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Information Sciences
Journal Prestige (SJR): 1.635
Citation Impact (citeScore): 5
Number of Followers: 643  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0020-0255 - ISSN (Online) 0020-0255
Published by Elsevier Homepage  [3161 journals]
  • PEER: A direct method for biosequence pattern mining through waits of
           optimal k-mers
    • Abstract: Publication date: May 2020Source: Information Sciences, Volume 517Author(s): Uddalak Mitra, Balaram Bhattacharyya, Tathagato MukhopadhyayAbstractAchieving accuracy of alignment-based methods at linear time complexity is desirable for biosequence studies. k-mer statistics is the principal alternative, but selecting the optimal k is crucial for best feature extraction. Prevalent methods require successive trials upon incrementing k for best match with a reference phylogeny tree.We observe that successive intervals(or waits) of optimal length k-mers contain precise information of the sequence such that feature extraction is possible from entropies of the waits. We introduce a method, Pattern Extraction through Entropy Retrieval(PEER), that transforms a sequence into a vector of wait entropies of optimal k-mers. Distance between a pair of sequences amounts to the Euclidean Distance between their wait vectors. We present an analytical determination of optimal k from maximality of total wait entropy. This makes PEER free from the usual multiple trials for obtaining optimal k.We conduct experiments on several benchmark datasets of omics clades for phylogeny analysis and perform an in-depth comparison against seven state-of-the-art alignment-free methods. Phylogeny tree from PEER distance closely resembles the corresponding biological taxonomy and achieves the best Robinson-Foulds score. PEER can sense small artificial mutations within sequence. It is highly scalable with linear time complexity, exceptionally useful for comparing long sequences.
       
  • Hybrid Many-Objective Particle Swarm Optimization Algorithm for Green Coal
           Production Problem
    • Abstract: Publication date: Available online 15 January 2020Source: Information SciencesAuthor(s): Zhihua Cui, Jiangjiang Zhang, Di Wu, Xingjuan Cai, Hui Wang, Wensheng Zhang, Jinjun ChenAbstractThe key aspect in coal production is realizing safe and efficient mining to maximize the utilization of the resources. A requirement for sustainable economic development is realizing green coal production, which is influenced by factors of coal economic, energy, ecological, coal gangue economic and social benefits. To balance these factors, this paper proposes a many-objective optimization model with five objectives for green coal production. Furthermore, a hybrid many-objective particle swarm optimization (HMaPSO) algorithm is designed to solve the established model. A new offspring of the alternative pool is generated by employing different evolutionary operators. The environmental selection mechanism is adopted to select and store the excellent solutions. Two sets of experiments are performed to verify the effectiveness of the proposed approach: First, the HMaPSO algorithm is tested on the DTLZ functions, and its performance is compared with that of several widely used many-objective algorithms. Second, the HMaPSO algorithm is applied to solve the many-objective green coal production optimization model. The computational results demonstrate the effectiveness of the proposed approach, and the simulation results prove that the designed approach can provide promising choices for decision makers in regional planning.
       
  • Multiple criteria group decision making based on group satisfaction
    • Abstract: Publication date: Available online 15 January 2020Source: Information SciencesAuthor(s): Chao Fu, Wenjun Chang, Shanlin YangTo generate solutions to multiple criteria group decision-making (MCGDM) problems that are satisfactory to the decision makers, this paper proposes a new method. To examine whether a group solution is satisfactory to the decision makers, group satisfaction is constructed from alternative assessment and ranking differences between the decision makers and the group. The difference between a decision maker's assessment and a group's assessment is designed based on differences in assessment grades, whose normalization is theoretically proven to construct alternative assessment differences. Inspired by Spearman's rank correlation coefficient, the expected utilities of decision makers’ and the group's assessments are used to construct alternative ranking differences. An abstract two-variable function with specific properties is designed to relate alternative assessment difference to alternative ranking difference to form group satisfaction. From the constructed group satisfaction, the process of generating group-satisfactory solutions to MCGDM problems is presented. The problem of selecting engineering project management software is analyzed by using the proposed method to demonstrate its applicability. To highlight the importance of group satisfaction in MCGDM, relationships and differences between group satisfaction and group consensus are analyzed through the problem and simulation experiments.
       
  • An evidential integrated method for maintaining case base and vocabulary
           containers within CBR systems
    • Abstract: Publication date: Available online 14 November 2019Source: Information SciencesAuthor(s): Safa Ben Ayed, Zied Elouedi, Eric LefevreAbstractCases and vocabulary maintenance presents a crucial task to preserve high competent Case-Based Reasoning (CBR) systems, since the accuracy of their offered solutions are strongly dependent on stored cases and their describing attributes quality. The maintenance aims generally at eliminating two types of undesirable knowledge which are noisy and redundant data. However, inexpedient Case Base Maintenance (CBM) or vocabulary maintenance may not only greatly decrease CBR competence in solving new problems, but also reduce its performance in term of retrieval time. Besides, to provide a high maintenance quality, it is necessary to manage uncertainty within knowledge since “real-world data are never perfect” and stored cases within a CBR system’s Case Base (CB) describe real-world experiences. Hence, we propose, in this paper, a new integrated method that maintains both of the CB and the vocabulary knowledge containers of CBR systems by offering a new alternating technique to properly detect noisiness and redundancy whether in cases or features. During the learning steps of our new integrated maintenance policy, which drives the decision making about cases and attributes selection, we manage uncertainty using one among the most powerful tools called the Belief Function Theory.
       
  • Effective privacy preserving data publishing by vectorization
    • Abstract: Publication date: Available online 26 September 2019Source: Information SciencesAuthor(s): Chris Soo-Hyun Eom, Charles Cheolgi Lee, Wookey Lee, Carson K. LeungAbstractAs smart devices and cloud services are rapidly expanding, a large amount of location information can easily be gathered. However, there is a conflict between collecting location data and protecting personal data since obtaining and utilizing the data may be restricted due to privacy concerns. Various methods for anonymity and on the original location data have been studied, but these methods have excessively reduced data utility while stressing highly on privacy preservation. In this article, we suggest a novel model to overcome this fundamental dilemma via a surrogate vector based on the grid environment. Compared to the existing approaches, our model shows a new theoretical advancement in privacy protection, and outstanding performance with respect to time complexity and data utility has been achieved.
       
  • Privacy by Evidence: A Methodology to develop privacy-friendly
           software applications
    • Abstract: Publication date: Available online 25 September 2019Source: Information SciencesAuthor(s): Pedro Barbosa, Andrey Brito, Hyggo AlmeidaAbstractIn an increasingly connected world, a diversity of data is collected from the environment and its inhabitants. Because of the richness of the information, privacy becomes an important requirement. Although there are principles and rules, there is still a lack of methodologies to guide the integration of privacy guidelines into the development process. Methodologies like the Privacy by Design (PbD) are still vague and leave many open questions on how to apply them in practice. In this work we propose a new concept, called Privacy by Evidence (PbE), in the form of a software development methodology to provide privacy-awareness. Given the difficulty in providing total privacy in many applications, we propose to document the mitigations in form of evidences of privacy, aiming to increase the confidence of the project. To validate its effectiveness, PbE has been used during the development of four case studies: a smart metering application; a people counting and monitoring application; an energy efficiency monitoring system; and a two factor authentication system. For these applications, the teams were able to provide seven, five, five, and four evidences of privacy, respectively, and we conclude that PbE can be effective in helping to understand and address the privacy protection needs when developing software.
       
  • Cloud-based lightweight secure RFID mutual authentication protocol in IoT
    • Abstract: Publication date: Available online 4 August 2019Source: Information SciencesAuthor(s): Kai Fan, Qi Luo, Kuan Zhang, Yintang YangAbstractRadio Frequency Identification (RFID) is a supporting technology for the Internet of things (IoT). RFID enables all physical devices to be connected to IoT. When RFID is widely used and developing rapidly, its security and privacy issues cannot be ignored. The wireless broadcast channel between the tag and the reader may be subject to many security attacks, such as interception, modification, and replay. Messages from unverified tags or readers are also untrustworthy. A secure and stable RFID authentication scheme is critical to IoT. This paper puts forward an efficient and reliable cloud-based RFID authentication scheme. In order to reduce the RFID tag's overhead, the proposed authentication scheme explores the rotation and enhanced permutation to encrypt data. The proposed protocol not only resists the above common attacks and protects the privacy of the tag, but also adds the cloud server to the RFID system. Performance simulation shows that permutation and rotation are efficient. Security analysis shows that our protocol can resist various attacks, such as tracking, replay, and desynchronization attack. Mutual authentication and backward security are also achieved. Finally, we apply BAN logic to prove the security of the protocol.
       
  • Privacy-preserving computation in cyber-physical-social systems: A survey
           of the state-of-the-art and perspectives
    • Abstract: Publication date: Available online 26 July 2019Source: Information SciencesAuthor(s): Jun Feng, Laurence T. Yang, Nicholaus J. Gati, Xia Xie, Benard S. GavunaAbstractCyber-physical-social systems (CPSSs) are leading digital revolutions in academia, industry and government. Due to the rise of big data analytics, tensor computations are currently used in CPSSs. With the increasing popularity of cloud computing or fog computing, big data in CPSSs are usually sent to clouds or fogs for computations. Recently, some studies about privacy-preserving computation have been conducted to address security concerns which enable data analysis and processing in cloud or fog environments in a privacy-preserving way. To fully understand the state-of-the-art advances and discover the research directions of this field, in this survey, both previous and current privacy-preserving schemes are comprehensively reviewed and studied. In addition, a novel privacy-preserving tensor computation framework, a case study, and several future research directions are presented for CPSSs.
       
  • Incentive mechanism for cooperative authentication: An evolutionary game
           approach
    • Abstract: Publication date: Available online 16 July 2019Source: Information SciencesAuthor(s): Liang Fang, Guozhen Shi, Lianhai Wang, Yongjun Li, Shujiang Xu, Yunchuan GuoAbstractIn mobile opportunistic networks (MONs), cooperative authentication is an efficient way to filter false or misleading messages. However, due to privacy issues and concerns related to the consumption of resources, without adequate incentives, most mobile users (or nodes) with limited resources often act selfishly. These users are frequently uninterested to help others to authenticate such messages. In this study, a cooperative authentication model was formulated in the form of an evolutionary game. This model addresses the problems caused when cooperative nodes do not have all the information regarding other neighboring nodes, which makes them inadequately rational. Herein, the behavior dynamics and evolutionary stable strategy (ESS) of neighboring nodes were derived. We showed that the behavior dynamics converge to the ESS. This induces the neighboring nodes to independently decide whether to participate in authentication or not, without depending on information from other nodes (therefore, our approach can be implemented in the de-centralized manner). Further, a scheme to help the source node was also designed to determine an optimal budget. Experiments were conducted both on simulated as well as real datasets. The results demonstrate that our approach exhibits overwhelming advantages to incentivize selfish nodes in MONs to cooperate.
       
  • An efficient approach for secure multi-party computation without
           authenticated channel
    • Abstract: Publication date: Available online 14 July 2019Source: Information SciencesAuthor(s): Duy-Hien Vu, The-Dung Luong, Tu-Bao HoAbstractSecure multi-party sum is one of the most important secure multi-party computation protocols. It has been widely applied to solve many privacy-preservation problems such as privacy-preserving data mining, secure auction, secure electronic voting, and privacy-preserving statistical data analysis. To guarantee the correctness of the final output and to enhance the security level, the existing secure multi-party sum protocols have to use authenticated channels, even secure channels for participants to communicate, however such a usage requirement significantly reduces their performance. Furthermore, these secure multi-party sum protocols are impossible to run on public networks. In this paper, we propose a new secure multi-party sum protocol that can ensure the correctness of the output result as well as securely protecting the parties’ privacy against attacks without requiring any authenticated/secure channel. The proposed protocol is based on a multi-party sum function employing a variant of ElGamal encryption and a Schnorr signature-derived authentication method, in which both these cryptographic tools use the same private and public keys. Additionally, our comparative evaluation shows that the proposed protocol is efficient and practical.
       
  • A privacy-preserving RFID authentication protocol based on El-Gamal
           cryptosystem for secure TMIS
    • Abstract: Publication date: Available online 9 July 2019Source: Information SciencesAuthor(s): Fatty M. Salem, Ruhul AminAbstractThe healthcare environment now provides the facility for patients to communicate with doctors from home via the Internet; this facility is very useful for seriously ill patients. Errors in medication are hazardous and can cause significant harm to patients; therefore, patient medication and information safety are essential issues in such a healthcare environment. To protect this sensitive information, an authentication protocol is needed. Moreover, in the context of sharing data including a patient's personal information, privacy leakage has become one of the most challenging issues in a telecare medicine information system (TMIS). In this paper, we propose a privacy-preserving radio frequency identification (RFID) authentication protocol based on the El-Gamal cryptosystem, for enhancing patient medication safety in a TMIS. The proposed protocol can achieve a number of security services and can also resist several types of attacks. We have also shown the results obtained by conducting an "Automated Validation of Internet Security Protocols and Applications" (AVISPA) simulation of our protocol. The simulation results verify that the proposed protocol is safe against active and passive attacks. The results of an informal security analysis also indicate that patient information is highly private, and the system is protected against possible related attacks. Our protocol is not only better in terms of protecting the privacy of patients but it also achieves better performance than similar existing protocols.
       
  • Building a Dynamic Searchable Encrypted Medical Database for Multi-client
    • Abstract: Publication date: Available online 24 May 2019Source: Information SciencesAuthor(s): Lei Xu, Chungen Xu, Joseph K. Liu, Cong Zuo, Peng ZhangAbstractE-medical record is an emerging health information exchange model based on cloud computing. As cloud computing allows companies and individuals to outsource their data and computation, the medical data is always stored at a third party such as cloud, which brings a variety of risks, such as data leakage to the untrusted cloud server, unauthorized access or modification operations. To assure the confidentiality of the data, the data owner needs to encrypt the sensitive data before uploading to the third party. Yet, issues like encrypted data search, flexible access and control on sensitive data have also remained the most significant challenges. In this paper, we investigate a novel searchable encrypted e-medical framework for multi-client which provides both confidentiality and searchability. Different from previous privacy protecting works in secure data outsourcing, we focus on providing a fine-grained access control encrypted data search scheme including clients and data. Our scheme also enables secure data update of the encrypted database by leveraging a secure dynamic searchable encryption. Furthermore, we implement the proposed scheme based on some existed cryptography library, and conduct several experiments on a selected dataset to evaluate its performance. The results demonstrate that our scheme provides a balance between security and efficiency.
       
  • An improved SVD-based blind color image watermarking algorithm with mixed
           modulation incorporated
    • Abstract: Publication date: Available online 13 January 2020Source: Information SciencesAuthor(s): Hwai-Tsu Hu, Ling-Yuan Hsu, Hsien-Hsin ChouAbstractThis study systematically investigated the use of singular value decomposition (SVD) for the blind watermarking of color images. The proposed algorithm overcomes most of the problems typically encountered when using existing SVD-based schemes, while concurrently enhancing performance in terms of imperceptibility and robustness. After applying SVD to non-overlapping 4 × 4 image blocks, level shifting is used to control the embedding strength in accordance with the intensity of pixels in each block. The proposed watermark embedding process helps to preserve orthonormality in the unitary matrix and compensate for the resulting distortion. Iterative regulation ensures the accurate retrieval of the embedded watermark, while mixed modulation helps to improve robustness without compromising image quality. Experiment results demonstrate that the proposed watermarking algorithm is highly resistant to a variety of image processing attacks and error-free in the absence of attack. The proposed method outperforms existing SVD-based schemes in terms of imperceptibility and robustness at a payload capacity of 1/16 bit per pixel.
       
  • Event-triggered constrained control with DHP implementation for nonaffine
           discrete-time systems
    • Abstract: Publication date: Available online 11 January 2020Source: Information SciencesAuthor(s): Mingming Ha, Ding Wang, Derong LiuAbstractThis paper proposes an event-based near-optimal control algorithm for nonaffine discrete-time systems with constrained inputs. The method is derived from the dual heuristic dynamic programming (DHP) technique. The challenge caused by saturating actuators is overcome by using a nonquadratic performance index. Then, the event-based control technique is used to decrease the amount of computation. Meanwhile, the stability analysis is provided. It illustrates that the proposed event-based method can asymptotically stabilize the nonaffine systems by using the Lyapunov method. Furthermore, the stability conditions and the design process of the event-based controller are established. The event-based DHP algorithm is implemented by constructing three neural networks, namely, the model network, the critic network, and the action network. Finally, simulation studies are conducted to demonstrate the applicability and the performance of the proposed method.
       
  • Reliable correlation tracking via dual-memory selection model
    • Abstract: Publication date: Available online 11 January 2020Source: Information SciencesAuthor(s): Guiji Li, Manman Peng, Ke Nai, Zhiyong Li, Keqin LiAbstractCorrelation-filter-based trackers have shown favorable accuracy and efficiency in visual tracking. However, most of these trackers are prone to drift in cases of heavy occlusions and temporal tracking failures because they only maintain the short-term memory of target appearance via a highly adaptive update mode. In this paper, we propose a reliable visual tracking method based on a dual-memory selection (DMS) model to alleviate tracking drift. Considering that long-term memory is robust to heavy occlusions while short-term memory performs well in rapid appearance changes, the proposed DMS model combines these two memory patterns of the target appearance and adaptively selects a reliable memory pattern to handle the current tracking challenges via a memory selector. For each memory pattern, a memory tracker is established based on discriminative correlation filters. The short-term tracker aggressively updates the target model to capture recent appearance changes via a linear interpolation update model, while the long-term tracker conservatively updates the target model to maintain historical appearance characteristics with a memory-improved update model and a dynamic learning rate. Furthermore, a novel memory evaluation criterion (MEC) is developed to evaluate the reliability of each tracker for memory selection. From credibility and discriminability measurements considering the temporal context, the memory tracker with the highest reliability score is selected to determine the target location in each frame. Extensive experiments on public benchmark datasets demonstrate that the proposed tracking method performs favorably compared to multiple recent state-of-the-art methods.
       
  • Robust trimap generation based on manifold ranking
    • Abstract: Publication date: Available online 11 January 2020Source: Information SciencesAuthor(s): Jinjiang LI, Genji YUAN, Hui FANAbstractIn this paper, we propose a simple and effective method for creating accurate trimaps based on input images. Most advanced matting algorithms require the user to provide prior information to estimate high-quality alpha masks, where the prior information is primarily in the form of trimaps. A precise trimap is one of the most important factors affecting the performance of the matting algorithm. It is a very tedious task for users to specify a large number of accurate trimaps, and it is even impractical in some applications. Based on manifold ranking, we use strokes to mark the superpixel nodes to create high-quality trimaps. The experimental results show that the method given in this paper can generate high-quality trimaps, thus ensuring the accuracy of the alpha masks that are estimated by the matting algorithm. We verify the performance of the trimaps that are created using the method given in this paper for various matting algorithms.
       
  • Recognizing novel patterns via adversarial learning for one-shot semantic
           segmentation
    • Abstract: Publication date: Available online 11 January 2020Source: Information SciencesAuthor(s): Guangchao Yang, Dongmei Niu, Caiming Zhang, Xiuyang ZhaoAbstractOne-shot semantic segmentation aims to recognize unseen object regions by using the reference of only one annotated example. Many deep convolutional neural networks have achieved enormous success on this task. However, most of the existing methods only use a fixed annotated dataset to train the network. The remaining unannotated examples remain difficult to be leveraged and recognized. In this study, we propose a procedure based on the generative adversarial network to enable the one-shot semantic segmentation model for learning information from previously unknown categories. Our method contains a segmentation network that generates segmentation predictions. We then use a discriminator to differentiate the probability maps of segmentation prediction from the ground truth distribution. Consequently, we can ignore the pixels classified as fake and only use trustworthy regions as the label to train the segmentation network, thus achieving semi-supervised learning. Experimental results demonstrate the effectiveness of the proposed adversarial learning method with an average gain of 49.7% accuracy score on the PASCAL VOC 2012 dataset.
       
  • Nonnegative Self-Representation with a Fixed Rank Constraint for Subspace
           Clustering
    • Abstract: Publication date: Available online 11 January 2020Source: Information SciencesAuthor(s): Guo Zhong, Chi-Man PunAbstractA number of approaches to graph-based subspace clustering, which assumes that the clustered data points were drawn from an unknown union of multiple subspaces, have been proposed in recent years. Despite their successes in computer vision and data mining, most neglect to simultaneously consider global and local information, which may improve clustering performance. On the other hand, the number of connected components reflected by the learned affinity matrix is commonly inconsistent with the true number of clusters. To this end, we propose an adaptive affinity matrix learning method, nonnegative self-representation with a fixed rank constraint (NSFRC), in which the nonnegative self-representation and an adaptive distance regularization jointly uncover the intrinsic structure of data. In particular, a fixed rank constraint as a prior is imposed on the Laplacian matrix associated with the data representation coefficients to urge the true number of clusters to exactly equal the number of connected components in the learned affinity matrix. Also, we derive an efficient iterative algorithm based on an augmented Lagrangian multiplier to optimize NSFRC. Extensive experiments conducted on real-world benchmark datasets demonstrate the superior performance of the proposed method over some state-of-the-art approaches.
       
  • On matching of intuitionistic fuzzy sets
    • Abstract: Publication date: May 2020Source: Information Sciences, Volume 517Author(s): Maciej Krawczak, Grażyna SzkatułaAbstractIn the paper, we describe the new measure of matching two intuitionistic fuzzy sets. The operations known in the intuitionistic fuzzy set theory are used, and the perturbation of one intuitionistic fuzzy set by another intuitionistic fuzzy set is understood as an impact of one intuitionistic fuzzy set on another intuitionistic fuzzy set. The opposite case can also be considered wherein the second intuitionistic fuzzy set is perturbed by the first one. The introduced measure of the perturbation of one intuitionistic fuzzy set by another intuitionistic fuzzy set is considered instead of commonly used distance between two intuitionistic fuzzy sets. In general, the new measure can be asymmetric and therefore can provide more information compare to a distance between intuitionistic fuzzy sets. The values of such measures of intuitionistic fuzzy sets’ perturbation are ranged between 0 and 1. In this paper specific mathematical properties of the measure of intuitionistic fuzzy sets’ perturbation are studied. The presented methodology is explained by several illustrative examples.
       
  • A Novel Batch Image Encryption Algorithm Using Parallel Computing
    • Abstract: Publication date: Available online 11 January 2020Source: Information SciencesAuthor(s): Wei Song, Yu Zheng, Chong Fu, Pufang ShanAbstractChaos-based encryption provides a practical way to protect the confidentiality of digital images nowadays. The increasing convenience (e.g., larger bandwidth) of data sharing stimulates the need for encrypting amounts of images in a fast manner. Yet most existing works aim to encrypt an image for each time. Although some parallel encryptions have been proposed, the speed is still far from satisfactory to proceed with the huge increasing number of images. This inspires us to consider another promising way, encrypting a batch of images parallelly for each time. We use maximum available number of threads in parallel computation for full use of processor resources. Considering the batch images as a shared resource, every thread competes with others to encrypt images in the shared resource in a preemptive manner for encryption. A classical permutation-diffusion architecture for chaos-based encryption is utilized for each thread, where logistic map and Lorenz system are used for generating keystream for permutation and diffusion, respectively. We make cryptographical analyses and perform experiments to confirm that the security is guaranteed. The results of efficiency tests demonstrate that the encryption speed is greatly improved compared with the state-of-art image encryption algorithms in parallel as well as serial modes.
       
  • Hybrid Belief Rule Base for Regional Railway Safety Assessment with Data
           and Knowledge under Uncertainty
    • Abstract: Publication date: Available online 11 January 2020Source: Information SciencesAuthor(s): Leilei Chang, Wei Dong, Jianbo Yang, Xinya Sun, Xiaobin Xu, Xiaojian Xu, Limao ZhangAbstractKeeping regional railway transportation safe is of great importance for railway system engineers and decision makers. However, there are still great challenges in modeling the complicated conditions in regional railway transportation: (1) Multiple types of data and knowledge in complicated correlations need to be analyzed, and (2) The approach must be open and accessible to decision makers so that a balanced decision can be made. To address the above challenges, a safety assessment approach using the hybrid Belief Rule Base (BRB) is proposed. In the new approach, multiple types of information are modeled under the hybrid assumption, and thus, hybrid rules are constructed to form the hybrid BRB. With this, both data and knowledge in complicated correlations can be used for the safety assessment on regional railway transportation, rather than only a single railway station or equipment component. Moreover, the assessment process remains open and accessible which provides good interpretability to stakeholders. An empirical regional railway safety assessment case is studied on the existing line and high speed line in the Cheng-Yu region located in the southwestern China. Five aspects, namely, the environment, equipment, management, passengers, and accident, are analyzed and then disintegrated into sub-factors. With the aspects and sub-factors, a comprehensive model is constructed. Case study results show that (1) the overall safety levels of the high speed line are better than the existing line, (2) the safety assessment results are consistent with the historical reports of accidents and system failures, (3) among all aspects, the environment and equipment have a more direct effect on the overall safety levels, and (4) consistency has also been found with railway accident statistics collected from Japan and Canada.
       
  • An Efficient and provable certificate-based proxy signature scheme for
           IIoT environment
    • Abstract: Publication date: Available online 10 January 2020Source: Information SciencesAuthor(s): Girraj Kumar Verma, B.B. Singh, Neeraj Kumar, Mohammad S Obaidat, Debiao He, Harendra SinghAbstractRecently, the deployment of sensors and actuators to collect and disseminate data in various applications such as e-healthcare, vehicular adhoc networks (VANETs) and smart factories has revolutionized several new communication technologies. The Internet of Things (IoT) is one of those emerging communication technologies. These revolutionary applications of IoT in industrial environment are termed as Industry 4.0 and it has vitalized the concept of Industrial IoT (IIoT). Being wireless communication, the authentication and integrity of data are the most important challenges. To mitigate these challenges, several digital signature schemes are proposed in the literature. However, due to identity-based or certificate-less construction, those schemes suffer from inborn key escrow and secret key distribution problems. To resolve such issues, the first certificate-based proxy signature (PFCBPS) scheme without pairing is proposed. The proposed PFCBPS scheme is provably secure in random oracle model (ROM). The performance comparison (in terms of computational costs of different phases and length of resulting delegation and signature) shows that the proposed PFCBPS scheme’s total computational cost is 46.69 msec. which is 52.24% of [8], 61.40% of [5], 23.33% of [20], 28% of [9] and 36.84% of [23]. Thus, it is more suitable to IIoT environment than existing competitive schemes.
       
  • Dynamic Time Series Smoothing for Symbolic Interval Data applied to
           Neuroscience
    • Abstract: Publication date: Available online 23 December 2019Source: Information SciencesAuthor(s): Diego C Nascimento, Bruno Pimentel, Renata Souza, João P. Leite, Dylan J. Edwards, Taiza E.G. Santos, Francisco LouzadaAbstractThis work aimed to appraise a multivariate time series, high-dimensionality data-set, presented as intervals using a Symbolic Analysis (SDA) approach. SDA reduces data dimensionality, considering the complexity of the model information through a set-valued (interval or multi-valued). Additionally, Dynamic Linear Models (DLM) are distinguished by modeling univariate or multivariate time series in the presence of non-stationarity, structural changes and irregular patterns. We considered neurophysiological (EEG) data associated with experimental manipulation of verticality perception in humans, using transcranial electrical stimulation. The innovation of the present work is centered on use of a dynamic linear model with SDA methodology, and SDA applications for analyzing EEG data.
       
  • Multi-state deterioration prediction for infrastructure asset: Learning
           from uncertain data, knowledge and similar groups
    • Abstract: Publication date: Available online 27 November 2019Source: Information SciencesAuthor(s): Haoyuan Zhang, D. William R. MarshAbstractInfrastructure assets such as bridges need to be inspected regularly for signs of deterioration. Although a fixed inspection interval could be used, an estimate of the rate of deterioration allows us to schedule the next inspection more cost-effectively. Our earlier work outlined a Bayesian framework that uses both data and knowledge to predict the transition between assets, which has been extended and realised in this paper for asset deterioration prediction. In the Bayesian model, censorship is modelled to incorporate the uncertainty from inspection records and prior of the parameter is used to express expert knowledge. In particular, we also suggest how the prior probabilities of the parameters of a Weibull distribution can be set in practice using expert estimates such as the maximum and average times of a transition from one state to another.Furthermore, assets with similar characteristics may deteriorate similarly. We propose to separate related assets into groups and learn deterioration between these groups. This assumption allows us to tackle the challenge of limited data further and is experimented with the deck inspection records from the National Bridge Inventory database in Wyoming. This database includes over 100 features of each bridge such as structure type and average daily traffic: we use a modified random forest to select a subset of important features to separate assets into groups. The model is extended into hierarchical Bayesian models to learn between groups with the help of hyper-parameters and an aggregated variable from the feature levels. Performance of our method is compared with other existing approaches from various aspects.
       
  • Multiattribute group decision making based on neutrality aggregation
           operators of q-rung orthopair fuzzy sets
    • Abstract: Publication date: Available online 24 November 2019Source: Information SciencesAuthor(s): Harish Garg, Shyi-Ming ChenAbstractq-rung orthopair fuzzy sets (q-ROFSs) are prominent ideas to express fuzzy data in decision-making. The q-ROFSs can dynamically adapt the area of evidence by altering the parameter q ≥ 1 based on the fluctuation degree and therefore support more innumerable possibilities. Hence, this set defeats over the existing Atanassov intuitionistic fuzzy sets (AIFSs) and Pythagorean fuzzy sets (PFS). In today’s life, there is frequently a setup concerning a neutral attitude towards the evaluation of the decision-makers. To arrange a pleasant decision throughout the method, in this paper, we illustrate innovative operational laws by uniting the features of the membership coefficients sum as well as the interaction between the membership degrees into the study for q-ROFSs. Associated with these laws, we establish some weighted averaging neutral aggregation operators (AOs) to aggregate the q-ROF erudition. Furthermore, we introduce an innovative MAGDM (“multiattribute group decision making”) process based on suggested AOs and illustrate with numerous numerical cases to verify it. A contrastive review is also administered to confirm the supremacies of the method.
       
  • Robust and blind image watermarking in DCT domain using inter-block
           coefficient correlation
    • Abstract: Publication date: May 2020Source: Information Sciences, Volume 517Author(s): Hung-Jui Ko, Cheng-Ta Huang, Gwoboa Horng, Shiuh-Jeng WANGAbstractThis study presents a robust and transparent watermarking method that exploits block-based discrete cosine transform (DCT) coefficient modification. The difference in the DCT coefficients of two blocks is calculated and modified based on the watermark bit to adjust this difference to a predefined range. The first coefficient in the upper left corner of the array basis function is known as the direct current (DC) coefficient, whereas the remainder includes the alternating current (AC) coefficients. The extent of the DCT coefficient modifications depends on the DC coefficient and median of the AC coefficients ordered by a zig-zag sequence. The robustness of the proposed method against various attacks was evaluated experimentally, and experimental results demonstrate that the proposed method possesses great robustness against various single and combined attacks.
       
  • Phrase2Vec: Phrase embedding based on parsing
    • Abstract: Publication date: May 2020Source: Information Sciences, Volume 517Author(s): Yongliang Wu, Shuliang Zhao, Wenbin LiAbstractText is one of the most common unstructured data, and usually, the most primary task in text mining is to transfer the text into a structured representation. However, the existing text representation models split the complete semantic unit and neglect the order of words, finally lead to understanding bias. In this paper, we propose a novel phrase-based text representation method that takes into account the integrity of semantic units and utilizes vectors to represent the similarity relationship between texts. First, we propose HPMBP (Hierarchical Phrase Mining Based on Parsing) which mines hierarchical phrases by parsing and uses BOP (Bag Of Phrases) to represent text. Then, we put forward three phrase embedding models, called Phrase2Vec, including Skip-Phrase, CBOP (Continuous Bag Of Phrases), and GloVeFP (Global Vectors For Phrase Representation). They learn the phrase vector with semantic similarity, further obtain the vector representation of the text. Based on Phrase2Vec, we propose PETC (Phrase Embedding based Text Classification) and PETCLU (Phrase Embedding based Text Clustering). PETC utilizes the phrase embedding to get the text vector, which is fed to a neural network for text classification. PETCLU gets the vectorization expression of text and cluster center by Phrase2Vec, furthermore extends the K-means model for text clustering. To the best of our knowledge, it is the first work that focuses on the phrase-based English text representation. Experiments show that the introduced Phrase2Vec outperforms state-of-the-art phrase embedding models in the similarity task and the analogical reasoning task on Enwiki, DBLP, and Yelp dataset. PETC is superior to the baseline text classification methods in the F1-value index by about 4%. PETCLU is also ahead of the prevalent text clustering methods in entropy and purity indicators. In summary, Phrase2Vec is a promising approach to text mining.
       
  • Leveraging Multiple Features for Document Sentiment Classification
    • Abstract: Publication date: Available online 8 January 2020Source: Information SciencesAuthor(s): Li Kong, Chuanyi Li, Jidong Ge, FeiFei Zhang, Yi Feng, Zhongjin Li, Bin LuoAbstractSentiment classification is an important research task in Natural Language Processing. To fulfill this type of classification, previous works have focused on leveraging task-specific features. However, they only notice part of the related features. Also, state-of-the-art methods based on neural networks often ignore traditional features. This paper proposes a novel text sentiment classification method that learns the representation of texts by hierarchically incorporating multiple features. More specifically, we design different representations for sentiment words according to the polarity of labeled texts and whether negation exists; we distinguish words with different part-of-speech tags; emoticons, if there are, are to optimize the word vectors obtained in the previous step; apart from word embeddings, character embeddings are also trained. We use a deep neural network to get a sentence-level representation from both word and character sequence. For documents with at least two sentences, we use a hierarchical structure and design a rule to give more weight to import sentences empirically to get a document-level representation. Experimental results on open datasets demonstrate that our method could effectively improve the sentiment classification performance compared with the basic models and state-of-the-art methods.
       
  • Efficient Two-Party Privacy-Preserving Collaborative k-means Clustering
           Protocol Supporting both Storage and Computation Outsourcing
    • Abstract: Publication date: Available online 8 January 2020Source: Information SciencesAuthor(s): Zoe L. Jiang, Ning Guo, Yabin Jin, Jiazhuo Lv, Yulin Wu, Zechao Liu, Junbin Fang, S.M. Yiu, Xuan WangAbstractNowadays, cloud computing has developed well and been applied in many kinds of areas. However, privacy is still the most challenging problem which obstructs it being applied in some privacy-sensitive fields, such as finance and government. Advanced cryptographic algorithms provide data privacy with encryption, which can also support computation on such encrypted data. However, new challenge arises when such ciphertexts come from different parties. In particular, how to execute collaboratively data mining on encrypted data coming from different parties is a key issue from cloud service point of view. This paper focuses on privacy problem on outsourced k-means clustering scheme for two parties. In particular, each party’s data are encrypted only once and then stored in cloud. The proposed privacy-preserving k-means collaborative clustering protocol is executed mainly at the cloud, with O(k(m+n)) rounds of interactions among the two parties and the cloud, where m and n represent the total numbers of records for the two parties, respectively. It is shown that the protocol is secure in the semi-honest security model and in the malicious model in which only one party is corrupted during the process of centroids re-computation. Both theoretical and experimental analysis of the proposed scheme are also provided.
       
  • T-S Fuzzy-based Sliding Mode Control Design for Discrete-time Nonlinear
           Model and Its Applications
    • Abstract: Publication date: Available online 8 January 2020Source: Information SciencesAuthor(s): Ramasamy Subramaniam, Dongran Song, Young Hoon JooAbstractThis paper investigates the Takagi-Sugeno (T-S) fuzzy based sliding-mode control (SMC) design of the discrete-time nonlinear model. By constructing a suitable fuzzy membership functions (FMFs) dependent Lyapunov function, the sufficient conditions are derived such that the resultant discrete-time T-S fuzzy model can achieve strictly (Q,S,R)−γ dissipative, where Q, S and R are known matrices with compatible dimensions satisfying Q≤0 and R=RT, and γ is a positive constant. Then, the desired control gain can be obtained by solving a set of linear matrix inequalities (LMIs). Besides that, a fuzzy SMC is designed to assure reaching condition. A modified fuzzy sliding-mode controller is also constructed to adapt input saturation. Finally, simulation results are presented to demonstrate the applicability and effectiveness of the proposed approaches.
       
  • Tripled fuzzy metric spaces and fixed point theorem
    • Abstract: Publication date: Available online 7 January 2020Source: Information SciencesAuthor(s): Jing-Feng Tian, Ming-Hu Ha, Da-Zeng TianAbstractOne of the most important topics of research in fuzzy sets is to get an appropriate notion of fuzzy metric space (FMS), in the paper we propose a new FMS–tripled fuzzy metric space (TFMS), which is a new generalization of George and Veeramani’s FMS. Then we present some related examples, topological properties, convergence of sequences, Cauchy sequence (CS) and completeness of the TFMS. Moreover, we introduce two kinds of notions of generalized fuzzy ψ-contractive (Fψ-C) mappings, and derive a fixed point theorem (FPT) on the mappings in the space.
       
  • Command Filtering-Based Adaptive Fuzzy Control for Permanent Magnet
           Synchronous Motors with Full-state Constraints
    • Abstract: Publication date: Available online 7 January 2020Source: Information SciencesAuthor(s): Mingjun Zou, Jinpeng Yu, Yumei Ma, Lin Zhao, Chong LinAbstractFocusing on the problem of position tracking control for permanent magnet synchronous motors (PMSMs) with full-state constraints, this article proposes an adaptive fuzzy control scheme based on command filtering error compensation mechanism. Firstly, the unknown nonlinear functions of PMSM drive systems are approximated by utilizing the fuzzy logic systems (FLSs). Then, the command filtering technique is employed to deal with the “explosion of complexity” problem arising from conventional backstepping scheme, and the filtering errors are reduced by the error compensation mechanism. In addition, the barrier Lyapunov functions (BLFs) are constructed to guarantee that the state variables are restricted in compact bounding sets. Finally, simulation results show the effectiveness of the proposed scheme.
       
  • A no self-edge stochastic block model and a heuristic algorithm for
           balanced anti-community detection in networks
    • Abstract: Publication date: Available online 7 January 2020Source: Information SciencesAuthor(s): Jiajing Zhu, Yongguo Liu, Hao Wu, Zhi Chen, Yun Zhang, Shangming Yang, Changhong Yang, Wen Yang, Xindong WuAbstractMany real-world networks own the characteristic of anti-community structure, i.e. disassortative structure, where nodes share no or few connections inside their groups but most of their connections outside. Detecting anti-community structure can explore negative relations among objects. However, the structures output by the existing algorithms are unbalanced, leading to no or few negative relations to be explored in some groups. Stochastic block models are promising methods for exploring disassortative structures in networks, but their results are highly dependent on the observed structure of a network. In this paper, we first improve the classic stochastic block model and propose a No sElf-edge Stochastic blOck Model (NESOM) for anti-community structure. NESOM considers the edges inside and among groups, respectively, and evolves a new objective function for evaluating anti-community structure. And then, a new heuristic algorithm NESOM-AC is proposed for balanced anti-community detection, which consists of three stages: creation of initial structure, decomposition of redundant group, and adjustment of group membership. Inspired by NESOM, we finally develop a new synthetic benchmark NESOM-Net for performance comparison. Experimental results on NESOM-Net with up to 100000 nodes and 16 real-world networks demonstrate the effectiveness of NESOM-AC in anti-community detection when compared with five state-of-the-art algorithms.
       
  • An implication based study on Łukasiewicz (Monteiro) 3-valued algebra
           and pre-rough algebra
    • Abstract: Publication date: Available online 7 January 2020Source: Information SciencesAuthor(s): Anirban Saha, Jayanta SenAbstractThis paper has unfolded from the study on rough sets, mainly from pre-rough algebra. Here we compare between implications of Łukasiewicz (Monteiro) 3-valued algebra and pre-rough algebra. This study aids in presenting an alternative axiomatization of Łukasiewicz (Monteiro) 3-valued algebra.
       
  • Scalable Revocable Identity-Based Signature over Lattices in the Standard
           Model
    • Abstract: Publication date: Available online 7 January 2020Source: Information SciencesAuthor(s): Congge Xie, Jian Weng, Jiasi Weng, Lin HouAbstractRevocable identity based signature (RIBS) is a useful cryptographic primitive, which provides a revocation mechanism to revoke misbehaving or malicious users over ID-based public key settings. In the past, many RIBS schemes have been previously proposed, but the security of all these existing schemes is based on traditional complexity assumptions, which are not secure against attacks in the quantum era. Lattice-based cryptography has many attractive features and it is all believed to be secure against attacks of quantum computing. Recently, Hung et al. proposed a RIBS with short size over lattices. However, in their scheme, it requires the private key generator (PKG) to perform linear work in the number of users and does not scale well. Moreover, their scheme is secure in the random oracle model. In this paper, we adopt the binary tree structure to present a scalable lattice-based RIBS scheme which greatly reduces the PKG’S workload associated with users from linear to logarithm. We prove that our proposed scheme is existentially unforgeable against chosen message attacks (EUF-CMA) under standard short integer solutions (SIS) assumption, in the standard model. Compared with the existing RIBS schemes over lattices, our proposed RIBS construction is secure in the standard model with scalability and meanwhile has efficient revocation mechanism with public channels.
       
  • Human Behavior Recognition from Multiview Videos
    • Abstract: Publication date: Available online 7 January 2020Source: Information SciencesAuthor(s): Yu-Ling Hsueh, Wen-Nung Lie, Guan-You GuoAbstractWith the proliferation of deep learning techniques, a significant number of applications related to home care systems have emerged recently. In particular, detecting abnormal events in a smart home environment has been extensively studied. In this paper, we adopt deep learning techniques, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, to construct deep networks to learn the long-term dependencies from videos for human behavior recognition in a multiview framework. We adopt two cameras as our sensors to efficiently overcome the problem of occlusions and contour ambiguity for improving the accuracy performance of the multiview framework. After performing a series of image preprocessing on the raw data, we obtain human silhouette images as the input to our training model. In addition, because real-world datasets are complicated for analysis, labeling data is time consuming. Therefore, we present an image clustering method based on a stacked convolutional autoencoder (SCAE), which generates clustering labels for autolabeling. Finally, we set up our experimental environment as a normal residence to collect a large dataset, and the experimental results demonstrate the novelty of our proposed models.
       
  • Intuitionistic fuzzy TOPSIS method based on CVPIFRS models: an application
           to biomedical problems
    • Abstract: Publication date: Available online 7 January 2020Source: Information SciencesAuthor(s): Li Zhang, Jianming Zhan, Yiyu YaoAbstractIn order to obtain the weights of a set of criteria by means of real-world data, an effective method based on the covering-based variable precision intuitionistic fuzzy rough set (CVPIFRS) models is presented. By combining the CVPIFRS models with the idea of TOPSIS, we propose a decision-making method to effectively settle the complex and changeable bone transplant selections, which is one of typical multi-attribute decision-making (MADM) problems. The sensitivity analysis of the proposed method shows that the approach is highly flexible and can be applied to a wide range of environments by adjusting the values of the intuitionistic fuzzy (IF) variable precision, together with the choice of different IF logical operators. Through a comparison of the proposed method and some existing MADM methods, it is shown that our method is more effective in dealing with these complex and changeable bone transplant selections issues.
       
  • A Constrained Agglomerative Clustering Approach for Unipartite and
           Bipartite Networks with Application to Credit Networks
    • Abstract: Publication date: Available online 3 January 2020Source: Information SciencesAuthor(s): Samrat Gupta, Pradeep KumarAbstractResearchers and practitioners have been interested in solving real-world problems through clustering. The clustering of nodes in networks with unipartite or bipartite structure is important to explore real-world complex networks present in nature and society. Bipartite networks form an important class of complex networks because they reveal the heterogeneity of nodes in a network. However, most extant clustering methods focus only on unipartite networks. In this work, a novel constrained agglomerative clustering method applicable to unipartite and bipartite networks has been proposed. Initially, the topology of a network is modeled according to set-theoretic principles. Subsequently, the concepts related to rough set theory and relative linkage are used to cluster the set of nodes. The utility and effectiveness of the proposed approach are demonstrated through offline experiments on unipartite and bipartite networks. A comparison against ten state-of-the-art similarity measures over two different partitional clustering algorithms reveals the effectiveness of the proposed relative linkage measure. Moreover, a comparative analysis with state-of-the-art network clustering methods reveals the viability of the proposed rough set-based constrained agglomerative clustering algorithm. Finally, the proposed method has been applied for the detection of cohesive subgroups of banks in a real bipartite network formed by mapping credit relationships between Indian firms and banks.
       
  • Key Regeneration-Free Ciphertext-Policy Attribute-Based Encryption and Its
           Application
    • Abstract: Publication date: Available online 3 January 2020Source: Information SciencesAuthor(s): Hui Cui, Robert H. Deng, Baodong Qin, Jian WengAbstractAttribute-based encryption (ABE) provides a promising solution for enabling scalable access control over encrypted data stored in the untrusted servers (e.g., cloud) due to its ability to perform data encryption and decryption defined over descriptive attributes. In order to bind different components which correspond to different attributes in a user’s attribute-based decryption key together, key randomization technique has been applied in most existing ABE schemes. This randomization method, however, also empowers a user the capability of regenerating a newly randomized decryption key over a subset of the attributes associated with the original decryption key. Because key randomization breaks the linkage between this newly generated key and the original key, a malicious user could leak the new decryption key to others without taking any responsibility for the key abuse. To solve this problem, we think of key regeneration-free ABE to disallow a user from randomizing his/her decryption key in any manner, i.e., a user can only delegate his/her decryption key in exactly the same form without any modification so that any abused or pirated key can be traced back to its original owner. Motivated by strongly unforgeable signature, we first define a security notion called strong key unforgeability, and show that ABE schemes equipped with the strong key unforgeability are immune to key regeneration. We then provide a generic transformation to convert ciphertext-policy ABE (CP-ABE) schemes of certain type to key regeneration-free CP-ABE schemes, and show how the transformation works by presenting two concrete constructions.
       
  • Granular Neural Networks: The Development of Granular Input Spaces and
           Parameters Spaces through A Hierarchical Allocation of Information
           Granularity
    • Abstract: Publication date: Available online 3 January 2020Source: Information SciencesAuthor(s): Mingli Song, Yukai JingThe issue of granular output optimization of neural networks with fixed connections within a given input space is explored. The numeric output optimization is a highly nonlinear problem if nonlinear activation functions are used; the granular output optimization becomes a even more challenging task. We solve the problem by developing an optimal hierarchical allocation of information granularity, proposing a new objective function which considers both specificity and evidence, and engaging here efficient techniques of evolutionary optimization. In contrast to the existing techniques, the hierarchical one builds a three-level hierarchy to allocate information granularity to the input space and the architecture (parameters) of the network. Granulating both the input features and the architecture at the same time return a different result with the single factor granulation. The constructed granular neural network emphasizes the abstract nature of data and the granular nature of nonlinear mapping of the architecture. Experimental studies completed for synthetic data and publicly available data sets are used to realize the algorithm.
       
  • Local Community Detection by the Nearest Nodes with Greater Centrality
    • Abstract: Publication date: Available online 2 January 2020Source: Information SciencesAuthor(s): Wenjian Luo, Nannan Lu, Li Ni, Wenjie Zhu, Weiping DingAbstractMost community detection algorithms require the global information of the networks. However, for large scale complex networks, the global information is often expensive and even impossible to obtain. Therefore, local community detection is of tremendous significance. In this paper, a new local community detection algorithm based on NGC nodes, named LCDNN, is proposed. For any node, its NGC node refers to the nearest node with greater centrality. In the LCDNN, local community C initially consists of the given node, v. Then, the remaining nodes are added to the local community one by one, and the added node should satisfy: 1) its NGC node is in C, or it is the NGC node of the center node of C; and 2) the fuzzy relation between the node and its NGC node is the largest; 3) the fuzzy relation is no less than half of the average fuzzy relation of the current local network. The experimental results on ten real-world and synthetic networks demonstrate that LCDNN is effective and highly competitive. Concurrently, LCDNN can also be extended for multiscale local community detection, and experimental results are provided to demonstrate its effectiveness.
       
  • An Evolving Approach to Data Streams Clustering Based on Typicality and
           Eccentricity Data Analytics
    • Abstract: Publication date: Available online 2 January 2020Source: Information SciencesAuthor(s): Clauber Gomes Bezerra, Bruno Sielly Jales Costa, Luiz Affonso Guedes, Plamen Parvanov AngelovAbstractIn this paper we propose an algorithm for online clustering of data streams. This algorithm is called AutoCloud and is based on the recently introduced concept of Typicality and Eccentricity Data Analytics, mainly used for anomaly detection tasks. AutoCloud is an evolving, online and recursive technique that does not need training or prior knowledge about the data set. Thus, AutoCloud is fully online, requiring no offline processing. It allows creation and merging of clusters autonomously as new data observations become available. The clusters created by AutoCloud are called data clouds, which are structures without pre-defined shape or boundaries. AutoCloud allows each data sample to belong to multiple data clouds simultaneously using fuzzy concepts. AutoCloud is also able to handle concept drift and concept evolution, which are problems that are inherent in data streams in general. Since the algorithm is recursive and online, it is suitable for applications that require a real-time response. We validate our proposal with applications to multiple well known data sets in the literature.
       
  • Novel Heterogeneous Grouping Method Based on Magic Square
    • Abstract: Publication date: Available online 2 January 2020Source: Information SciencesAuthor(s): Chun-Cheng Peng, Cheng-Jung Tsai, Ting-Yi Chang, Jen-Yuan Yeh, Meng-Chu LeeAbstractGrouping students appropriately to increase learning achievement is important in learning and teaching. Traditional grouping methods include both homogeneous and heterogeneous grouping; heterogeneous grouping has been claimed to improve students' learning achievement and learning process in both cooperative and collaborative learning. Recently, machine–learning-based grouping approaches have been proposed to produce better heterogeneous groups. One main drawback of these machine-learning-based methods is that they are highly affected by parameter settings; setting the appropriate parameters is difficult for general users. Consequently, the most adopted heterogeneous grouping methods currently are s-shape placement, random assignment, and self-grouping, as the three methods do not require additional parameter settings. Herein, a new heterogeneous grouping algorithm named MASA (magic square-based heterogeneous grouping algorithm) is proposed. As in the s-shape placement method, the only parameter required in MASA is the number of groups. Experimental analysis on 92 datasets indicated that MASA was superior to the s-shape placement, random assignment, and self-grouping methods for generating better heterogeneous groups. Additionally, MASA is an adaptive method that can generate several grouping results simultaneously, and users can select the preferred solution.
       
  • Adaptive Memory Programming for the Dynamic Bipartite Drawing Problem
    • Abstract: Publication date: Available online 2 January 2020Source: Information SciencesAuthor(s): Bo Peng, Donghao Liu, Zhipeng Lü, Rafael Martí, Junwen DingAbstractThe bipartite drawing problem is a well-known NP-hard combinatorial optimization problem with numerous applications. The aim is to minimize the number of edge crossings in a two-layer graph, in which the edges are drawn as straight lines. We consider the dynamic variant of this problem, called the dynamic bipartite drawing problem (DBDP), which consists of adding (resp. or removing) vertices and edges to (resp. or from) a given bipartite drawing, thereby obtaining a new drawing with a layout similar to that of the original drawing. To solve this problem, we propose a tabu search method that incorporates adaptive memory to search the solution space efficiently. In this study, we compare the explicit memory in the proposed method, which is called iterated solution-based tabu search (ISB-TS), with that in the previous best method on the basis of attributive memory, thereby comparing these two memory implementations. Extensive computational experiments on two sets of more than 1,000 problem instances indicate that the proposed ISB-TS is highly competitive in comparison with existing methods. Key components of the approach are analyzed to evaluate their effect on the proposed algorithm and determine which search mechanisms are better suited for this type of problems.
       
  • Graph Structured Sparse Subset Selection
    • Abstract: Publication date: Available online 2 January 2020Source: Information SciencesAuthor(s): Hyungrok Do, Myun-Seok Cheon, Seoung Bum KimAbstractWe propose a new method for variable subset selection and regression coefficient estimation in linear regression models that incorporates a graph structure of the predictor variables. The proposed method is based on the cardinality constraint that controls the number of selected variables and the graph structured subset constraint that encourages the predictor variables adjacent in the graph to be simultaneously selected or eliminated from the model. Moreover, we develop an efficient discrete projected gradient descent method to handle the NP-hardness of the problem originating from the discrete constraints. Numerical experiments on simulated and real-world data are conducted to demonstrate the usefulness and applicability of the proposed method by comparing it with existing graph regularization methods in terms of the predictive accuracy and variable selection performance. The results confirm that the proposed method outperforms the existing methods.
       
  • Clonal Selection Based Intelligent Parameter Inversion Algorithm for
           Prestack Seismic Data
    • Abstract: Publication date: Available online 2 January 2020Source: Information SciencesAuthor(s): Xuesong Yan, Pengpeng Li, Ke Tang, Liang Gao, Ling WangAbstractAmplitude variation with offset (AVO) elastic parameter inversion is an approach of oil exploration that employs seismic information, and it is a problem of non-linear optimization. When using a quasi-linear or linear approach to solve the problem, the inversion result is unreliable or inaccurate. Metaheuristic search methods, e.g., bio-inspired optimization algorithms such as genetic algorithms, are capable of handling highly non-linear optimization problems and thus provide a promising approach for oil and gas exploration. As one of the metaheuristic search approaches, the immune clone selection algorithm exhibits the property of fast convergence and strong global search capability. In this paper, the immune clone selection algorithm is used to address the problem of AVO elastic parameter inversion. This algorithm employs the specific initialization strategy of Aki as well as the approximation equation of Rechard, which is utilized in the elastic parameter inversion process to smooth the initialization parameter curve. Additionally, the genetic operation in the algorithm is improved in accordance. The results of multiple experiments demonstrate that the approach could significantly improve the inversion accuracy, and the correlation coefficient of the elastic parameters acquired via inversion is specifically high.
       
  • CAGE: Constrained deep Attributed Graph Embedding
    • Abstract: Publication date: Available online 31 December 2019Source: Information SciencesAuthor(s): Debora Nozza, Elisabetta Fersini, Enza MessinaAbstractIn this paper we deal with complex attributed graphs which can exhibit rich connectivity patterns and whose nodes are often associated with attributes, such as text or images. In order to analyze these graphs, the primary challenge is to find an effective way to represent them by preserving both structural properties and node attribute information. To create low-dimensional and meaningful embedded representations of these complex graphs, we propose a fully unsupervised model based on Deep Learning architectures, called Constrained Attributed Graph Embedding model (CAGE). The main contribution of the proposed model is the definition of a novel two-phase optimization problem that explicitly models node attributes to obtain a higher representation expressiveness while preserving the local and the global structural properties of the graph. We validated our approach on two different benchmark datasets for node classification. Experimental results demonstrate that this novel representation provides significant improvements compared to state of the art approaches, also showing higher robustness with respect to the size of the training data.
       
  • Set-Membership Filtering with Incomplete Observations
    • Abstract: Publication date: Available online 31 December 2019Source: Information SciencesAuthor(s): Yuan Wang, Jian Huang, Dongrui Wu, Zhi-Hong Guan, Yan-Wu WangAbstractThis study addresses the set-membership estimation problem for a class of discrete time-varying systems with incomplete observations. A set-membership filter is developed and a recursive algorithm is proposed to calculate the state estimate ellipsoid which contains the true value. To solve the problem that the conventional set-membership filter cannot guarantee the stability when applied to discrete time-varying systems with incomplete observations, a quantitative analysis method about incomplete observations is developed and a tight bound of the estimation error is found based on interval analysis and some bounded noise assumptions. In terms of bounded assumptions, the relationship between the bound of estimated error and the data dropout rate is obtained. If the data dropout rate is less than a maximal value, the set-membership filter is asymptotically stable. The proposed filter is applied to a two-state example to demonstrate the effectiveness of theoretical results.
       
  • Why you should stop predicting customer churn and start using uplift
           models
    • Abstract: Publication date: Available online 31 December 2019Source: Information SciencesAuthor(s): Floris Devriendt, Jeroen Berrevoets, Wouter VerbekeAbstractUplift modeling has received increasing interest in both the business analytics research community and the industry as an improved paradigm for predictive analytics for data-driven operational decision-making. The literature, however, does not provide conclusive empirical evidence that uplift modeling outperforms predictive modeling. Case studies that directly compare both approaches are lacking, and the performance of predictive models and uplift models as reported in various experimental studies cannot be compared indirectly since different evaluation measures are used to assess their performance.Therefore, in this paper, we introduce a novel evaluation metric called the maximum profit uplift (MPU) measure that allows assessing the performance in terms of the maximum potential profit that can be achieved by adopting an uplift model. This measure, developed for evaluating customer churn uplift models, extends the maximum profit measure for evaluating customer churn prediction models. While introducing the MPU measure, we describe the generally applicable liftup curve and liftup measure for evaluating uplift models as counterparts of the lift curve and lift measure that are broadly used to evaluate predictive models. These measures are subsequently applied to assess and compare the performance of customer churn prediction and uplift models in a case study that applies uplift modeling to customer retention in the financial industry. We observe that uplift models outperform predictive models and lead to improved profitability of retention campaigns.
       
  • Learning Reinforced Attentional Representation for End-to-End Visual
           Tracking
    • Abstract: Publication date: Available online 31 December 2019Source: Information SciencesAuthor(s): Peng Gao, Qiquan Zhang, Fei Wang, Liyi Xiao, Hamido Fujita, Yan ZhangAbstractAlthough numerous recent tracking approaches have made tremendous advances in the last decade, achieving high-performance visual tracking remains a challenge. In this paper, we propose an end-to-end network model to learn reinforced attentional representation for accurate target object discrimination and localization. We utilize a novel hierarchical attentional module with long short-term memory and multi-layer perceptrons to leverage both inter- and intra-frame attention to effectively facilitate visual pattern emphasis. Moreover, we incorporate a contextual attentional correlation filter into the backbone network to make our model trainable in an end-to-end fashion. Our proposed approach not only takes full advantage of informative geometries and semantics but also updates correlation filters online without fine-tuning the backbone network to enable the adaptation of variations in the target object’s appearance. Extensive experiments conducted on several popular benchmark datasets demonstrate that our proposed approach is effective and computationally efficient.
       
  • Hierarchical Optimal Control for Input-affine Nonlinear Systems Through
           the Formulation of Stackelberg Game
    • Abstract: Publication date: Available online 28 December 2019Source: Information SciencesAuthor(s): Chaoxu Mu, Ke Wang, Qichao Zhang, Dongbin ZhaoAbstractSubstantial efforts have been undertaken to explore nonzero-sum differential games. Most of these studies are devoted to devising algorithms to pursue Nash equilibrium, where all players with the same access to information will take policies synchronously. However, when it comes to hierarchical optimization and asymmetric information, Nash equilibrium is ineffective. The Stackelberg game provides us with an idea of leader-follower strategy to cope with this conundrum. The paper investigates hierarchical optimal control for continuous-time two-player input-affine systems characterized by nonlinear dynamics and quadratic cost functions. By introducing new costates, this optimization problem is formulated as a Stackelberg game in conjunction with a parametric optimization problem. Besides, the closed-loop information is available for both players. An adaptive learning algorithm is thus developed to approximately obtain the open-loop Stackelberg equilibrium while ensuring the uniform ultimate bounded stability of this closed-loop system, and two approximators structured by neural networks put this purpose into practice. Finally, two numerical examples illustrate that the proposed methodology can accurately obtain optimal solutions, and a comparative example illustrates its characteristics.
       
  • Fault Detection for Switched Systems with All Modes Unstable based on
           Interval Observer
    • Abstract: Publication date: Available online 28 December 2019Source: Information SciencesAuthor(s): Qingyu Su, Zhongxin Fan, Tong Lu, Yue Long, Jian LiAbstractThis paper concentrates on observer-based fault detection (FD) for switched systems. By the introduction of the interval observer, the error dynamic system is proposed. In light of the switching logic, exponential stability of the augmented system is firstly achieved. Then, to ensure the fault sensitivity as well as disturbance robustness, H∞/H− performance analysis is employed. One thing has to be mentioned is that we investigate the unobservable condition of (Ai; Ci). Moreover, the instability of the error dynamic system brought by the unobservability is eliminated by the switching strategy using average dwell time (ADT) method. Afterwards, sufficient conditions guaranteing the H∞/H− performance level are obtained, which is divided into disturbance attenuation and fault sensitivity analysis. Finally, examples demonstrating the effectiveness of the provided method are provided.
       
  • T+MultiDim+Model&rft.title=Information+Sciences&rft.issn=0020-0255&rft.date=&rft.volume=">Enabling Instant- and Interval-based Semantics in Multidimensional Data
           Models: the T+MultiDim Model
    • Abstract: Publication date: Available online 28 December 2019Source: Information SciencesAuthor(s): Carlo Combi, Barbara Oliboni, Giuseppe Pozzi, Alberto Sabaini, Esteban ZimányiAbstractTime is a vital facet of every human activity. Data warehouses, which are huge repositories of historical information, must provide analysts with rich mechanisms for managing the temporal aspects of information. In this paper, we (i) propose T+MultiDim, a multidimensional conceptual data model enabling both instant- and interval-based semantics over temporal dimensions, and (ii) provide suitable OLAP (On-Line Analytical Processing) operators for querying temporal information. T+MultiDim allows one to design typical concepts of a data warehouse including temporal dimensions, and provides one with the new possibility of conceptually connecting different temporal dimensions for exploiting temporally aggregated data. The proposed approach allows one to specify and to evaluate powerful OLAP queries over information from data warehouses. In particular, we define a set of OLAP operators to deal with interval-based temporal data. Such operators allow the user to derive new measure values associated to different intervals/instants, according to different temporal semantics. Moreover, we propose and discuss through examples from the healthcare domain the SQL specification of all the temporal OLAP operators we define.
       
  • Quantifying consensus of rankings based on q-support patterns
    • Abstract: Publication date: Available online 28 December 2019Source: Information SciencesAuthor(s): Zhengui Xue, Zhiwei Lin, Hui Wang, Sally McCleanAbstractRankings, representing preferences over a set of candidates, are widely used in many applications, e.g., group decision making and information retrieval. Rankings may be obtained by different agents (humans or systems). It is often necessary to evaluate consensus of obtained rankings from multiple agents, as a measure of consensus provides insights into the rankings. Moreover, a consensus measure could provide a quantitative basis for comparing groups and for improving a ranking system. Existing studies on consensus measurement are insufficient, since they did not evaluate consensus among most rankings or consensus with respect to specific preference patterns. In this paper, a novel consensus quantifying approach, without the use of correlation or distance functions as in existing studies of consensus, is proposed based on the concept of q-support patterns, which represent the commonality embedded in a set of rankings. A pattern is regarded as a q-support pattern if it is included by at least q rankings in the ranking set. A method for detecting outliers in a set of rankings is naturally derived from the proposed consensus quantifying approach. Experimental studies are conducted to demonstrate the effectiveness of the proposed approach.
       
  • Statistical Learning and Estimation of Piano Fingering
    • Abstract: Publication date: Available online 27 December 2019Source: Information SciencesAuthor(s): Eita Nakamura, Yasuyuki Saito, Kazuyoshi YoshiiAbstractAutomatic estimation of piano fingering is important for understanding the computational process of music performance and applicable to performance assistance and education systems. While a natural way to formulate the quality of fingerings is to construct models of the constraints/costs of performance, it is generally difficult to find appropriate parameter values for these models. Here we study an alternative data-driven approach based on statistical modeling in which the appropriateness of a given fingering is described by probabilities. Specifically, we construct two types of hidden Markov models (HMMs) and their higher-order extensions. We also study deep neural network (DNN)-based methods for comparison. Using a newly released dataset of fingering annotations, we conduct systematic evaluations of these models as well as a representative constraint-based method. We find that the methods based on high-order HMMs outperform the other methods in terms of estimation accuracies. We also quantitatively study individual difference of fingering and propose evaluation measures that can be used with multiple ground truth data. We conclude that the HMM-based methods are currently state of the art and generate acceptable fingerings in most parts and that they have certain limitations such as ignorance of phrase boundaries and interdependence of the two hands.
       
  • Interval Multiplicative Pairwise Comparison Matrix: Consistency,
           Indeterminacy and Normality
    • Abstract: Publication date: Available online 27 December 2019Source: Information SciencesAuthor(s): Ting KuoAbstractTo manifest human judgments, a long-established method called Pairwise Comparison (PC) has been successfully applied in the Analytic Hierarchy Process (AHP). In practice, human judgments are often made with uncertainty, and can be characterized by an Interval Multiplicative Pairwise Comparison Matrix (IMPCM). Since consistency is a key issue that has plagued decision makers and researchers for a long time, it is useful to propose a transformation that can effectively convert an inconsistent IMPCM into a consistent one, especially in group decision-making. However, a consistent IMPCM is not sufficient to be acceptable, indeterminacy should also be considered. Moreover, the interval priority weights should be normalized. To consider consistency, indeterminacy, and normality simultaneously, we put forward a new definition of acceptable IMPCM. To obtain such an acceptable IMPCM, we propose a theorem of consistency, a consistent transformation, and a normalized prioritization scheme. As a result, the proposed methods guarantee an inconsistent IMPCM can be directly converted into an acceptable IMPCM. Five theorems are proved to corroborate the proposed methods. A numerical example is presented to illustrate the validity and superiority of the proposed methods. Finally, discussion and conclusions are given.
       
  • Ordinal sums of triangular norms on bounded lattices
    • Abstract: Publication date: Available online 26 December 2019Source: Information SciencesAuthor(s): Ümit Ertuğrul, Merve YeşilyurtAbstractIn this paper, we deal with the problem of ordinal sum construction for t-norms on subintervals of a given bounded lattice L to obtain a t-norm on L without any additional requirements. Some illustrative examples are added to emphasize the difference of our method from some existing methods in the literature. Finally, we generalize our construction method to ensure its applicability for a greater number of t-norms on the subintervals.
       
  • Distributed and Adaptive Triggering Control for Networked Agents with
           Linear Dynamics
    • Abstract: Publication date: Available online 26 December 2019Source: Information SciencesAuthor(s): Na Huang, Zhiyong Sun, Brian D.O. Anderson, Zhisheng DuanAbstractThis paper proposes distributed event-triggered schemes for achieving state consensus for multi-agent linear systems. For each agent modeled by a linear control system in Rn, a positive signal is embedded in its event function, with the aim of guaranteeing an asymptotic convergence to state consensus for networked linear systems interacted in an undirected and connected graph, and with Zeno triggering excluded forall the agents. The proposed distributed event-based consensus algorithm allows each agent to update its own control at its own triggering times instead of using continuous updates, which thereby avoids complicated computation steps involving data fusion and matrix exponential calculations as used in several event-based control schemes reported in the literature. We further propose a totally distributed and adaptive event-based algorithm, in the sense that each agent utilizes only local measurements with respect to its neighboring agents in its event detection and control update. In this framework, the proposed algorithm is independent of any global network information such as Laplacian matrix eigenvalues associated with the underlying interaction graph. A positive L1 signal function is included in the adaptive event-based algorithm to guarantee asymptotic consensus convergence and Zeno-free triggering for all the agents. Simulations are provided to validate the performance and superiority of the developed event-based consensus strategies.
       
  • Cube-based Incremental Outlier Detection for Streaming Computing
    • Abstract: Publication date: Available online 26 December 2019Source: Information SciencesAuthor(s): Jianhua Gao, Weixing Ji, Lulu Zhang, Anmin Li, Yizhuo Wang, Zongyu ZhangAbstractOutlier detection is one of the most critical and challenging tasks of data mining. It aims to find patterns in data that do not conform to expected behavior. Data streams in streaming computing are huge in nature and arrive continuously with changing distribution, which imposes new challenges for outlier detection algorithms in time and space efficiency. Incremental local outlier factor (ILOF) detection dynamically updates the profiles of data points, while the arrival of consecutive and massive volume data points in a streaming manner causes high local data density and leads to expensive time and space overheads. Our work is motivated by its deficiencies, and in this paper we propose a cube-based outlier detection algorithm (CB-ILOF). The data space of streaming data is divided into multiple cubes, then the outlier detection of data points is transferred into the outlier detection of cubes, which significantly reduces time and memory overheads. We also present a performance evaluation on 5 datasets. Experimental results show the superiority of the CB-ILOF over the ILOF on accuracy, memory usage, and execution time..
       
  • Simplified Optimized Control Using Reinforcement Learning Algorithm for a
           Class of Stochastic Nonlinear Systems
    • Abstract: Publication date: Available online 24 December 2019Source: Information SciencesAuthor(s): Guoxing Wen, C.L. Philip Chen, Wei Nian LiAbstractIn this work, a reinforcement learning (RL) based optimized control approach is developed by implementing tracking control for a class of stochastic nonlinear systems with unknown dynamic. The RL is constructed in identifier-actor-critic architecture, where the identifier aims for determining the stochastic system in mean square, the actor aims for executing the control action and the critic aims for evaluating the control performance. In almost all of the published RL-based optimal control, since both actor and critic updating laws are yielded on the basis of implementing gradient descent method to the square of Bellman residual error, these methods are very complex and are performed difficultly. By contrast, the proposed optimized control is obviously simple because the RL algorithm is derived based on the negative gradient of a simple positive function. Furthermore, the proposed approach can remove the assumption of persistence excitation, which is required for most RL based adaptive optimal control. Finally, based on the adaptive identifier, the system stability is proven by using the quadratic Lyapunov function rather than quartic Lyapunov function, which is usually required for most stochastic systems. Simulation further demonstrates that the optimized stochastic approach can achieve the desired control objective.
       
  • Manifold Learning for Efficient Gravitational Search Algorithm
    • Abstract: Publication date: Available online 24 December 2019Source: Information SciencesAuthor(s): Chen Giladi, Avishai SintovAbstractMetaheuristic algorithms provide a practical tool for optimization in a high-dimensional search space. Some mimic phenomenons of nature such as swarms and flocks. Prominent one is the Gravitational Search Algorithm (GSA) inspired by Newton’s law of gravity to manipulate agents modeled as point masses in the search space. The law of gravity states that interaction forces are inversely proportional to the squared distance in the Euclidean space between two objects. In this paper we claim that when the set of solutions lies in a lower-dimensional manifold, the Euclidean distance would yield unfitted forces and bias in the results, thus causing suboptimal and slower convergence. We propose to modify the algorithm and utilize geodesic distances gained through manifold learning via diffusion maps. In addition, we incorporate elitism by storing exploration data. We show the high performance of this approach in terms of the final solution value and the rate of convergence compared to other meta-heuristic algorithms including the original GSA. In this paper we also provide a comparative analysis of the state-of-the-art optimization algorithms on a large set of standard benchmark functions.
       
 
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