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Information Sciences
Journal Prestige (SJR): 1.635
Citation Impact (citeScore): 5
Number of Followers: 245  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0020-0255
Published by Elsevier Homepage  [3161 journals]
  • Context-based prediction for road traffic state using trajectory pattern
           mining and recurrent convolutional neural networks
    • Abstract: Publication date: Available online 18 September 2018Source: Information SciencesAuthor(s): Jia Zhu, Changqin Huang, Min Yang, Gabriel Pui Cheong FungAbstractWith the broad adoption of the global positioning system-enabled devices, the massive amount of trajectory data is generated every day, which results in meaningful traffic patterns. This study aims to predict the future state of road traffic instead of merely showing the current traffic condition. First, we use a clustering method to group similar trajectories of a particular period together to get a cluster for each road. Second, for each cluster, we average the lengths and angles of the entire trajectories in the group as the representative trajectory, which is regarded as the road pattern. Third, we create a feature vector for each road based on its historical traffic conditions and neighbor road patterns. Finally, we design a recurrent convolutional neural network for modeling the complex nonlinear relationship among features for predicting road traffic conditions. Experimental results show that our approach performs more favorably compared with several traditional machine learning and state-of-the-art algorithms.
  • A Top-K retrieval algorithm based on a decomposition of ranking functions.
    • Abstract: Publication date: Available online 18 September 2018Source: Information SciencesAuthor(s): Nicolás Madrid, Pavel RusnokAbstractA Top-k retrieval algorithm returns the k best answers of a query according to a given ranking. From a theoretical point of view, the solution of this query is straightforward if we do not take into consideration execution time. However, in many practical situations, execution time is a substantial issue. In such a case, the process of getting the top k in an optimal time becomes an interesting and non-trivial task. This paper presents an algorithm to retrieve the top-k associated to an arbitrary ranking function. The idea is to decompose the ranking function as a supremum of a certain set of functions where an efficient top-k retrieval procedure can be easily applied. Besides the theoretical presentation of the algorithm, the paper provides a set of experiments to validate the approach.
  • A New Threshold Changeable Secret Sharing Scheme Based on the Chinese
           Remainder Theorem
    • Abstract: Publication date: Available online 17 September 2018Source: Information SciencesAuthor(s): Xingxing Jia, Daoshun Wang, Daxin Nie, Xiangyang Luo, Jonathan Zheng SunAbstractA general (t, n) secret sharing (SS) scheme with fixed threshold allows a secret to be shared without considering the time dynamic nature of the security environment. In this paper, we propose a threshold changeable secret sharing scheme whose threshold can be changed in an integer interval [t, t′] without updating the shares. In this scheme, a different threshold can be activated at any time through the public broadcast channel. At the heart of the proposed scheme is a novel matrix of primes. The validity of the share generation and secret reconstruction is provided by the Chinese Remainder Theorem (CRT). We prove the existence of the proposed matrix and present a method to efficiently construct it, which makes use of a proposed sequence of nested closed intervals generated by large co-prime numbers. We further use the structure to propose a scheme with computational security, without maintaining online dealer. Compared with previous methods, the proposed scheme has short share size and low complexity for recovery. For any changeable threshold in [t, t′], the increase in share size is at most 1t−1 of that from previous methods. Computational complexity for secret recovery is O(t), compared with O(tlog2t) of the best previous methods.
  • An Evolutionary Context-aware Sequential Model for Topic Evolution of Text
    • Abstract: Publication date: Available online 17 September 2018Source: Information SciencesAuthor(s): Ziyu Lu, Haihui Tan, Wenjie LiAbstractSocial media acts as the platform for users to acquire information and spreads out breaking news. The overwhelming amount of fast-growing information makes it a challenge to track the subsequences of the breaking news or events and find the corresponding user opinions towards special aspects. Tracking the evolution of an event and predicting its subsequent trends play an important role in social media. In this paper, we propose an Evolutionary Context-aware Sequential model (ECSM) to track the evolutionary trends of the streaming text and investigate their focused context-aware topics. We integrate two novel layers into the Recurrent Chinese Restaurant Process (RCRP), respectively one context-aware topic layer and one Long Short Term Memory (LSTM) based sequential layer. The context-aware topic layer can help capture the global context-aware semantic coherences and the sequential layer is exploited to learn the local dynamics and semantic dependencies during the dynamic evolutionary process. Experimental results on real datasets show that our method significantly outperforms the state-of-the-art approaches.
  • A Local Pilot Auxiliary Key Generation Scheme for Secure Underwater
           Acoustic Communication
    • Abstract: Publication date: Available online 17 September 2018Source: Information SciencesAuthor(s): Zhiwei Shen, Jingmei Liu, Qingqing HanAbstractTo effectively extend physical layer key generation techniques to the underwater acoustic channel, a novel underwater key generation scheme is proposed. First, a local pilot auxiliary wait channel sounding protocol is proposed to solve impaired reciprocity problems caused by the great transmission delay. At the same time, the protocol ensures high key entropy and prevents the nearby eavesdroppers. Second, two traditional enhancing mechanisms are applied to improve the key agreement. Third, an assembly line protocol is proposed to solve the problem that the key generation rate decreases rapidly with the increase of communication distance. Fourth, a double-layer compensation centralized quantization combined with adaptive guard interval quantization method is proposed to improve the rate of key agreement and key generation. Finally, simulation results show that the bit error rate is lower than 0.01, and the key generation rate is higher than 600 bits/s, which verify the superiority of the proposed scheme.
  • Parallel Approach to Incremental Co-location Pattern Mining
    • Abstract: Publication date: Available online 17 September 2018Source: Information SciencesAuthor(s): W. Andrzejewski, P. BoinskiAbstractCo-location pattern mining is an important area of spatial data mining. In many real word applications, new data is continuously arriving to the system and is stored in spatial databases. As co-location discovery is computationally demanding task, it is crucial to maintain co-location patterns for such dynamic databases without recalculating them from scratch. In this paper we present the first GPU-parallelized solution to this problem. Our contribution is threefold: 1) we present a modified version of EUCOLOC algorithm using the iCPI-tree method called iCPI-EUCOLOC, 2) we modify state-of-the-art MGPUCPM algorithm to implement iCPI-EUCOLOC algorithm on GPUs and 3) we present experimental results showing large performance improvements over the original MGPUCPM algorithm. Our solution allows to reduce user waiting times and is economically beneficial due to the reduced overall computation time.
  • Blind Assessment for Stereo Images Considering Binocular Characteristics
           and Deep Perception Map Based on Deep Belief Network
    • Abstract: Publication date: Available online 15 September 2018Source: Information SciencesAuthor(s): Jiachen Yang, Yang Zhao, Yinghao Zhu, Huifang Xu, Wen Lu, Qinggang MengAbstractIn recent years, blind image quality assessment in the field of 2D image/video has gained the popularity, but its applications in 3D image/video are to be generalized. In this paper, we propose an effective blind metric evaluating stereo images via deep belief network (DBN). This method is based on wavelet transform with both 2D features from monocular images respectively as image content description and 3D features from a novel depth perception map (DPM) as depth perception description. In particular, the DPM is introduced to quantify longitudinal depth information to align with human stereo visual perception. More specifically, the 2D features are local histogram of oriented gradient (HoG) features from high frequency wavelet coefficients and global statistical features including magnitude, variance and entropy. Meanwhile, the global statistical features from the DPM are characterized as 3D features. Subsequently, considering binocular characteristics, an effective binocular weight model based on multiscale energy estimation of the left and right images is adopted to obtain the content quality. In the training and testing stages, three DBN models for the three types features separately are used to get the final score. Experimental results demonstrate that the proposed stereo image quality evaluation model has high superiority over existing methods and achieve higher consistency with subjective quality assessments.
  • Spatial-domain fitness landscape analysis for combinatorial optimization
    • Abstract: Publication date: Available online 14 September 2018Source: Information SciencesAuthor(s): Hui Lu, Rongrong Zhou, Zongming Fei, Chongchong GuanAbstractFitness landscape analysis has been effectively used to analyze the characteristics of combinatorial optimization problems (COPs) while investigating the behavior of applied algorithms. However, most COPs are high dimensional and an intuitive understanding of traditional fitness landscape analysis is challenging. To address this issue, the present study proposes a spatial-domain fitness landscape analysis framework. This framework aims to visualize the fitness landscapes regarding of a target COP and evaluate the properties of the COP. To construct the landscapes, a mapping strategy inspired by lexicographic order is designed to establish the connection between the high-dimensional and low-dimensional spaces. Accordingly, a spatial-domain parameter, namely, the slope of the digital elevation models (DEMs), is utilized to assess the ruggedness of landscapes. As an auxiliary value, the neutrality ratio is introduced to reflect the degree of neutrality. In addition, a parameter control mechanism based on these landscape metrics is advanced to enhance the adaptability of the algorithm. Finally, the experimental results reveal that the spatial-domain fitness landscape can help extract the characteristics of COPs and study the behavior of algorithms effectively. In addition, the proposed adaptive parameter control strategy significantly improves the performance of the algorithm for solving COPs.
  • Identity-based key-exposure resilient cloud storage public auditing scheme
           from lattices
    • Abstract: Publication date: Available online 14 September 2018Source: Information SciencesAuthor(s): Xiaojun Zhang, Huaxiong Wang, Chunxiang XuAbstractWith the rapid development of cloud auditing services, key exposure has been highlighted as a serious security issue. Using the exposed private key of a client, cloud servers can forge previous auditing proofs to cheat auditors. To date, a few pairing-based cloud storage auditing schemes addressing key exposure have been proposed. However, they are not secure from quantum attacks, and they rely on public key infrastructure (PKI), which involves complex certificate management. In this paper, we propose an efficient identity-based key-exposure resilient public auditing scheme from lattice assumptions in cloud storage. Our scheme is not only quantum-resistant, but eliminates the need to establish a PKI. We employ lattice basis delegation technique to update a client’s private key flexibly, keeping the private key size constant. Based on the hardness of lattice assumptions, we prove the forward security of storage correctness guarantee against malicious cloud servers in detail, and that the proposed scheme preserves privacy against curious auditors. Furthermore, we conduct a performance comparison to demonstrate that our scheme is much more efficient and practical for post-quantum secure cloud storage.
  • Public Auditing for Shared Cloud Data with Efficient and Secure Group
    • Abstract: Publication date: Available online 11 September 2018Source: Information SciencesAuthor(s): Hui Tian, Fulin Nan, Hong Jiang, Chin-Chen Chang, Jianting Ning, Yongfeng HuangAbstractWith increasing popularity of collaboration in clouds, shared data auditing has become an important issue in cloud auditing field, and attracted extensive attention from the research community. However, none of the state of the arts can fully achieve all indispensable functional and security requirements. Thus, in this paper, we present a comprehensive public auditing scheme for shared data. Specifically, to preserve users’ identity privacy, signatures on the challenged blocks are converted to the ones signed by the group manager during proof generation; to protect data privacy, a random masking is adopted to blind data proof; a modification record table is designed to record operation information to support identity traceability; we further extend the dynamic hash table to support shared-data dynamics, and present a batch auditing strategy. Moreover, we design a lazy-revocation based group management mechanism to achieve efficient group dynamics, which can resist collusion attacks while significantly reducing computational costs. We formally prove the security of our scheme, and evaluate its performance by comprehensive experiments and comparisons with the state-of-the-art ones. The results demonstrate that our scheme can effectively achieve secure auditing and outperforms the previous ones in computational overhead while maintaining relatively low communication costs.
  • Stochastic Global Optimization Algorithms: A Systematic Formal Approach
    • Abstract: Publication date: Available online 11 September 2018Source: Information SciencesAuthor(s): Jonatan GomezAbstractUp to now, different algorithmic methods have been developed to find ’good’ solutions (near to the optimal solution) for global optimization problems that cannot be solved using analytical methods. In particular, algorithmic methods such as differential evolution, evolutionary algorithms, and hill climbing belong to the class of Stochastic Global Optimization Algorithms (SGoals). In general, anSGoal iteratively applies stochastic operations to a set of candidate solutions (population), following, in some cases, a heuristic/metaheuristic. Although some research works have tried to formalize SGoals using Markov kernels, such formalization is not general and sometimes is blurred. In this paper, we propose a comprehensive, systematic and formal approach for studying SGoals. First, we present concepts of probability theory that are required to perform such formalization and we demonstrate that the swapping, sorting, and permutation functions, among others, can be represented by Markov kernels. Then, we introduce the joint Markov kernel as a way of characterizing the combination of stochastic methods. Next, we define the optimization space, a σ-algebra that contains ϵ-optimal states, and develop Markov kernels for stochastic methods like swapping, sorting and permutation. Finally, we introduce sufficient convergence conditions for SGoals and present some popular SGoals in terms of the developed theory.
  • Collaborative Block Compressed Sensing Reconstruction with Dual-Domain
           Sparse Representation
    • Abstract: Publication date: Available online 11 September 2018Source: Information SciencesAuthor(s): Yu Zhou, Hainan GuoAbstractIn the past decade, image reconstruction based on compressed sensing (CS) has attracted great interest from researchers in signal processing. Due to the tremendous amount of information that an image contains, block compressed sensing (BCS) is often applied to divide an entire image into non-overlapping sub-blocks, treating all sub-blocks separately. However, an independent reconstruction ignores the correlation between adjacent sub-blocks and results in quality degradation, both in objective and subjective assessments. To obtain a satisfactory reconstructed image, this paper proposes a collaborative BCS (CBCS) framework with dual-domain sparse representation, where local structural information (LSI) and non-local pixel similarity are jointly considered. During a reconstruction, a local data-adaptive kernel regressor is introduced to extract the local image structure, which helps build a correlation of pixels between adjacent sub-blocks and preserves the details of an image. At the same time, a perceptually non-local similarity (PNLS), based on the human visual system, is used to improve visual quality. In addition, we employed both an analysis model and a synthesis model to further enhance sparseness and to formulate a dual-domain sparse representation based BCS reconstruction problem. Finally, an efficient, iterative approach, based on the multi-criteria Nash equilibrium technique, is proposed to solve this problem. Experimental results on benchmark images demonstrate that the proposed method can achieve competitive results both in both numerical and visual comparisons with some state-of-the-art BCS algorithms.
  • Identity-Based Revocation System: Enhanced Security Model and Scalable
           Bounded IBRS Construction with Short Parameters
    • Abstract: Publication date: Available online 10 September 2018Source: Information SciencesAuthor(s): Peng Jiang, Jianchang Lai, Fuchun Guo, Willy Susilo, Man Ho Au, Guomin Yang, Yi Mu, Rongmao ChenAbstractIdentity-based revocation system (IBRS) produces a ciphertext based on the revoked identities such that this ciphertext can only be decrypted by the private keys of non-revoked identities. IBRS can be classified into unbounded IBRS and bounded IBRS, depending on whether to set a maximal revocation number (e.g., N) or not. Compared to unbounded IBRS, existing bounded IBRS schemes have the advantage of short ciphertexts, however, they cannot support an increase of N and have to fix a large N to provide the revocation. This compromises the computational efficiency, which is linear in N. To bridge this gap, we are interested in exploring whether N can be dynamic. Precisely, N is initially set as a small number and increased into a larger one when more than N identities are revoked, while this increase does not affect other non-revoked users in the system. In this paper, motivated by the security of bounded IBRS with a dynamic N, we define an enhanced security model, which allows the corruption of any polynomial number (e.g., n) of private keys, independent of N. It augments existing security models by running different challenges when n ≤ N and n > N, respectively. The enhanced security model suits both unbounded IBRS and bounded IBRS with a general description that can be “empty” for unbounded IBRS or a special N for bounded IBRS. Then, we propose a scalable bounded IBRS scheme and are able to prove its security in an enhanced security model. The increase of N incurs no changing on all of the existing private keys and the only cost is a slight expansion of the master public keys. Our proposed scheme also has short ciphertexts and private keys, comparable to the state-of-the-art (bounded) IBRS schemes.
  • On Operations of Possibilistic Belief Structures
    • Abstract: Publication date: Available online 10 September 2018Source: Information SciencesAuthor(s): Lei Li, Zhi LiAbstractThis paper studies operations on possibilistic belief structures, which are constructed by basic possibility assignments, or equivalently, maxitive belief structures, as a framework for modeling imprecise possibility distributions. Different from Dempster-Shafer structures, it is shown that there is no one-to-one correspondence between basic possibility assignments and their induced upper and lower possibilities. The operations on the set of basic possibility assignments might not induce the operations on their upper and lower possibilities. An operation on basic possibility assignments is called proper if it is also the operation on their upper and lower possibilities. We introduce set operations and point operations on basic possibility assignments. Set operations include intersection, union, complement, difference, projection, marginalization, cylindrical extension, vacuous extension and Cartesian product operations. Point operations include all binary and unary operations. We show that all point operations are proper. For set operations, it is shown that union, projection, marginalization, cylindrical extension, vacuous extension and Cartesian product operations are proper, while intersection, complement and difference operations are not proper. In addition, we study construction of compatible possibility distributions and show that the lower possibility of a basic possibility assignment can be represented by the infinimum of compatible possibility distributions. We also show that the local computation technique can be applied to the computation problem of possibilistic belief structures.
  • An Algorithm for Spelling the Pitches of Any Musical Scale
    • Abstract: Publication date: Available online 10 September 2018Source: Information SciencesAuthor(s): Uzay Bora, Baris Tekin Tezel, Alper VahaplarAbstractIn this paper, we propose a method for the fundamental task of optimally spelling the pitches of any given musical scale. The input, given as a sequence of pitch-class numbers, can be any randomly compiled subset of the chromatic scale, resulting in either a traditional/known scale or a novel/unknown one. The method consists of generating all potential solutions containing all possible spellings for the pitch classes in a given input sequence, and subjecting them to five filtering stages to find the correct solution. We present an algorithm to accomplish this task, and demonstrate some exemplary outputs. Constructing also a modified version of the algorithm to retrieve and execute all possible input sequences, we also present distributions of various outcomes of the procedure over the input universe to exhibit an overall view of results to be produced by the algorithm, along with some findings obtained by this process.
  • Mining Frequent Patterns in Process Models
    • Abstract: Publication date: Available online 6 September 2018Source: Information SciencesAuthor(s): David Chapela-Campa, Manuel Mucientes, Manuel LamaAbstractProcess mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from event logs and by offering techniques to discover, monitor and enhance real processes. In the discovery of process models, retrieving a complex one, i.e., a hardly readable process model, can hinder the extraction of information. Even in well-structured process models, there is information that cannot be obtained with the current techniques. In this paper, we present WoMine, an algorithm to retrieve frequent behavioural patterns from the model. Our approach searches in process models extracting structures with sequences, selections, parallels and loops, which are frequently executed in the logs. This proposal has been validated with a set of process models, including some from BPI Challenges, and compared with the state of the art techniques. Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.
  • Fast Neighbor Search By Using Revised K-D Tree
    • Abstract: Publication date: Available online 6 September 2018Source: Information SciencesAuthor(s): Yewang Chen, Lida Zhou, Yi Tang, Jai Puneet Singh, Nizar Bouguila, Cheng Wang, Huazhen Wang, Jixiang DuAbstractWe present two new neighbor query algorithms, including range query (RNN) and nearest neighbor (NN) query, based on revised k-d tree by using two techniques. The first technique is proposed for decreasing unnecessary distance computations by checking whether the cell of a node is inside or outside the specified neighborhood of query point, and the other is used to reduce redundant visiting nodes by saving the indices of descendant points. We also implement the proposed algorithms in Matlab and C. The Matlab version is to improve original RNN and NN which are based on k-d tree, C version is to improve k-Nearest neighbor query (kNN) which is based on buffer k-d tree. Theoretical and experimental analysis have shown that the proposed algorithms significantly improve the original RNN, NN and kNN in low dimension, respectively. The tradeoff is that the additional space cost of the revised k-d tree is approximately O(αnlog (n)).
  • On the efficient stability computation for the selection of interesting
           formal concepts
    • Abstract: Publication date: Available online 6 September 2018Source: Information SciencesAuthor(s): A. Mouakher, S. Ben YahiaAbstractThe lattice theory under the framework of formal concept analysis has brought mathematical thinking to knowledge representation and discovery. In this respect, this mathematical framework offers a conceptual knowledge representation through the Galois lattice. This hierarchical conceptual structure has been beneficial within the task of knowledge discovery in databases. However, its effective use in large datasets is always limited by the overwhelming number of extracted formal concepts. To select interesting formal concepts, the stability measure can be of valuable help. The dedicated literature has highlighted non-scalable approaches to compute such a stability measure. In an effort to tackle this issue, we introduce the Dfsp algorithm dedicated to efficiently compute the quality measure of the stability of formal concepts. We also show that the stability computation is an instantiation of a larger issue: locating minimal generators given the closed pattern as a reference point. The guiding idea of the Dfsp algorithm is to maximize as far as possible the quantity of the useless search space through the swift localization of maximal non-generator cliques. The experiments performed demonstrate the efficiency of the Dfsp algorithm.
  • Body Surface Feature-based Multi-modal Learning for Diabetes Mellitus
    • Abstract: Publication date: Available online 4 September 2018Source: Information SciencesAuthor(s): Jinxing Li, Bob Zhang, Guangming Lu, Jane You, David ZhangAbstractIn recent year, the number of people who are suffering from the Diabetes Mellitus (DM) has increased remarkably and the detection of DM disease has attracted much attention. Different from some existing methods which are invasive, Traditional Chinese Medicine (TCM) provides a non-invasive strategy for DM diagnosis by exploiting some features in the body surface, including the tongue, face, sublingual vein, pulse and odor. Since a combination of these modalities would contribute to improving detection performance, a novel multi-modal learning method is proposed to learn a shared latent variable among the tongue, face, sublingual, pulse and odor information, which efficiently exploits the correlation. In detail, the raw images or signals of five modalities are first captured through our non-invasive devices. Their corresponding features are then extracted, respectively. Finally, a shared auto-encoder gaussian process latent variable model (SAGP) is introduced to learn a latent variable for various modalities in a non-linear and generative way. An efficient algorithm is designed to optimize the proposed model. The DM detection experiments are conducted on a dataset composed of 548 Healthy and 356 DM samples collected by us and the results substantiate the superiority of the proposed method.
  • A Complete Expected Improvement Criterion for Gaussian Process Assisted
           Highly Constrained Expensive Optimization
    • Abstract: Publication date: Available online 4 September 2018Source: Information SciencesAuthor(s): Ruwang Jiao, Sanyou Zeng, Changhe Li, Yuhong Jiang, Yaochu JinAbstractExpected improvement (EI) is a popular infill criterion in Gaussian process assisted optimization of expensive problems for determining which candidate solution is to be assessed using the expensive evaluation method. An EI criterion for constrained expensive optimization (constrained EI) has also been suggested, which requires that feasible solutions exist in the candidate solutions. However, the constrained EI criterion will fail to work in case there are no feasible solutions. To address the above issue, this paper proposes a new EI criterion for highly constrained optimization that can work properly even when no feasible solution is available in the current population. The proposed constrained EI criterion can not only exploit local feasible regions, but also explore infeasible yet promising regions, making it a complete constrained EI criterion. The complete constrained EI is theoretically validated and empirically verified. Simulation results demonstrate that the proposed complete constrained EI is better than or comparable to five existing infill criteria.
  • L-concept lattices with positive and negative attributes: Modeling
           uncertainty and reduction of size
    • Abstract: Publication date: Available online 4 September 2018Source: Information SciencesAuthor(s): Eduard Bartl, Jan KonecnyAbstractIn our previous works, we introduced an extension of formal fuzzy concept analysis where attributes were considered as a positive and negative information based on user input. In the present paper, we show that the extension is naturally capable to model uncertainty and we describe a general method to increase that uncertainty in a parametric way. Furthermore, we demonstrate that two methods of concept lattice size reduction, which were thoroughly studied in formal fuzzy concept analysis, become instances of the general method when adapted to our extension.
  • Quaternion Lifting Scheme Applied to the Classification of Motion Data
    • Abstract: Publication date: Available online 4 September 2018Source: Information SciencesAuthor(s): Agnieszka Szczȩsna, Adam Świtoński, Janusz Słupik, Hafed Zghidi, Henryk Josiński, Konrad WojciechowskiAbstractIn this study, a new method of classification of skeleton-based motion data has been introduced. In the first stage, we performed multiscale feature extraction of rotational data. It is based on the proposed linear quaternion lifting scheme, with respect to the rotations coded by unit quaternions, which computes each scale based on the spherical linear interpolation (SLERP) prediction function and preserves the average signal value on each scale. Consequently, motion descriptors are extracted as quaternion attributes on different scales. The final recognition is performed by the nearest neighbor and minimum distance classifiers, adapted to support nonscalar features. Because of dimensionality of obtained descriptors, an attribute selection with respect to the multiresolution data has been proposed. It takes into consideration a specified number of resolutions, which is similar to low-pass filtering of the frequency domain. This method is utilized to solve the gait-based human identification problem. To validate such an application, a database containing data from 30 subjects was collected at the Human Motion Laboratory of the Polish-Japanese Academy of Information Technology (PJAIT). The obtained results were found to be satisfactory. In the best case, over 96% precision with only seven misclassified gaits of 178 samples was achieved.
  • NIS-Apriori-Based Rule Generation with Three-way Decisions and Its
           Application System in SQL
    • Abstract: Publication date: Available online 4 September 2018Source: Information SciencesAuthor(s): Hiroshi Sakai, Michinori Nakata, Junzo WatadaAbstractIn the study, non-deterministic information systems-Apriori-based (NIS-Apriori-based) rule generation from table data sets with incomplete information, SQL implementation, and the unique characteristics of the new framework are presented. Additionally, a few unsolved new research topics are proposed based on the framework. We follow the framework of NISs and propose certain rules and possible rules based on possible world semantics. Although each rule τ depends on a large number of possible tables, we prove that each rule τ is determined by examining only two τ-dependent possible tables. The NIS-Apriori algorithm is an adjusted Apriori algorithm that can handle such tables. Furthermore, it is logically sound and complete with regard to the rules. Subsequently, the implementation of the NIS-Apriori algorithm in SQL is described and a few new topics induced by effects of NIS-Apriori-based rule generation are confirmed. One of the topics that are considered is the possibility of estimating missing values via the obtained certain rules. The proposed methodology and the environment yielded by NIS-Apriori-based rule generation in SQL are useful for table data analysis with three-way decisions.
  • A Novel Multi-Objective Evolutionary Algorithm with Fuzzy Logic Based
           Adaptive Selection of Operators: FAME
    • Abstract: Publication date: Available online 4 September 2018Source: Information SciencesAuthor(s): Alejandro Santiago, Bernabé Dorronsoro, Antonio J. Nebro, Juan J. Durillo, Oscar Castillo, Héctor J. FraireAbstractWe propose a new method for multi-objective optimization, called Fuzzy Adaptive Multi-objective Evolutionary algorithm (FAME). It makes use of a smart operator controller that dynamically chooses the most promising variation operator to apply in the different stages of the search. This choice is guided by a fuzzy logic engine, according to the contributions of the different operators in the past. FAME also includes a novel effective density estimator with polynomial complexity, called Spatial Spread Deviation (SSD). Our proposal follows a steady-state selection scheme and includes an external archive implementing SSD to identify the candidate solutions to be removed when it becomes full. To assess the performance of our proposal, we compare FAME with a number of state of the art algorithms (MOEA/D-DE, SMEA, SMPSOhv, SMS-EMOA, and BORG) on a set of difficult problems. The results show that FAME achieves the best overall performance.
  • An Analysis of Hierarchical Text Classification Using Word Embeddings
    • Abstract: Publication date: Available online 4 September 2018Source: Information SciencesAuthor(s): Roger Alan Stein, Patrícia A. Jaques, João Francisco ValiatiAbstractEfficient distributed numerical word representation models (word embeddings) combined with modern machine learning algorithms have recently yielded considerable improvement on automatic document classification tasks. However, the effectiveness of such techniques has not been assessed for the hierarchical text classification (HTC) yet. This study investigates application of those models and algorithms on this specific problem by means of experimentation and analysis. We trained classification models with prominent machine learning algorithm implementations—fastText, XGBoost, SVM, and Keras’ CNN—and noticeable word embeddings generation methods—GloVe, word2vec, and fastText—with publicly available data and evaluated them with measures specifically appropriate for the hierarchical context. FastText achieved an lcaF1 of 0.893 on a single-labeled version of the RCV1 dataset. An analysis indicates that using word embeddings and its flavors is a very promising approach for HTC.
  • Trice-Valued Fuzzy Sets: Mathematical Model for Three-way Decisions
    • Abstract: Publication date: Available online 4 September 2018Source: Information SciencesAuthor(s): Kiyomitsu Horiuchi, Branimir Šešelja, Andreja TepavčevićAbstractUnder the idea to develop a mathematical model for three-way decisions, the aim of the paper is to study trice-valued fuzzy sets, i.e., mappings from a set to a structure called a trice. A trice is a triple semilattice satisfying roundabout absorption laws, suitable for representing multi-dimensional orders, which appear in complex movements in a plane or in a space. Our approach is cutworthy, namely, we investigate cuts of such fuzzy sets and prove theorems of decomposition and synthesis. This new notion provides a possibility to capture vague triangular situations. Therefore, a motivation for our research is to provide a new algebraic and order-theoretic model for three-way decisions, as this topic has been introduced recently for solving particular human problems and for information processing.
  • Hierarchical Residual Stochastic Networks for Time Series Recognition
    • Abstract: Publication date: Available online 1 September 2018Source: Information SciencesAuthor(s): Chunyu Xie, Ce Li, Baochang Zhang, Lili Pan, Qixiang Ye, Wei ChenAbstractDue to the complex spatio-temporal variations of data, time series recognition remains a challenging problem for the present deep networks. In this paper, we propose end-to-end hierarchical residual stochastic (HRS) networks to effectively and efficiently describe spatio-temporal variations. Specifically, we design stochastic kernelized filters based on a hierarchical framework with a new correlation residual (CorrRes) block to align the spatio-temporal features of a sequence. We further encode complex sequence patterns with a stochastic convolution residual (SConvRes) block, which employs the stochastic kernelized filters and a dropout strategy to reconfigure the convolution filters for large-scale computing in deep networks. Experiments on large-scale datasets, namely NTU RGB+D, SYSU-3D, UT-Kinect and Radar Behavior show that HRS networks significantly boost the performance of time series recognition and improve the state-of-the-art of skeleton, action, and radar behavior recognition performance.
  • Multi-Goal Motion Planning Using Traveling Salesman Problem in Belief
    • Abstract: Publication date: Available online 30 August 2018Source: Information SciencesAuthor(s): Ali Noormohammadi-Asl, Hamid D. TaghiradAbstractIn this paper, the multi-goal motion planning problem of an environment with some background information about its map is addressed in detail. The motion planning goal is to find a policy in belief space for the robot to traverse through a number of goal points. This problem is modeled as an asymmetric traveling salesman problem (TSP) in the belief space using partially observable Markov decision process (POMDP) framework. Then, feedback-based information roadmap (FIRM) algorithm is utilized to reduce the computational burden and complexity. By generating a TSP-FIRM graph, the search policy is obtained and an algorithm is proposed for online execution of the policy. Moreover, approaches to cope with challenges such as map updating, large deviations and high uncertainty in localization which are more troublesome in a real implementation are carefully addressed. Finally, in order to evaluate applicability and performance of the proposed algorithms, it is implemented in a simulation environment as well as on a physical robot in which some challenges such as kidnapping and discrepancies between real and computational models and map are examined.
  • Optimal directed hypergraph traversal with ant-colony optimisation
    • Abstract: Publication date: Available online 29 August 2018Source: Information SciencesAuthor(s): Marco ComuzziAbstractDirected hypergraphs are an extension of directed graphs in which edges connect a set of source nodes to a set of target nodes. Unlike graphs, they can capture complex relations in network structures that go beyond the union of pairwise associations. They are widely applied in a variety of different domains, such as finding pathways in chemical reaction networks or minimising propositional Horn formulas. Calculating optimal paths in hypergraphs in the general case is an NP-hard problem, which can be solved in polynomial time only when utility functions hold specific properties. We present in this paper an approach to search for optimal hypergraph paths in the general case based on ant colony optimisation. Ant colony optimisation is an evolutionary meta-heuristic that is particularly suitable to combinatorial problems, such as optimal graph traversal. We present an experimental evaluation using artificially-generated hypergraphs and discuss innovative applications of the proposed approach in the domains of industrial engineering and chemical informatics.
  • Towards Detecting Structural Branching and Cyclicity in Graphs: A
           Polynomial-based Approach
    • Abstract: Publication date: Available online 29 August 2018Source: Information SciencesAuthor(s): Matthias Dehmer, Zengqiang Chen, Frank Emmert-Streib, Abbe Mowshowitz, Yongtang Shi, Shailesh Tripathi, Yusen ZhangAbstractStructural properties of graphs and networks have been investigated across scientific disciplines ranging from mathematics to structural chemistry. Structural branching, cyclicity and, more generally, connectedness are well-known examples of such properties. In particular, various graph measures for detecting structural branching and cyclicity have been investigated. These measures are of limited applicability since their interpretation relies heavily on a certain definition of structural branching. In this paper we define a related measure, taking an approach to measurement similar to that of Lovász and Pelikán (On the eigenvalues of trees, Periodica Mathematica Hungarica, Vol. 3 (1-2), 1973, 175-182). We define a complex valued polynomial which also has a unique positive root. Analytical and numerical results demonstrate that this measure can be interpreted as a structural branching and cyclicity measure for graphs. Our results generalize the work of Lovász and Pelikán since the measure we introduce is not restricted to trees.
  • A novel three-way decision model under multiple-criteria environment
    • Abstract: Publication date: Available online 28 August 2018Source: Information SciencesAuthor(s): Fan Jia, Peide LiuAbstractThree-way decisions provide a new research perspective for solving decision-making problems. Under a multiple-criteria environment, this paper proposes a novel decision model based on the combination of three-way decisions and multi-criteria decision-making (MCDM). MCDM-based three-way decisions extend the range of applications of three-way decisions to MCDM problems and provide new calculation methods of loss functions and decision rules in view of the difference of alternatives. First, we define two concepts of relative loss functions and inverse loss functions to illustrate several properties of loss functions in three-way decisions, and we subsequently establish calculation methods of loss functions and thresholds with the aid of evaluation values of criteria. In consideration of all of the criteria in MCDM problems, we propose an aggregation method of loss functions to reflect the comprehensive losses of alternatives. By establishing the key steps and algorithm of the MCDM-based three-way decisions, three-way decision rules of all of the alternatives are obtained. Finally, an empirical example of project investment is utilized to validate the effectiveness and superiority of the proposed model.
  • HybridORAM: Practical oblivious cloud storage with constant bandwidth
    • Abstract: Publication date: Available online 8 February 2018Source: Information SciencesAuthor(s): Bo Li, Yanyu Huang, Zheli Liu, Jin Li, Zhihong Tian, Siu-Ming YiuAbstractAlong with the popularity of outsourcing data to the cloud server, data privacy becomes a central consideration. Because encryption alone has been proved insecure for the leakages of access pattern, Oblivious RAM (ORAM) was proposed to protect where, when and how often the data block has been accessed. However, different types of ORAM implementations have different limitations in terms of significant bandwidth cost or massive storage space, making them impractical for some applications like Internet of Things (IoT).In this paper, we present a practical ORAM, called HybridORAM, with constant bandwidth, which can be applied in wide application scopes. HybridORAM explores a new ORAM design to combine the advantages of layer and tree ORAMs; more specifically, it combines the frequently-accessed small levels of the former to improve the response time, and the small shuffle of the latter to save the storage capacity. Compared to the typical schemes, HybridORAM has an efficient response time reduced by O(log k), low bandwidth cost optimized from O(log N · B) to O(B) and small client storage, where k is level size factor, B is block size, N is the number of real blocks in ORAM. Experiments show that the response time of HybridORAM is 50.3% shorter than OnionORAM and 34.8% shorter than OS-PIR by practical parameters.
  • Efficient attribute-based encryption with attribute revocation for assured
           data deletion
    • Abstract: Publication date: Available online 8 February 2018Source: Information SciencesAuthor(s): Liang Xue, Yong Yu, Yannan Li, Man Ho Au, Xiaojiang Du, Bo YangAbstractCloud storage allows customers to store their data on remote cloud servers. With the advantage of reducing the burden of data management and storage, an increasing number of users prefer to store their data on the cloud. While secure data deletion is a crucial, it is a challenging issue in cloud storage. Logically deleted data may be easily exposed to un-authorized users in the cloud storage scenario thanks to its salient features such as multi-tenancy, virtualization and elasticity. Moreover, cloud servers might not delete customers’ data as instructed for hidden business interest. Hence, assured deletion is highly sought after. It helps preserve cloud users’ data privacy and is a necessary component of data retention regulations in cloud storage. In this paper, we first investigate the goals of assured data deletion and formalize its security model.Then, we propose a key-policy attribute-based encryption scheme for assured deletion (AD-KP-ABE) of cloud data. Our construction makes use of the attribute revocation cryptographic primitive and Merkle Hash Tree to achieve fine-grained access control and verifiable data deletion. The proposed AD-KP-ABE enjoys desirable properties such as no secret key update, partial ciphertext update and assured data deletion. The detailed security proof and implementation results demonstrate the security and practicality of our proposal.
  • Privacy-preserving smart IoT-based healthcare big data storage and
           self-adaptive access control system
    • Abstract: Publication date: Available online 6 February 2018Source: Information SciencesAuthor(s): Yang Yang, Xianghan Zheng, Wenzhong Guo, Ximeng Liu, Victor ChangAbstractIn this paper, a privacy-preserving smart IoT-based healthcare big data storage system with self-adaptive access control is proposed. The aim is to ensure the security of patients’ healthcare data, realize access control for normal and emergency scenarios, and support smart deduplication to save the storage space in big data storage system. The medical files generated by the healthcare IoT network are encrypted and transferred to the storage system, which can be securely shared among the healthcare staff from different medical domains leveraging a cross-domain access control policy. The traditional access control technology allows the authorized data users to decrypt patient’s sensitive medical data, but also hampers the first-aid treatment when the patient’s life is threatened because the on-site first-aid personnel are not permitted to get patient’s historical medical data. To deal with this dilemma, we propose a secure system to devise a novel two-fold access control mechanism, which is self-adaptive for both normal and emergency situations. In normal application, the healthcare staff with proper attribute secret keys can have the data access privilege; in emergency application, patient’s historical medical data can be recovered using a password-based break-glass access mechanism. To save the storage overhead in the big data storage system, a secure deduplication method is designed to eliminate the duplicate medical files with identical data, which may be encrypted with different access policies. A highlight of this smart secure deduplication method is that the remaining medical file after the deduplication can be accessed by all the data users authorized by the different original access policies. This smart healthcare big data storage system is formally proved secure, and extensive comparison and simulations demonstrate its efficiency.
  • A hand-based biometric system in visible light for mobile environments
    • Abstract: Publication date: Available online 9 January 2018Source: Information SciencesAuthor(s): Silvio Barra, Maria De Marsico, Michele Nappi, Fabio Narducci, Daniel RiccioAbstractThe analysis of the shape and geometry of the human hand has long represented an attractive field of research to address the needs of digital image forensics. Over recent years, it has also turned out to be effective in biometrics, where several innovative research lines are pursued. Given the widespread diffusion of mobile and portable devices, the possibility of checking the owner identity and controlling the access to the device by the hand image looks particularly attractive and less intrusive than other biometric traits. This encourages new research to tacklethe present limitations. The proposed work implements the complete architecture of a mobile hand recognition system, which uses the camera of the mobile device for the acquisition of the hand in visible light spectrum. The segmentation of the hand starts from the detection of the convexities and concavities defined by the fingers, and allows extracting 57 different features from the hand shape. The main contributions of the paper develop along two directions. First, dimensionality reduction methods are investigated, in order to identify subsets of features including only the most discriminating and robust ones. Second, different matching strategies are compared. The proposed method is tested over a dataset of hands from 100 subjects. The best obtained Equal Error Rate is 0.52%. Results demonstrate that discarding features that are more prone to distortions allows lighter processing, but also produces better performance than using the full set of features. This confirms the feasibility of such an approach on mobile devices, and further suggests to adopt it even in more traditional settings.
  • Decision Aiding Model with Entropy-Based Subjective Utility
    • Abstract: Publication date: Available online 27 August 2018Source: Information SciencesAuthor(s): Manish AggarwalAbstractAn entropy-based method is presented to model a decision-maker’s (DM’s) subjective utility for a criterion value. The proposed method considers distribution of all the values that the criterion takes for the given set of alternatives. Based on the utility so modeled, and and the DM’s attitudinal character, a multi criteria decision aiding (MCDA) approach is developed to find the best alternative. The proposed method and the approach are applied in a real car selection case-study.
  • An efficient method for clustered multi-metric learning
    • Abstract: Publication date: Available online 27 August 2018Source: Information SciencesAuthor(s): Bac Nguyen, Francesc J. Ferri, Carlos Morell, Bernard De BaetsAbstractDistance metric learning, which aims at finding a distance metric that separates examples of one class from examples of the other classes, is the key to the success of many machine learning tasks. Although there has been an increasing interest in this field, learning a global distance metric is insufficient to obtain satisfactory results when dealing with heterogeneously distributed data. A simple solution to tackle this kind of data is based on kernel embedding methods. However, it quickly becomes computationally intractable as the number of examples increases. In this paper, we propose an efficient method that learns multiple local distance metrics instead of a single global one. More specifically, the training examples are divided into several disjoint clusters, in each of which a distance metric is trained to separate the data locally. Additionally, a global regularization is introduced to preserve some common properties of different clusters in the learned metric space. By learning multiple distance metrics jointly within a single unified optimization framework, our method consistently outperforms single distance metric learning methods, while being more efficient than other state-of-the-art multi-metric learning methods.
  • A new coupled disease-awareness spreading model with mass media on
           multiplex networks
    • Abstract: Publication date: Available online 27 August 2018Source: Information SciencesAuthor(s): Chengyi Xia, Zhishuang Wang, Chunyuan Zheng, Quantong Guo, Yongtang Shi, Matthias Dehmer, Zengqiang ChenAbstractHow to understand the propagation properties of infectious diseases among the population is an important topic in the field of epidemic modelling. In this paper, we investigate the interaction between the disease transmission and disease-related awareness spread, and then propose a new coupled disease spreading model on a two-layered multiplex network, where one layer denotes the underlying topology for the epidemics and the other one represents the corresponding topology for the awareness spread. In the current model, once the information about the disease is acquired by some susceptible ones, it is assumed that they will take the preventive measures to reduce the probability of being infected (e.g., social distancing or taking the vaccine). In the epidemic layer, the transmission dynamics among nodes can be characterized with the classic SIR (susceptible-infective-recovered) model, and those individuals who obtain the disease information will cut down their rate of being infected with a multiplicative factor γ during the course of disease transmission; while in the awareness layer, we will employ the UAU (unaware-aware-unaware) model to denote the diffusion of information. Meanwhile, we explore the impact of mass media on the spread of infectious diseases in the model. Based on the microscopic Markovian chain approach, we build the probability tree to describe the switching process among different states and then establish the different equations for the state transition in detail. Furthermore, we analytically derive the epidemic threshold regarding the disease propagation, which is correlated with the multiplex network topology and the coupling relationship between two classes of spreading dynamics, and extensive simulations also validate the theoretical predictions. Current results are beneficial for us to deeply understand the contagion characteristics of real epidemics within the population.
  • Optimal Boolean lattice-based algorithms for the U-curve optimization
    • Abstract: Publication date: Available online 27 August 2018Source: Information SciencesAuthor(s): Marcelo S. Reis, Gustavo Estrela, Carlos Eduardo Ferreira, Junior BarreraAbstractThe U-curve optimization problem is characterized by a decomposable in U-shaped curves cost function over the chains of a Boolean lattice. This problem can be applied to model the classical feature selection problem in Machine Learning. In this paper, we point out that the firstly proposed algorithm to tackle the U-curve problem, the RBM algorithm, is in fact suboptimal. We also present two new algorithms: UCS, which is actually optimal to tackle this problem; and UCSR, a variation of UCS that solves a special case of the U-curve problem and relies on a reduced, ordered binary decision diagram to control the search space. We provide results of two computational assays with these new algorithms: first, W-operator design for filtering of binary images; second, linear SVM design for classification of data sets from the UCI Machine Learning Repository. We show that, in these assays, UCS and UCSR outperformed an exhaustive search and also three widely used heuristics: the SFFS sequential selection, the BFS graph-based search, and the CHCGA genetic algorithm. Finally, we analyze the obtained results and point out improvements that might enhance the performance of these two novel algorithms.
  • Inclusion measure-based multi-granulation decision-theoretic rough sets in
           multi-scale intuitionistic fuzzy information tables
    • Abstract: Publication date: Available online 27 August 2018Source: Information SciencesAuthor(s): Bing Huang, Wei-Zhi Wu, Jinjiang Yan, Huaxiong Li, Xianzhong ZhouAbstractMulti-granulation rough sets (MGRSs) and decision-theoretic rough sets (DTRSs) are important and popular extended types of Pawlak’s classical rough set model. Multi-granulation DTRSs (MG-DTRSs), which combine these two generalized rough set models, have been investigated in depth in recent years to handle noisy distributed data. However, this combination cannot be used to acquire knowledge from multi-scale information systems, in which an object may take on different values under the same attribute depending on the scale used to measure it. Two novel types of MG-DTRSs in multi-scale intuitionistic fuzzy (IF) information tables have been developed on the basis of IF inclusion measures in this study to solve this problem. First, we introduce a type of inclusion measure between two IF sets and present the concept of inclusion measure-based DTRSs in multi-scale IF information tables. Second, we present the inclusion measure-based optimistic and pessimistic MG-DTRSs in multi-scale IF information tables, examine their properties, and analyze the three-way decision method based on the presented models. Third, we define the optimal scale selection and present the two optimal scale selection algorithms based on MG-DTRSs in multi-scale IF information tables. Fourth, we provide the reducts of the optimal scales based on MG-DTRSs in multi-scale IF information tables, examine the discernibility function reduction method, and devise two algorithms for computing an optimal approximation scale reduct. Finally, we discuss several possible generalizations related to MG-DTRSs in multi-scale IF information tables. This study provides an MG-DTRS method for acquiring knowledge from multi-scale IF information tables.
  • FAS: Forward Secure Sequential Aggregate Signatures for Secure Logging
    • Abstract: Publication date: Available online 25 August 2018Source: Information SciencesAuthor(s): Jihye Kim, Hyunok OhAbstractAudit logs are an elemental part of computer systems due to their forensic value. Safeguarding audit logs on a physically unprotected machine are a challenging work, especially in the presence of active adversaries. Forward security is necessary for such a logging system. In this paper, we propose a new Forward secure sequential Aggregate Signature (FAS) scheme with optimal storage and communication for keys and signatures. To the best of our knowledge, our FAS scheme is the only scheme that has constant-sized public and secret keys as well as constant-sized aggregate signatures. Our proposed scheme supports aggregation of consecutive signatures by a third party. We prove the security of our proposed scheme under the hardness of factoring assumption, in the random oracle model.
  • Towards Faster Local Search for Minimum Weight Vertex Cover on Massive
    • Abstract: Publication date: Available online 25 August 2018Source: Information SciencesAuthor(s): Shaowei Cai, Yuanjie Li, Wenying Hou, Haoran WangAbstractThe minimum weight vertex cover (MWVC) problem is a well known NP-hard problem with various real-world applications. In this paper, we design an efficient algorithm named FastWVC to solve MWVC problem in massive graphs. To this end, we propose a construction procedure, which aims to generate a quality initial vertex cover in short time. We also propose a new exchange step for reconstructing a vertex cover. Additionally, a cost-effective strategy is used for choosing adding vertices , which can accelerate the algorithm. Experiments on 102 instances were conducted to confirm the effectiveness of our algorithm. The results show that the FastWVC algorithm outperforms other algorithms in terms of both solution quality and computational time in most of the instances. We also carry out experiments that analyze the effectiveness of the underlying ideas.
  • Modeling and clustering attacker activities in IoT through machine
           learning techniques
    • Abstract: Publication date: Available online 23 April 2018Source: Information SciencesAuthor(s): Peiyuan Sun, Jianxin Li, Md Zakirul Alam Bhuiyan, Lihong Wang, Bo LiAbstractWith the rise of the Internet of Things, malicious attacks pose serious threats to the massive vulnerable IoT devices. Recently, attackers have initiated increasingly coordinated attack activities commonly pertaining to botnets. However, how to effectively detect the botnet based on attacker activities is proven challenging. In this paper, we propose a Machine Learning-based method for modeling attacker activities based on the following intuitive observations: attackers in the same botnet tend to launch temporally close attacks. We then directly model attack temporal patterns using a special class of point process called Multivariate Hawkes Process. Intuitively, Multivariate Hawkes Process identifies the latent influences between attackers by utilizing the mutually exciting properties. We then cluster the attacker activities based on the inferred weighted influence matrix with resort to the graph-based clustering approach. To evaluate the applicability of our method, we deployed 10 honeypots in a real-world environment, and conduct experiments on the collected dataset. The results show that we can identify the activity pattern and the structure of botnets effectively.
  • Protecting internet infrastructure against link flooding attacks: A
           techno-economic perspective
    • Abstract: Publication date: Available online 19 April 2018Source: Information SciencesAuthor(s): Xiaobo Ma, Jianfeng Li, Yajuan Tang, Bo An, Xiaohong GuanAbstractAs an emerging threat, link flooding attacks (LFAs) target and congest core links that constitute Internet routing infrastructure, hence posing a growing threat to networks worldwide. Mitigating and defeating LFAs is particularly challenging for two reasons. First, arising from the end-to-end communication from bots to public servers (e.g., Web servers), the attack traffic flows could be indistinguishable from legitimate ones, and even unobservable to the victim network surrounded by the target links. Therefore, typical flow-filtering countermeasures deployed at the network perimeter become invalid when handling LFAs. Second, the target link and the victim network belong to an autonomous system (AS) different from the source ASs where the attack traffic flows originate. These source ASs, however, have no idea the target link is under attack, whereas they are in charge of routing decisions and thus capable of mitigating LFAs by rerouting the attack traffic flows to bypass the target link. Therefore, inter-AS cooperation is indispensable to defeat LFAs. Unfortunately, the source ASs lack incentives to cooperate because the collateral damage of LFAs to them may be negligible, making it challenging to eradicate LFAs. In this paper, we make the first effort to cope with LFAs from a techno-economic perspective, for accelerating ISPs’ cooperation in defending against LFAs. We propose two novel mechanisms to mitigate LFAs by stimulating the inter-AS cooperation via incentive design and Nash bargaining. Experiments using Internet AS relationship data demonstrate the feasibility and effectiveness of our mechanisms.
  • A multi-factor monitoring fault tolerance model based on a GPU cluster for
           big data processing
    • Abstract: Publication date: Available online 18 April 2018Source: Information SciencesAuthor(s): Yuling Fang, Qingkui Chen, Naixue XiongAbstractHigh-performance computing clusters are widely used in large-scale data mining applications, and have higher requirements for persistence, stability and real-time use and sre therefore computationally intensive. To support large-scale data processing, we design a multi-factor real-time monitoring fault tolerance (MRMFT) model based on a GPU cluster. However, the higher clock frequency of GPU chips results in excessively high energy consumption in computing systems. Moreover, the ability to support a long-lasting high temperature operation varies greatly between different GPUs owing to the individual differences between the chips. In this paper, we design a GPU cluster energy consumption monitoring system based on wireless sensor networks (WSNs) and propose an energy consumption aware checkpointing (ECAC) for high energy consumption problems with the following two advantages: the system sets checkpoints according to actual energy consumption and the device temperature to improve the utilization of checkpoints and reduce time cost; and it exploits the parallel computing features of CPU and GPU to hide the CPU detection overhead in GPU parallel computation, and further reduce the time and energy consumption overhead in the fault tolerance phase. Using ECAC as the constraint and aiming for a persistent and reliable operation, the dynamic task migration mechanism is designed, and the reliability of the cluster is greatly improved. The theoretical analysis and experiment results show that the model improves the persistence and stability of the computing system while reducing checkpoint overhead.
  • Adapting topic map and social influence to the personalized hybrid
           recommender system
    • Abstract: Publication date: Available online 13 April 2018Source: Information SciencesAuthor(s): Hei-Chia Wang, Hsu-Tung Jhou, Yu-Shan TsaiAbstractA recommender system utilizes information filtering techniques to help users obtain accurate information effectively and efficiently. The existing recommender systems, however, recommend items based on the overall ratings or the click-through rate, and emotions expressed by users are neglected. Conversely, the cold-start problem and low model scalability are the two main problems with recommender systems. The cold-start problem is encountered when the system lacks initial rating. Low model scalability indicates that a model is incapable of coping with high-dimensional data. These two problems may mislead the recommender system, and thus, users will not be satisfied with the recommended items. A hybrid recommender system is proposed to mitigate the negative effects caused by these problems. Additionally, ontologies are applied to integrate the extracted features into topics to reduce dimensionality. Topics mentioned in the items are displayed in the form of a topic map, and users can refer to these similar items for further information.
  • Frequent pattern discovery with tri-partition alphabets
    • Abstract: Publication date: Available online 4 April 2018Source: Information SciencesAuthor(s): Fan Min, Zhi-Heng Zhang, Wen-Jie Zhai, Rong-Ping ShenAbstractThe concept of patterns is the basis of sequence analysis. There are various pattern definitions for biological data, texts, and time series. Inspired by the methodology of three-way decisions and protein tri-partition, this paper proposes a frequent pattern discovery algorithm for a new type of pattern by dividing the alphabet into strong, medium, and weak parts. The new type, called a tri-pattern, is more general and flexible than existing ones and is therefore more interesting in applications. Experiments were undertaken on data in various fields to reveal the universality of this new pattern. These include protein sequence mining, petroleum production time series analysis, and forged Chinese text keyword mining. The results show that tri-patterns are more meaningful and desirable than the existing four types of patterns. This study enriches the semantics of sequential pattern discovery and the application fields of three-way decisions.
  • Adaptive fuzzy asymptotical tracking control of nonlinear systems with
           unmodeled dynamics and quantized actuator
    • Abstract: Publication date: Available online 3 April 2018Source: Information SciencesAuthor(s): Huanqing Wang, Peter Xiaoping Liu, Xuejun Xie, Xiaoping Liu, Tasawar Hayat, Fuad E. AlsaadiAbstractThis paper studies the problem of adaptive fuzzy asymptotical quantized tracking control of non-strict-feedback systems with unmodeled dynamics. A dynamic signal is used to cope with the unmodeled dynamics and fuzzy systems are introduced to approximate the packaged unknown nonlinearities. Based on backstepping technique and fuzzy approximation property, a systemic fuzzy adaptive control scheme is proposed. By the utilization of Lyapunov theory, the semi-globally uniformly ultimate boundedness of all closed-loop system signals and asymptotical tracking performance are guaranteed. The main contributions of this work are two aspects: (i) a backstepping-based quantized control algorithm is firstly extended to nonlinear systems with unmodeled dynamics and non-strict-feedback structure; (ii) the semi-globally asymptotic tracking control scheme is independent of the quantized parameter. Simulation results verify the presented control approach.
  • Game theoretic security of quantum bit commitment
    • Abstract: Publication date: Available online 20 March 2018Source: Information SciencesAuthor(s): Lu Zhou, Xin Sun, Chunhua Su, Zhe Liu, Kim-Kwang Raymond ChooAbstractDue to the threat posed by quantum computing, there has been an increasingly focus on designing secure and efficient quantum-based and post-quantum cryptographic protocols. In this paper, we study and propose a quantum bit commitment (QBC) protocol inspired by the framework of categorical quantum mechanics. We show that our protocol is more secure and simpler than most existing cheat-sensitive QBC protocols. Then, we introduce the notion of game theoretic security, and demonstrate that such a notion is less demanding than unconditional security, yet stricter than cheat-sensitive. We show that our protocol is game theoretic secure for many commitment games. Being game theoretic secure opens the door of applying our protocol to game theory. Specifically, we show that our protocol can be used to implement equilibrium in commitment games. Finally, we run experiments on the IBM quantum computer to demonstrate the practicability of our protocol.
  • Performance and energy optimisation in CPUs through fuzzy knowledge
    • Abstract: Publication date: Available online 12 March 2018Source: Information SciencesAuthor(s): Arpad Gellert, Adrian Florea, Ugo Fiore, Paolo Zanetti, Lucian VintanAbstractThis paper presents an automatic design space exploration using processor design knowledge for the multi-objective optimisation of a superscalar microarchitecture enhanced with selective load value prediction (SLVP). We introduced new important SLVP parameters and determined their influence regarding performance, energy consumption, and thermal dissipation. We significantly enlarged initial processor design knowledge expressed through fuzzy rules and we analysed its role in the process of automatic design space exploration. The proposed fuzzy rules improve the diversity and quality of solutions, and the convergence speed of the design space exploration process. Experiments show that a set-associative prediction table is more effective than a direct mapped table and that 86% of the configurations in the Pareto front use multiple values per load. In conclusion, our experiments show that integrating an SLVP module into a superscalar microarchitecture is hardware feasible; in comparison with the case without SLVP, performance is better, energy consumption is lower, and the temperatures inside the chip decreases, remaining below 75 °C.
  • On efficient computation of inter-simplex Chebyshev distance for
           voxelization of 2-manifold surface
    • Abstract: Publication date: Available online 7 March 2018Source: Information SciencesAuthor(s): Piyush Kanti Bhunre, Partha Bhowmick, Jayanta MukherjeeAbstractDetermining an appropriate voxel size is the foremost step for topology-aware voxelization of a 2-manifold surface. In this paper, we show how this is related to computation of inter-simplex Chebyshev distance (ICD) and how this computation is efficiently doable using a hierarchical decomposition of ICD function for higher-order simplices to that of lower-order ones. A novel idea of partitioning the manifold surface based on functional components is used alongside to reduce the number of distance computations. Geometric data structures and efficient query algorithms are also used to speed up the computation further. Detailed experimental results have been furnished to demonstrate its merit and aptness.
  • An active three-way clustering method via low-rank matrices for multi-view
    • Abstract: Publication date: Available online 5 March 2018Source: Information SciencesAuthor(s): Hong Yu, Xincheng Wang, Guoyin Wang, Xianhua ZengAbstractIn recent years, multi-view clustering algorithms have shown promising performance by combining multiple sources or views of datasets. A problem that has not been addressed satisfactorily is the uncertain relationship between an object and a cluster. Thus, this paper investigates an active three-way clustering method via low-rank matrices that can improve clustering accuracy as clustering proceeds for the multi-view data of high dimensionality. We adopt a three-way clustering representation to reflect the three types of relationships between an object and a cluster, namely, belong-to definitely, uncertain and not belong-to definitely. We construct the consensus low-rank matrix from each weighted low-rank matrix by taking account of the diversity of views, and give the method to solve the optimization problem of objective function based on the improved augmented Lagrangian multiplier algorithm. We suggest an active learning strategy to learn important informative pairwise constraints after measuring the uncertainty of an object based on the entropy concept. The experimental results conducted on real-world datasets have validated the effectiveness of the proposed method.
  • A novel CNN based security guaranteed image watermarking generation
           scenario for smart city applications
    • Abstract: Publication date: Available online 27 February 2018Source: Information SciencesAuthor(s): Daming Li, Lianbing Deng, Brij Bhooshan Gupta, Haoxiang Wang, Chang ChoiAbstractThe rise of machine learning increases the current computing capabilities and paves the way to novel disruptive applications. In the current era of big data, the application of image retrieval technology for large-scale data is a popular research area. To ensure the robustness and security of digital image watermarking, we propose a novel algorithm using synergetic neural networks. The algorithm first processes a meaningful gray watermark image, then embeds it as a watermark signal into the block Discrete Cosine Transform (DCT) component. The companion algorithm for detection and extraction of the watermark uses a cooperative neural network, where the suspected watermark signal is used as the input while the output consists in the result of the recognition process. The simulation experiments show that the algorithm can complete certain image processing operations with improved performance, not only simultaneously completing watermark detection and extraction, but also efficiently determining the watermark attribution. Compared with other state-of-the-art models, the proposed model obtains an optimal Peak Signal-to-noise ratio (PSNR).
  • Repairing 3D binary images using the BCC grid with a 4-valued
           combinatorial coordinate system
    • Abstract: Publication date: Available online 24 February 2018Source: Information SciencesAuthor(s): Lidija Čomić, Paola MagilloAbstractA 3D binary image I is called well-composed if the set of points in the topological boundary of the cubes in I is a 2-manifold. Repairing a 3D binary image is a process which produces a well composed image (or a polyhedral complex) from the non-well-composed image I.We propose here to repair 3D images by associating the Body-Centered Cubic grid (BCC grid) to the cubical grid. The obtained polyhedral complex is well composed, since two voxels in the BCC grid either share an entire face or are disjoint. We show that the obtained complex is homotopy equivalent to the cubical complex naturally associated with the image I.To efficiently encode and manipulate the BCC grid, we present an integer 4-valued combinatorial coordinate system that addresses cells of all dimensions (voxels, faces, edges and vertices), and allows capturing all the topological incidence and adjacency relations between cells by using only integer operations.We illustrate an application of this coordinate system on two tasks related with the repaired image: boundary reconstruction and computation of the Euler characteristic.
  • Secure weighted possibilistic c-means algorithm on cloud for clustering
           big data
    • Abstract: Publication date: Available online 10 February 2018Source: Information SciencesAuthor(s): Qingchen Zhang, Laurence T. Yang, Arcangelo Castiglione, Zhikui Chen, Peng LiAbstractThe weighted possibilistic c-means algorithm is an important soft clustering technique for big data analytics with cloud computing. However, the private data will be disclosed when the raw data is directly uploaded to cloud for efficient clustering. In this paper, a secure weighted possibilistic c-means algorithm based on the BGV encryption scheme is proposed for big data clustering on cloud. Specially, BGV is used to encrypt the raw data for the privacy preservation on cloud. Furthermore, the Taylor theorem is used to approximate the functions for calculating the weight value of each object and updating the membership matrix and the cluster centers as the polynomial functions which only include addition and multiplication operations such that the weighed possibilistic c-means algorithm can be securely and correctly performed on the encrypted data in cloud. Finally, the presented scheme is estimated on two big datasets, i.e., eGSAD and sWSN, by comparing with the traditional weighted possibilistic c-means method in terms of effectiveness, efficiency and scalability. The results show that the presented scheme performs more efficiently than the traditional weighted possiblistic c-means algorithm and it achieves a good scalability on cloud for big data clustering.
  • Could emerging fraudulent energy consumption attacks make the cloud
           infrastructure costs unsustainable'
    • Abstract: Publication date: Available online 27 June 2018Source: Information SciencesAuthor(s): Massimo FiccoAbstractCloud paradigm is vulnerable to emerging breeds of fraudulent energy-related threats, which seek to exploit the cloud elasticity and the multi-tenant model. Recently, several sophisticated attacks have been reported by the cloud customers, which induced sustained and prolonged fraudulent resource consumptions, making the cloud costs unsustainable. If properly orchestrated, such attacks can also significantly affect the cloud service providers, forcing a frequent scaling and migration of virtual machines in the cloud. Such attacks aim at exploiting the elasticity and multi-tenacity of the cloud paradigm, in order to compromise the long-term financial viability of operating in the cloud, and thus, inflicting significant energy cost and loss of reputation to the cloud provider. This paper discusses the vulnerabilities associated to such a new breed of attacks, paying special emphasis to the risks for the cloud service providers. Practical experiments and simulations have been used to demonstrate the vulnerability of the cloud resource manager against emerging energy-related threats, named Fraudulent Energy Consumption attacks. Finally, some countermeasures are also discussed.
  • An optimized unsupervised manifold learning algorithm for manycore
    • Abstract: Publication date: Available online 21 June 2018Source: Information SciencesAuthor(s): Alexandro Baldassin, Ying Weng, Daniel Carlos Guimarães Pedronette, Jurandy AlmeidaAbstractMultimedia data, such as images and videos, has become very popular in people’s daily life as a result of the widespread use of mobile devices. The ever-increasing amount of such data, along with the necessity for real-time retrieval, has lead to the development of new methods that can process them in a timely fashion with acceptable accuracy. In this paper, we study the performance of ReckNN, an unsupervised manifold learning algorithm based on the reciprocal neighbourhood and the authority of ranked lists. Most of the related work in this field do not fully investigate optimization strategies, an aspect that is becoming more important with the high availability of manycore machines. In order to address that issue, we fully investigate optimization opportunities in this article and make the following three main contributions. Firstly, we develop an efficient and scalable method for storing and accessing the distances between objects (e.g., video or image) based on dictionaries. Secondly, we employ memoization to speed up the computation of authority scores, leading to a significant performance gain even on single-core architectures. Lastly, we devise and implement several parallelization strategies and show that they are scalable on a 72-core Intel machine. The experimental results with MPEG-7, Corel5k and MediaEval benchmarks show that the optimized ReckNN delivers both efficiency and scalability, highlighting the importance of the proposed optimizations for manycore machines.
  • A periodicity-based parallel time series prediction algorithm in cloud
           computing environments
    • Abstract: Publication date: Available online 19 June 2018Source: Information SciencesAuthor(s): Jianguo Chen, Kenli Li, Huigui Rong, Kashif Bilal, Keqin Li, Philip S. YuAbstractIn the era of big data, practical applications in various domains continually generate large-scale time-series data. Among them, some data show significant or potential periodicity characteristics, such as meteorological and financial data. It is critical to efficiently identify the potential periodic patterns from massive time-series data and provide accurate predictions. In this paper, a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for large-scale time-series data is proposed and implemented in the Apache Spark cloud computing environment. To effectively handle the massive historical datasets, a Time Series Data Compression and Abstraction (TSDCA) algorithm is presented, which can reduce the data scale as well as accurately extracting the characteristics. Based on this, we propose a multi-layer time series periodic pattern recognition (MTSPPR) algorithm using the Fourier Spectrum Analysis (FSA) method. In addition, a Periodicity-based Time Series Prediction (PTSP) algorithm is proposed. Data in the subsequent period are predicted based on all previous period models, in which a time attenuation factor is introduced to control the impact of different periods on the prediction results. Moreover, to improve the performance of the proposed algorithms, we propose a parallel solution on the Apache Spark platform, using the Streaming real-time computing module. To efficiently process the large-scale time-series datasets in distributed computing environments, Distributed Streams (DStreams) and Resilient Distributed Datasets (RDDs) are used to store and calculate these datasets. Logical and data dependencies of RDDs in the P-TSDCA, P-MTSPPR, and P-PTSP processes are considered, and the corresponding parallel execution solutions are conducted. Extensive experimental results show that our PPTSP algorithm has significant advantages compared with other algorithms in terms of prediction accuracy and performance.
  • Determination of 3D curve skeleton of a digital object
    • Abstract: Publication date: Available online 15 June 2018Source: Information SciencesAuthor(s): Nilanjana Karmakar, Sharmistha Mondal, Arindam BiswasAbstractThe 3D curve skeleton of a digital object, constructed as a connected set of voxels spanning its interior, captures an approximate but truthful representation of the object shape. An efficient and robust technique for extraction of the 3D curve skeleton of a digital object is presented in this paper. The 3D isothetic inner cover of the object, obtained in a preprocessing step, is utilized to design a combinatorial algorithm in the orthogonal domain that also provides further results in the general domain. The voxels representing the surface of the inner cover are represented in a topological space. They are completely enclosed by the inner cover that satisfies certain conditions expressed in another topological space. The homotopy equivalence of topological spaces is used to report the 3D curve skeleton. Also, the homology groups of the topological spaces are proved to be isomorphic. The resulting skeleton is connected, single voxel thick, and centered with respect to the object volume. It further preserves the topology of the inner cover and may be modified to preserve the object topology in most of the cases. The accuracy of shape representation by the skeleton can be controlled based on the requirement. Experimental results on a wide range of objects demonstrate the efficacy and robustness of the method.
  • A novel energy-efficient neighbor discovery procedure in a wireless
           self-organization network
    • Abstract: Publication date: Available online 15 June 2018Source: Information SciencesAuthor(s): Yupeng Wang, Zelong Yu, Jun Huang, Chang ChoiAbstractNeighbor discovery plays a major role in the wireless self-organizing network, which is the cornerstone for the autonomous coverage extension and performance enhancement such as cooperative transmission and multi-hop transmission. However, the hidden node problem is frequently encountered in neighbor discovery procedure based on the simple flooding due to the uncoordinated transmission, which gives rise to unnecessary energy waste. In this paper, the hidden node problem in the conventional neighbor discovery procedure is analyzed. A novel neighbor discovery procedure with the ability of node distance estimation and power allocation for the packet retransmission after collision is proposed, which utilizes fuzzy location information to properly configure the transmission power of the collided packets for better reception. The proposed algorithm improves the power efficiency of the neighbor discovery procedure by reducing the number of transmissions required for the successful reception with negligible induced complexity. Through the Monte-Carlo simulation, the proposed procedure with fuzzy location information and the power allocation method outperforms the conventional procedure in different node population scenarios, and the proposed procedure is insensitive to the distance estimation error, displaying strong robustness.
  • Intrusion detection model of wireless sensor networks based on game theory
           and an autoregressive model
    • Abstract: Publication date: Available online 15 June 2018Source: Information SciencesAuthor(s): Lansheng Han, Man Zhou, Wenjing Jia, Zakaria Dalil, Xingbo XuAbstractAn effective security strategy for Wireless Sensor Networks (WSNs) is imperative to counteract security threats. Meanwhile, energy consumption directly affects the network lifetime of a wireless sensor. Thus, an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Existing Intrusion Detection Systems can only detect specific attacks and their network lifetime is short due to their high energy consumption. For the purpose of reducing energy consumption and ensuring high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model into a non-cooperative, complete-information, static game model, but also predicts attack pattern reliably. The proposed approach improves on previous approaches in two main ways: (1) it takes energy consumption of the intrusion detection process into account, and (2) it obtains the optimal defense strategy that balances the system’s detection efficiency and energy consumption by analyzing the model’s mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that our proposed IDS not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption.
  • CSP-E2: An abuse-free contract signing protocol with low-storage TTP for
           energy-efficient electronic transaction ecosystems
    • Abstract: Publication date: Available online 15 June 2018Source: Information SciencesAuthor(s): Guangquan Xu, Yao Zhang, Arun Kumar Sangaiah, Xiaohong Li, Aniello Castiglione, Xi ZhengAbstractIn an increasingly connected world, energy efficiency has become a significant problem. Online electronic transactions are booming based on shopping convenience, fast payments, and lower prices. Electronic trading systems are one of the main sources of energy consumption because the so-called contract signing protocol (CSP) is a crucial part of e-commerce. Therefore, the energy consumption is largely determined by the efficiency of the CSP. To this end, an abuse-free CSP with low-storage TTP is proposed to eliminate the enormous energy-consumption problem originating from the unfit protocol design. This protocol exploits a proxy key scheme and introduces a lightweight TTP storage policy that greatly reduces the TTP burden by integrating symmetric encryption aiming at constructing an energy-efficient electronic transaction ecosystem. Theoretical analysis and comparative analysis show that the storage requirement of TTP is reduced by more than 50% while ensuring fairness, optimism and abuse-freeness in our protocol. Moreover, the exchange steps of our protocol are reduced so that the protocol efficiency is improved.
  • Smart collaborative distribution for privacy enhancement in moving target
    • Abstract: Publication date: Available online 6 June 2018Source: Information SciencesAuthor(s): Fei Song, Yu-Tong Zhou, Yu Wang, Tian-Ming Zhao, Ilsun You, Hong-Ke ZhangAbstractThe Moving Target Defense (MTD) has been widely discussed in many communities to upgrade the network reliability, survivability, dependability, etc. However, utilizing MTD in privacy protection still needs more investigations. In this paper, we propose a smart collaborative distribution scheme to enhance the privacy based on MTD guidelines. A target application scenario is the Domain Name System (DNS) that is experiencing serious and complex privacy issues. The preliminary and potential risks are firstly analyzed based on DNS attack approaches, DNS server locations and the vulnerability of user privacy. Then, the details of our scheme are illustrated through port number assignment patterns, main procedures of dynamic port hopping and the implementation method. To quantitatively evaluate the performance, an analytical model was established from theoretical perspectives. The relationships between multiple parameters and overall system capacity are explored as well. The validation results demonstrate that the smart collaborative distribution is able to improve the privacy without affecting the basic DNS functionality.
  • Weakly well-composed cell complexes over nD pictures
    • Abstract: Publication date: Available online 5 June 2018Source: Information SciencesAuthor(s): Nicolas Boutry, Rocio Gonzalez-Diaz, Maria-Jose JimenezAbstractIn previous work we proposed a combinatorial algorithm to “locally repair” the cubical complex Q(I) that is canonically associated with a given 3D picture I. The algorithm constructs a 3D polyhedral complex P(I) which is homotopy equivalent to Q(I) and whose boundary surface is a 2D manifold. A polyhedral complex satisfying these properties is called well-composed. In the present paper we extend these results to higher dimensions. We prove that for a given n-dimensional picture the obtained cell complex is well-composed in a weaker sense but is still homotopy equivalent to the initial cubical complex.
  • Three-way class-specific attribute reducts from the information viewpoint
    • Abstract: Publication date: Available online 5 June 2018Source: Information SciencesAuthor(s): Xianyong Zhang, Jilin Yang, Lingyu TangAbstractBy virtue of granular computing, attribute reduction of rough sets can effectively perform information processing. Regarding the decision table with granular structures, the traditional classification-based attribute reducts at the macro-top already have the algebra and information interpretations, while the new class-specific attribute reducts at the meso-middle currently have only the algebra explanation. Aiming at the class-specific reducts, this paper establishes three-way types from the information viewpoint, and it further compares the information and algebra types. At first, the three-way information class-specific reducts (including the likelihood, prior, and posterior types) are systematically proposed by using the three-way weighted entropies with uncertainty measurement. Then, optimization preservation conditions of the positive region and weighted entropy are deeply mined to describe reduction targets of the algebra and information types, and relevant conditions related to knowledge coarsening are effectively represented by the granular merging and three-way regions. Furthermore, all four types of class-specific reducts gain their relationships, including the strong-weak relationship based on optimization preservation conditions, the degeneration relationship based on consistency and inconsistency, and the information relationship based on systematic equalities. Finally, algebra and information class-specific reducts and their relationships are verified by a consistency example and an inconsistency example. As a conclusion, the four types of class-specific reducts adopt distinctive algebra or information perspectives to present the difference and emphasis, especially in the inconsistency case, and they have in-depth mutual relationships. This study provides novel insight into attribute reduction based on granular computing, mainly via the information theory and three-way decisions.
  • Planning and operational energy optimization solutions for smart buildings
    • Abstract: Publication date: Available online 5 June 2018Source: Information SciencesAuthor(s): David Sembroiz, Davide Careglio, Sergio Ricciardi, Ugo FioreAbstractThe incremented popularity of Internet of Things (IoT), thanks to improvements both in hardware and software of sensors over the last years, enables the possibility to monitor and gather any kind of data. Additionally, the arrangement of heterogeneous sensors, capable of perceiving information about their surroundings, into a rich Wireless Sensor Network (WSN) allows the appearance of complex systems in which resources are managed more efficiently. Smart cities, buildings, parkings, emergency services are appearing, where control over energy consumption and better sustainability are coupled with an improvement of the comfort of occupants. In this paper, we address the problem of energy optimization in smart buildings, considering both the planning and operational aspects. Specifically, the first aim is to propose an optimal deployment of the WSN inside a building. For this, we present a model able to identify the optimal locations for different types of sensors and gateways, by optimizing energy consumption while fulfilling connectivity, resource, protection, and clustering coverage constraints. Once the IoT system is deployed, we address the problem of how the building actually functions, according to the behaviour of the occupants. In particular, we propose a Building Management System (BMS) capable of efficiently and automatically manage the building elements using human behavioural models, thus lowering the overall building energy consumption whilst maintaining acceptable levels of comfort.
  • Multigranulation rough-fuzzy clustering based on shadowed sets
    • Abstract: Publication date: Available online 30 May 2018Source: Information SciencesAuthor(s): Jie Zhou, Zhihui Lai, Duoqian Miao, Can Gao, Xiaodong YueAbstractIn this study, a new technique of rough-fuzzy clustering based on multigranulation approximation regions is developed to tackle the uncertainty associated with the fuzzifier parameter m. According to shadowed set theory, the multigranulation approximation regions for each cluster can be formed based on fuzzy membership degrees under the multiple values of fuzzifier parameter with a partially ordered relation. The uncertainty generated by the fuzzifier parameter m can be captured and interpreted through the variations in approximation regions among different levels of granularity, rather than at a single level of granularity under a specific fuzzifier value. An ensemble strategy for updating prototypes is then presented based on the constructed multigranulation approximation regions, in which the prototype calculations that may be spoiled due to the uncertainty caused by a single fuzzifier value can be modified. Finally, a multilevel degranulation mechanism is introduced to evaluate the validity of clustering methods. By integrating the notions of shadowed sets and multigranulation into rough-fuzzy clustering approaches, the overall topology of data can be captured well and the uncertain information implicated in data can be effectively addressed, including the uncertainty generated by fuzzification coefficient, the vagueness arising in boundary regions and overlapping partitions. The essence of the proposed method is illustrated by comparative experiments in terms of several validity indices.
  • Accelerating Binary Biclustering on Platforms with CUDA-enabled GPUs
    • Abstract: Publication date: Available online 10 May 2018Source: Information SciencesAuthor(s): Jorge González-Domínguez, Roberto R. ExpósitoAbstractData mining is nowadays essential in many scientific fields to extract valuable information from large input datasets and transform it into an understandable structure. For instance, biclustering techniques are very useful in identifying subsets of two-dimensional data where both rows and columns are correlated. However, some biclustering techniques have become extremely time-consuming when processing very large datasets, which nowadays prevents their use in many areas of research and industry (such as bioinformatics) that have experienced an explosive growth on the amount of available data. In this work we present CUBiBit, a tool that accelerates the search for relevant biclusters on binary data by exploiting the computational capabilities of CUDA-enabled GPUs as well as the several CPU cores available in most current systems. The experimental evaluation has shown that CUBiBit is up to 116 times faster than the fastest state-of-the-art tool, BiBit, in a system with two Intel Sandy Bridge processors (16 CPU cores) and three NVIDIA K20 GPUs. CUBiBit is publicly available to download from
  • Multiobjective E-commerce Recommendations based on Hypergraph Ranking
    • Abstract: Publication date: Available online 16 August 2018Source: Information SciencesAuthor(s): Mingsong Mao, Jie Lu, Jialin Han, Guangquan ZhangAbstractRecommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single-objective and search for items that fit the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple objectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User-Item-Attribute-Context data model is proposed to summarize different information resources and high-order relationships for the construction of a multipartite hypergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical experiments are conducted with the dataset from a review site, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommendations. The experiments also demonstrate that this framework is still compatible for traditional single-objective recommendations and can improve accuracy significantly. In conclusion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers.
  • Three-way decisions with decision-theoretic rough sets in multiset-valued
           information tables
    • Abstract: Publication date: Available online 9 August 2018Source: Information SciencesAuthor(s): Xue Rong Zhao, Bao Qing HuAbstractThe goal of three-way decisions is to divide a universe into three pair-wise disjoint regions and to act on some or all of these regions using appropriate strategies. The decision-theoretic rough set model, a typical three-way decision model, trisects a universe using a pair of thresholds computed from loss functions. Previous studies of decision-theoretic rough set models do not consider loss functions based on multiset values and are unable to deal with multiset-valued information tables. In this paper, two generalized decision-theoretic rough set models involving multiset-valued data are proposed, namely a multiset-decision-theoretic rough set model and a multiset-fuzzy-decision-theoretic rough set model. Two methods are introduced that compute expected costs from loss functions. The first method is based on multiset addition and a new normal-multiset multiplication. The second method is based on a new concept known as the p-length of finite normal multisets. These two methods offer different ways of building multiset-decision-theoretic rough set models. By integrating the multiset-decision-theoretic rough set model with the fuzzy decision-theoretic rough set model, a multiset-fuzzy-decision-theoretic rough set model is created, a model which considers fuzzy relations in multiset-valued information tables. An example that recommends different home options is given to illustrate these models.
  • An Efficient Parallel Row Enumerated Algorithm for Mining Frequent
           Colossal Closed Itemsets from High Dimensional Datasets
    • Abstract: Publication date: Available online 3 August 2018Source: Information SciencesAuthor(s): Manjunath K Vanahalli, Nagamma PatilAbstractMining colossal itemsets from high dimensional datasets have gained focus in recent times. The conventional algorithms expend most of the time in mining small and mid-sized itemsets, which do not enclose valuable and complete information for decision making. Mining Frequent Colossal Closed Itemsets (FCCI) from a high dimensional dataset play a highly significant role in decision making for many applications, especially in the field of bioinformatics. To mine FCCI from a high dimensional dataset, the existing preprocessing techniques fail to prune the complete set of irrelevant features and irrelevant rows. Besides, the state-of-the-art algorithms for the same are sequential and computationally expensive. The proposed work highlights an Effective Improved Parallel Preprocessing (EIPP) technique to prune the complete set of irrelevant features and irrelevant rows from high dimensional dataset and a novel efficient Parallel Frequent Colossal Closed Itemset Mining (PFCCIM) algorithm. Further, the PFCCIM algorithm is integrated with a novel Rowset Cardinality Table (RCT), an efficient method to check the closeness of a rowset and also an efficient pruning strategy to cut down the mining search space. The proposed PFCCIM algorithm is the first parallel algorithm to mine FCCI from a high dimensional dataset. The performance study shows the improved effectiveness of the proposed EIPP technique over the existing preprocessing techniques and the improved efficiency of the proposed PFCCIM algorithm over the existing algorithms.
  • Credit Scoring using Three-Way Decisions with Probabilistic Rough Sets
    • Abstract: Publication date: Available online 2 August 2018Source: Information SciencesAuthor(s): Sebastián Maldonado, Georg Peters, Richard WeberAbstractCredit scoring is a crucial task within risk management for any company in the financial sector. On the one hand, it is in the self-interest of banks to avoid approving credits to customers who probably default. On the other hand, regulators require strict risk management systems from banks to protect their customers and, from “too big to fail institutions”, to avoid bankruptcy with negative impacts on an economy as a whole. However, credit scoring is also expensive and time-consuming. So, any possible method, like three-way decisions, to further increase its efficiency, is worth a try. We propose a two-step approach based on three-way decisions. Customers whose credit applications can be approved or rejected right away are decided in a first step. For the remaining credit applications, additional information is gathered in a second step. Hence, these decisions are more expensive than the ones in the first step. In our paper, we present a methodology to apply three-way decisions with probabilistic rough sets for credit scoring and an extensive case study with more than 7,000 credit applications from Chilean micro-enterprises.
  • Fuzzy integrated Bayesian Dempster-Shafer Theory to defend cross-layer
           heterogeneity attacks in Communication Network of Smart Grid
    • Abstract: Publication date: Available online 29 July 2018Source: Information SciencesAuthor(s): Durgadevi Velusamy, Ganeshkumar PugalendhiAbstractCross-layer heterogeneity is a critical issue in designing a secured communication network for a Smart Grid, as it shows a high degree of uncertainty during packet transmission. In this paper, the Bayesian theory is combined with Dempster-Shafer theory (BDST) to handle physical layer (transmission rate of the node) and medium access control (MAC) layer (buffering capacity of the node) metrics to calculate trust at node level for packet delivery. Further, the fuzzy theory is integrated with BDST to handle MAC layer (Capacity of the link) and Network layer (Distance and Link Quality) metrics for calculating trust at link level for secured routing. Experimental setup is created using Network Simulator 2 (NS2) to demonstrate how the cross-layer metrics are handled by the proposed fuzzy-based Bayesian Dempster-Shafer trusted routing (BDSFTR). Extensive experiments are conducted with the inclusion of malicious and faulty nodes to highlight the performances of the proposed BDSFTR in the identification of the on-off, packet dropping, and bad-mouthing attacks. From the simulation results, it is clear that the proposed model provides a reliable and secured trust-based routing framework for the communication network of smart grid.
  • An Efficient Manifold Regularized Sparse Non-negative Matrix Factorization
           Model for Large-scale Recommender Systems on GPUs
    • Abstract: Publication date: Available online 27 July 2018Source: Information SciencesAuthor(s): Hao Li, Keqin Li, Jiyao An, Weihua Zheng, Kenli LiAbstractNon-negative Matrix Factorization (NMF) plays an important role in many data mining applications for low-rank representation and analysis. Due to the sparsity that is caused by missing information in many high-dimension scenes, e.g., social networks or recommender systems, NMF cannot mine a more accurate representation from the explicit information. Manifold learning can incorporate the intrinsic geometry of the data, which is combined with a neighborhood with implicit information. Thus, manifold-regularized NMF (MNMF) can realize a more compact representation for the sparse data. However, MNMF suffers from a) the forming of large-scale Laplacian matrices, b) frequent large-scale matrix manipulation, and c) the involved K-nearest neighbor points, which will result in the over-writing problem in parallelization. To address these issues, a single-thread-based MNMF model is proposed on two types of divergence, i.e., Euclidean distance and Kullback-Leibler (KL) divergence, which depends only on the involved feature-tuples’ multiplication and summation and can avoid large-scale matrix manipulation. Furthermore, this model can remove the dependence among the feature vectors with fine-grain parallelization inherence. On that basis, a CUDA parallelization MNMF (CUMNMF) is presented on GPU computing. From the experimental results, CUMNMF achieves a 20X speedup compared with MNMF, as well as a lower time complexity and space requirement.
  • Fuzzy Neighborhood Covering for Three-Way Classification
    • Abstract: Publication date: Available online 27 July 2018Source: Information SciencesAuthor(s): X.D. Yue, Y.F. Chen, D.Q. Miao, H. FujitaAbstractNeighborhood Covering (NC) is the union of homogeneous neighborhoods and provides a set-level approximation of data distribution. Because of the nonparametric property and the robustness to complex data, neighborhood covering has been widely used for data classification. Most existing methods directly classify data samples according to the nearest neighborhoods. However, the certain classification methods strictly classify the uncertain data and may lead to serious classification mistakes. To tackle this problem, we extend traditional neighborhood coverings to fuzzy ones and thereby propose a Three-Way Classification method with Fuzzy Neighborhood Covering (3WC-FNC). Fuzzy neighborhood covering consists of membership functions and forms an approximate distribution of neighborhood belongingness. Based on the soft partition induced by the memberships of fuzzy neighborhood coverings of different classes, data samples are classified into Positive (certainly belonging to a class), Negative (certainly beyond classes) and Uncertain cases. Experiments verify that the proposed three-way classification method is effective to handle the uncertain data and in the meantime reduce the classification risk.
  • Game Theoretic Approach to Shadowed Sets: A Three-way Tradeoff Perspective
    • Abstract: Publication date: Available online 26 July 2018Source: Information SciencesAuthor(s): Yan Zhang, JingTao YaoAbstractThree-way approximations can be constructed by shadowed sets based on a pair of thresholds. The determination and interpretation of the thresholds is one of the key issues for applying three-way approximations. We apply a principle of tradeoff with games in order to determine the thresholds of three-way approximations in the shadowed set context. The changes of the elevation and reduction errors with the alteration of thresholds are examined and analyzed. The proposed game-theoretic shadowed sets (GTSS) aim to determine the thresholds of three-way approximations according to a principle of tradeoff with games. GTSS employ game theoretic approaches to formulate games between the elevation and reduction errors. A repetition learning mechanism is adopted to gradually reach balanced threshold pairs by repeatedly formulating games and finding the equilibria between the errors. The shadowed set based three-way approximations defined by the resulting thresholds represent a tradeoff between the elevation and reduction errors. Feasibility study and effectiveness analysis of GTSS is conducted with an experimental data set.
  • Parallel and Distributed Association Rule Mining in Life Science: a Novel
           Parallel Algorithm to Mine Genomics Data
    • Abstract: Publication date: Available online 26 July 2018Source: Information SciencesAuthor(s): Giuseppe Agapito, Pietro Hiram Guzzi, Mario CannataroAbstractAssociation rule mining (ARM) is largely employed in several scientific areas and application domains, and many different algorithms for learning association rules from databases have been introduced. Despite the presence of many existing algorithms, there is still room for the introduction of novel approaches tailored for novel kinds of datasets. Because often the efficiency of such algorithms depends on the type of analyzed dataset. For instance, classical ARM algorithms present some drawbacks for biological datasets produced by microarray technologies in particular containing Single Nucleotide Polymorphisms (SNPs). In particular classical algorithms require large execution times also with small datasets. Therefore the possibility to improve the performance of such algorithms by leveraging parallel computing is a growing research area. The main contributions of this paper are: a comparison among different sequential, parallels and distributed ARM techniques, and the presentation of a novel ARM algorithm, named Balanced Parallel Association Rule Extractor from SNPs (BPARES), that employs parallel computing and a novel balancing strategy to improve response time. BPARES improves performance without loosing in accuracy as well as it handles more efficiently the available computational power and reduces the memory consumption.
  • On decision evaluation functions in generalized three-way decision spaces
    • Abstract: Publication date: Available online 24 July 2018Source: Information SciencesAuthor(s): Junsheng Qiao, Bao Qing HuAbstractRecently, Hu proposed the notion of three-way decision spaces based on partially ordered sets with involutive negations by the axiomatic definition. However, involutive negation is so strong that imposes restrictions on the use of the theory of three-way decision spaces from theoretical point of view. Therefore, this paper attempts to extend the concept of three-way decision spaces from partially ordered sets with involutive negations to partially ordered sets with negations. At first, we generalize three-way decision spaces and three-way decisions based on general partially ordered sets with negations such that the existing three-way decisions are the specific cases of generalized three-way decision spaces discussed in this paper. And then, we give some new decision evaluation functions in generalized three-way decision spaces (e.g., decision evaluation functions based on fuzzy sets, interval-valued fuzzy sets, fuzzy relations, shadowed sets and hesitant fuzzy sets), which enriched the class of decision evaluation functions. In particular, they provide more choices for decision-makers in realistic decision-making problems. Finally, in order to illustrate the practical applications of the results presented in this paper, we analyse a practical example of an evaluation problem of credit card applicants.
  • Exploiting GPU and cluster parallelism in single scan frequent itemset
    • Abstract: Publication date: Available online 20 July 2018Source: Information SciencesAuthor(s): Youcef Djenouri, Djamel Djenouri, Asma Belhadi, Alberto CanoAbstractThis paper considers discovering frequent itemsets in transactional databases and addresses the time complexity problem by using high performance computing (HPC). Three HPC versions of the Single Scan (SS) algorithm are proposed. The first one (GSS) implements SS on a GPU (Graphics Processing Unit) architecture using an efficient mapping between thread blocks and the input data. The second approach (CSS) implements SS on a cluster architecture by scheduling independent jobs to workers in a cluster. The third, (CGSS) accelerates the frequent itemset mining process by using multiple cluster nodes equipped with GPUs. Moreover, three partitioning strategies are proposed to reduce GPU thread divergence and cluster load imbalance. Results show that CGSS outperforms SS, GSS, and CSS in terms of speedup. Specifically, CGSS provides up to a 350 times speedup for low minimum support values on large datasets. GCSS demonstrably outperforms the state-of-the-art HPC-based algorithms on big databases.
  • Raspberry Pi Assisted Facial Expression Recognition Framework for Smart
           Security in Law-Enforcement Services
    • Abstract: Publication date: Available online 17 July 2018Source: Information SciencesAuthor(s): Muhammad Sajjad, Mansoor Nasir, Fathu Min Allah, Khan Muhammad, Arun Kumar Sangaiah, Sung Wook BaikAbstractFacial expression recognition is an active research area for which the research community has presented a number of approaches due to its diverse applicability in different real-world situations such as real-time suspicious activity recognition for smart security, monitoring, marketing, and group sentiment analysis. However, developing a robust application with high accuracy is still a challenging task mainly due to the inherent problems related to human emotions, lack of sufficient data, and computational complexity. In this paper, we propose a novel, cost-effective, and energy-efficient framework designed for suspicious activity recognition based on facial expression analysis for smart security in law-enforcement services. The Raspberry Pi camera captures the video stream and detects faces using the Viola Jones algorithm. The face region is pre-processed using Gabor filter and median filter prior to feature extraction. Oriented FAST and Rotated BRIEF (ORB) features are then extracted and the support vector machine (SVM) classifier is trained, which predicts the known emotions (Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise). Based on the collective emotions of the faces, we predict the sentiment behind the scene. Using this approach, we predict if a certain situation is hostile and can prevent it prior to its occurrence. The system is tested on three publically available datasets: Cohen Kande (CK+), MMI, and JAFEE. A detailed comparative analysis based on SURF, SIFT, and ORB is also presented. Experimental results verify the efficiency and effectiveness of the proposed system in accurate recognition of suspicious activity compared to state-of-the-art methods and validate its superiority for enhancing security in law enforcement services.
  • Exploiting Data Centres Energy Flexibility in Smart Cities: Business
    • Abstract: Publication date: Available online 11 July 2018Source: Information SciencesAuthor(s): Tudor Cioara, Ionut Anghel, Ioan Salomie, Marcel Antal, Claudia Pop, Massimo Bertoncini, Diego Arnone, Florin PopAbstractIn this paper, we have considered Data Centres (DCs) as computing facilities functioning at the crossroad of electrical, thermal and data networks and have defined optimisation techniques to exploit their energy flexibility. Our methods are leveraging on non-electrical cooling devices such as thermal storage and heat pumps for waste heat reuse and IT workload execution time shifting and spatial relocation in federated DCs. To trade energy flexibility we have defined an Energy Marketplace which allows DCs to act as active energy players integrated into the smart grid, contributing to smart city-level efficiency goals. Reinforcing this vision, we have proposed four innovative business scenarios that enable next generation smart Net-zero Energy DCs acting as energy prosumers at the interface with smart energy grids within smart city environments. Simulation experiments are conducted to determine the DCs potential electrical and thermal energy flexibility in meeting various network level goals and to assess the financial viability of the defined business scenarios. The results show that DCs have a significant amount of energy flexibility which may be shifted and traded to interested stakeholders thus allowing them to gain new revenue streams not foreseen before.
  • Intrusion Detection and Security Calculation in Industrial Cloud Storage
           based on an Improved Dynamic Immune Algorithm
    • Abstract: Publication date: Available online 10 July 2018Source: Information SciencesAuthor(s): Weikai Wang, Lihong Ren, Lei Chen, Yongsheng DingAbstractCloud computing is a new storage and calculation mode, which has been widely used as a tool to store and analyze users’ data. With the development of industrial intelligence and big data, it is also prevalent in industrial field to accelerate production or to discover more knowledge to make better decisions. There are three main properties in cloud computing, namely dynamic and time-variation, large-scale, and change-of-ownership. Therefore, the data security problems are different and complicated than those in conventional networks. The existing methods, such as encrypted storage, security audit, and access control, cannot solve these security problems proactively and efficiently due to their own drawbacks. Commonly, self-samples always have similar features, while non-self-samples are abnormal in their own way. This paper mainly focuses on these two sources of data, one is intrusion detection of external non-self-samples and another is security calculation of self-samples. In this case, an improved dynamic immune algorithm (IDIA) is proposed, which is composed of an improved negative selection algorithm (iNSA) with the way of shift mutation and an improved dynamic clone selection algorithm (iDCS) with random grouping strategy. The former algorithm is used to generate detectors, and the latter is used to update them dynamically and adaptively. On this basis, an automated industrial production system is designed to guarantee the data security that not been accessed by external or other users. Datasets of KDD CUP99 and real industrial production data are collected to evaluate the performance of IDIA. The experiment results show the proposed algorithm can detect the intrusive data samples and identify autologous ones efficaciously.
  • Counting cliques in parallel without a cluster Engineering a fork/join
           algorithm for shared-memory platforms
    • Abstract: Publication date: Available online 10 July 2018Source: Information SciencesAuthor(s): Emilio Coppa, Irene Finocchi, Renan Leon GarciaAbstractIn this paper we develop simple and fast multicore parallel algorithms for counting the number of k-cliques in large undirected graphs, for any small constant k ≥ 4. Clique counting is an important problem in a variety of network analytics applications. Differently from existing solutions, which mainly target distributed memory settings (e.g., MapReduce), our algorithms work on off-the-shelf shared-memory multicore platforms.We assess the effectiveness of our approach through an extensive experimental analysis on a variety of real-world graphs, considering different clique sizes and scalability on different numbers of cores. The experimental results show that our parallel algorithms largely outperform the running times of highly optimized sequential solutions and gracefully scale to non-trivial values of k even on medium/large graphs. For instance, computing hundreds of billions of cliques for rather demanding Web graphs and social networks requires about 15 minutes on a 32-core machine. As a by-product of our experimental analysis, we also compute the exact number of k-cliques with at most 20 nodes in many real-world networks from the SNAP repository.
  • AnD: A Many-Objective Evolutionary Algorithm with Angle-based Selection
           and Shift-based Density Estimation
    • Abstract: Publication date: Available online 3 July 2018Source: Information SciencesAuthor(s): Zhi-Zhong Liu, Yong Wang, Pei-Qiu HuangAbstractEvolutionary many-objective optimization has been gaining increasing attention from the evolutionary computation research community. Much effort has been devoted to addressing this issue by improving the scalability of multiobjective evolutionary algorithms, such as Pareto-based, decomposition-based, and indicator-based approaches. Different from current work, we propose an alternative algorithm in this paper called AnD, which consists of an angle-based selection strategy and a shift-based density estimation strategy. These two strategies are employed in the environmental selection to delete poor individuals one by one. Specifically, the former is devised to find a pair of individuals with the minimum vector angle, which means that these two individuals have the most similar search directions. The latter, which takes both diversity and convergence into account, is adopted to compare these two individuals and to delete the worse one. AnD has a simple structure, few parameters, and no complicated operators. The performance of AnD is compared with that of seven state-of-the-art many-objective evolutionary algorithms on a variety of benchmark test problems with up to 15 objectives. The results suggest that AnD can achieve highly competitive performance. In addition, we also verify that AnD can be readily extended to solve constrained many-objective optimization problems.
  • Exploiting GPU parallelism in improving bees swarm optimization for mining
           big transactional databases
    • Abstract: Publication date: Available online 3 July 2018Source: Information SciencesAuthor(s): Youcef Djenouri, Djamel Djenouri, Asma Belhadi, Philippe Fournier-Viger, Jerry Chun-Wei Lin, Ahcene BendjoudiAbstractThis paper investigates the use of GPU (Graphics Processing Unit) in improving the bees swarm optimization metaheuristic performance for solving the association rule mining problem. Although this metaheuristic proved its effectiveness, it requires huge computational resource when considering big databases for mining. To overcome this limitation, we develop in this paper a GPU-based Bees Swarm Optimization Miner (GBSO-Miner) where the GPU is used as a co-processor to compute the CPU-time intensive steps of the algorithm. Unlike state-of-the-art GPU-based ARM methods, all BSO steps including the determination of search area, the local search, the evaluation, and the dancing are performed on GPU. A mapping method between the data input of each task and the GPU blocks/threads is developed. To demonstrate the effectiveness of the GBSO-Miner framework, intensive experiments have been carried out. The results show that GBSO-Miner outperforms the baseline methods of the literature (GPApriroi, MEGPU, and Dmine) using big textual and graph databases. The results reveal that GBSO-Miner is up to 800 times faster than an optimized CPU-Implementation.
  • A multi-objective pigeon-inspired optimization approach to UAV distributed
           flocking among obstacles
    • Abstract: Publication date: Available online 2 July 2018Source: Information SciencesAuthor(s): Huaxin Qiu, Haibin DuanAbstractUnmanned aerial vehicle (UAV) flocking control with obstacle avoidance is a many-objective optimization problem for centralized algorithms. A UAV flocking distributed optimization control frame is designed to render the many-objective optimization problem into a multi-objective optimization solved by a single UAV. For different objectives, two kinds of criteria are raised to guarantee flight safety: the hard constraints that must be satisfied and the soft ones that will be optimized. Considering the restrictions of onboard computing resources, multi-objective pigeon-inspired optimization (MPIO) is modified based on the hierarchical learning behavior in pigeon flocks. On such a basis, a UAV distributed flocking control algorithm based on the modified MPIO is proposed to coordinate UAVs to fly in a stable formation under complex environments. Comparison experiments with basic MPIO and a modified non-dominated sorting genetic algorithm (NSGA-II) are carried out to show the feasibility, validity, and superiority of the proposed algorithm.
  • A Global and Updatable ECG Beat Classification System Based on Recurrent
           Neural Networks and Active Learning
    • Abstract: Publication date: Available online 2 July 2018Source: Information SciencesAuthor(s): Guijin Wang, Chenshuang Zhang, Yongpan Liu, Huazhong Yang, Dapeng Fu, Haiqing Wang, Ping ZhangAbstractThe key challenges faced in the automatic diagnosis of arrhythmia by electrocardiogram (ECG) is enormous differences among individual patients and high cost of labeling clinical ECG records. In order to establish a system with an automatic feature learning scheme and an effective optimization mechanism, we propose a global and updatable classification scheme named Global Recurrent Neural Network (GRNN). Recurrent Neural Network (RNN) is adopted to explore the underlying features of ECG beats, based on morphological and temporal information. In order to improve system performance when new samples are obtained, active learning is applied to select the most informative beats and incorporate them into training set. The system is then updated as the training set grows. Our GRNN has three main innovations. Firstly, relying on the large capacity and fitting ability of GRNN, we can classify samples of multiple different patients with a single model. Secondly, the GRNN improves generalization performance when training samples and test samples are from distinct databases. This can be explained that the optimization mechanism finds the most informative samples to improve performance as training data. Finally, RNN automatically learns the underlying differences among the samples from different classes. Experimental results prove that the GRNN system achieves the state-of-the-art performance with a single model. In across-database experiments where the training data and test data are from different databases respectively, the GRNN achieves significant improvement compared with other algorithms. This study illustrates the feasibility of a global and updatable ECG beat classification system in practical applications.
  • Modified NSGA-III for sensor placement in water distribution system
    • Abstract: Publication date: Available online 28 June 2018Source: Information SciencesAuthor(s): Chengyu Hu, Liguo Dai, Xuesong Yan, Wenyin Gong, Xiaobo Liu, Ling WangAbstractContaminant events in drinkable water distribution systems (WDSs) have occurred frequently in recent years, causing severe damages, economic loss, and long-lasting societal impact. A critical and effective method to monitor WDS in real-time is deploying a water quality sensor. However, the placement of such sensors in a water distribution network (WDN) has become a foremost concern around the world. In this paper, we first analyze sensor placement mathematically and prove that it is NP-hard. Subsequently, we distinguish between single- and multi-objective optimization, and attempt, for the first time, to propose a modified NSGA-III to solve many-objective optimization for the sensor placement problem. WDNs of two sizes are employed and simulation results demonstrate the validity and effectiveness of the proposed model and methodology. The future research works are also identified and discussed.
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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