Third, in the global view, a spatial attention module is introduced among the neighborhoods, allowing long-range contextual information to be exploited for 3D point cloud semantic segmentation. Finally, a channel attention module is appended to the RGAM: the correlation of each channel with key information enhances the complex scene recognition ability of the RGAM. Experimental results on the challenging S3DIS, ScanNet, and NYU-V2 datasets demonstrate that the RGAM has stronger recognition ability than the existing networks based on several state-of-the-art algorithms for 3D point cloud semantic segmentation.

The rapid development of 5G communication technology will considerably help the expansion and gth Industrial Internet of things (IIoT). Indeed, both the data scale and dimension will significantly increase, leading to a challenging problem of the effective real-time stream clustering in the field of IIoT streaming mining. This paper proposes an efficient and novel real-time kernel function stream clustering method based on shared nearest-neighbor density for IIoT. In the proposed method, the projection technology is used to select the dimensions of high-dimensional data, while the Euler kernel function is used as the similarity measure. Furthermore, the micro-clusters are divided by the shared nearest-neighbor density, and the outliers are relearned. The main innovation lies in using the Euler kernel function to measure the similarity, reduce the sensitivity of outliers, and use the relearning strategy to improve the clustering quality of the data stream. The theoretical analysis and experimental comparisons on the simulated data sets show that the proposed method is very effective and represents a good solution for clustering real-time data streams of IIoT.

Cloud computing has become a popular technology for executing scientific workflows. However, with a large number of hosts and virtual machines (VMs) being deployed, the cloud resource failures, such as the permanent failure of hosts (HPF), the transient failure of hosts (HTF), and the transient failure of VMs (VMTF), bring the service reliability problem. Therefore, fault tolerance for time-consuming scientific workflows is highly essential in the cloud. However, existing fault-tolerant (FT) approaches consider only one or two above failure types and easily neglect the others, especially for the HTF. This paper proposes a Real-time and dynamic Fault-tolerant Scheduling (ReadyFS) algorithm for scientific workflow execution in a cloud, which guarantees deadline constraints and improves resource utilization even in the presence of any resource failure. Specifically, we first introduce two FT mechanisms , i.e., the replication with delay execution (RDE) and the checkpointing with delay execution (CDE), to cope with HPF and VMTF, simultaneously. Additionally, the rescheduling (ReSC) is devised to tackle the HTF that affects the resource availability of the entire cloud datacenter. Then, the resource adjustment (RA) strategy, including the resource scaling-up (RS-Up) and the resource scaling-down (RS-Down), is used to adjust resource demands and improve resource utilization dynamically. Finally, the ReadyFS algorithm is presented to schedule real-time scientific workflows by combining all the above FT mechanisms with RA strategy. We conduct the performance evaluation with real-world scientific workflows and compare ReadyFS with five vertical comparison algorithms and three horizontal comparison algorithms. Simulation results confirm that ReadyFS is indeed able to guarantee the fault tolerance of scientific workflow execution and improve cloud resource utilization.

Three-way decision (3WD) provides a new perspective and methodology for solving multi-attribute decision-making (MADM) problem. In this paper, we introduce 3WD into a multi-scale decision information system (MS-DIS), which provides a new idea for solving MADM issues in MS-DISs. By using fuzzy membership functions , a multi-scale evaluation information table is first converted into a numerical evaluation value table. To calculate loss functions at two states generated by the decision class partition, a distance-based cost measurement is then defined. With reference to fuzzy σ -neighborhood classes, conditional probabilities are further calculated and 3WD rules from MS-DISs can be unravelled. As a result, the ranking about all objects in the data is obtained according to expected losses. Finally, a specific numerical example is used to illustrate the effectiveness of the proposed method comparing with other ones. An experimental analysis is also used to describe the superiority of the proposed method.

Automatic understanding of floor plan images is a key component of various applications. Due to the style diversity of rural housing design, the latest learning-based approaches cannot achieve satisfactory recognition results. In this paper, we present a new framework for parsing floor plans of rural residence that combines semantic neural networks with a post-processed room segmentation. First, we take case studies from typical residential buildings in China’s rural areas and provide a novel image dataset, called RuralHomeData, containing 800 rural residence floor plans with accurate man-made annotations. Based on the dataset, we propose a new deep learning-based recognition framework using a joint neural network to predict the geometric elements and text information on the floor plan simultaneously. Our insight is that walls and openings (doors and windows) are the basic elements corresponding to the room boundary that a closed 1D loop must form a certain room. Then the semantic information (e.g., the room function) of room regions can be obtained through text detection and identification. Furthermore, we use the MIQP algorithm to divide the area containing multiple room type texts into multiple room areas. Finally, the input floor plan can be transformed into a room layout graph with room attributes and adjacent relationships. The proposed algorithm has been tested on both urban and rural datasets, and the experimental results demonstrate our efficiency and robustness compared with the state-of-the-art methods.

Novelty detection aims to identify novel instances in the test data that differ in some respect from the normal instances in the training data. Novel instances may be defined and interpreted in different ways. We consider a specific interpretation where novel instances are instances from unknown classes which are not seen during the training phase. This is also sometimes referred to as open world classification or open set recognition. A key challenge in this scenario is to design approaches that effectively classify normal instances and reject the classification of novel instances. Three-way decisions may be realized as a useful strategy to deal with this challenge. It provides provision for deferring the decisions of classifying objects whenever the available evidence is not enough. The deferred cases may be realized as novel or unknown since their classification results are not known and not available. Three-way clustering is an important three-way decision model which can be used for the classification of objects by considering classes as clusters in the data. In this paper, we introduce a three-way clustering based algorithm called reduction and elevation based three-way clustering for open world classification or RE3OWC. A three-way cluster consists of a pair of core and support sets. The RE3OWC uses the operations of reduction and elevation to define the core and support of a three-way cluster. The two sets lead to the three regions of inside, partial and outside corresponding to a cluster. The three regions provide the realization of three-way decisions and are used to identify instances from the unknown classes. Experimental results on datasets of 20 Newsgroups and Amazon reviews suggest improvements in commonly and widely used F1 measure by up to 2.3% and 6.5%, respectively, in comparisons to some of the best known available approaches of DOC, cbsSVM, openMax and others, for identifying instances from unknown classes.

The rough set theory is a nonstatistical mathematical approach to address the issues of vagueness and uncertain knowledge The rationale of this theory relies on associating a subset with two crisp sets called lower and upper approximations which are utilized to determine the boundary region and accuracy measure of that subset Neighborhoods systems are pivotal technique to reduce the boundary region and improve the accuracy measure Therefore, we aim through this paper to introduce new types of neighborhoods called containment neighborhoods (briefly, Cj-neighborhoods) They are defined depending on the inclusion relations between j-neighborhoods under arbitrary binary relation We study their relationships with some previous types of neighborhoods, and determine the conditions under which they are equivalent Then, we applied Cj-neighborhoods to present the concepts of Cj-lower and Cj-upper approximations and reveal main properties with the help of examples We also prove that a Cj-accuracy measure is the highest in cases of j=i,〈i〉 Furthermore, we compare our approach with two approaches given in published literatures and show that accuracy measure induced from our technique is the best Finally, we successfully applied Cj-neighborhoods, Nj-neighborhoods and Ej-neighborhoods in a medical application aiming to classify medical staff in terms of suspected infection with the new corona-virus (COVID-19)