-
Hyperspectral-cube-based mobile face recognition: A comprehensive review Inform. Fusion (IF 13.669) Pub Date : 2021-04-12 Xianyi Zhang, Haitao Zhao
-
Learning disentangled user representation with multi-view information fusion on social networks Inform. Fusion (IF 13.669) Pub Date : 2021-04-03 Wenyi Tang, Bei Hui, Ling Tian, Guangchun Luo, Zaobo He, Zhipeng Cai
User representation learning is one prominent and critical task of user analysis on social networks, which derives conceptual user representations to improve the inference of user intentions and behaviors. Previous efforts have shown its substantial value in multifarious real-world applications, including product recommendation, textual content modeling, link prediction, and many more. However, existing
-
Trust-aware recommendation based on heterogeneous multi-relational graphs fusion Inform. Fusion (IF 13.669) Pub Date : 2021-04-08 Jie Guo, Yan Zhou, Peng Zhang, Bin Song, Chen Chen
Users’ trust relations have a significant influence on their choice towards different products. However, few recommendation or prediction algorithms both consider users’ social trust relations and item-related knowledge, which makes them difficult to cope with cold start and the data sparsity problems. In this paper, we propose a novel trust-ware recommendation method based on heterogeneous multi-relational
-
A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance Inform. Fusion (IF 13.669) Pub Date : 2021-03-24 Haidong Shao, Jing Lin, Liangwei Zhang, Diego Galar, Uday Kumar
Collaborative fault diagnosis can be facilitated by multisensory fusion technologies, as these can give more reliable results with a more complete data set. Although deep learning approaches have been developed to overcome the problem of relying on subjective experience in conventional fault diagnosis, there are two remaining obstacles to collaborative efficiency: integration of multisensory data and
-
SPICE-IT: Smart COVID-19 pandemic controlled eradication over NDN-IoT Inform. Fusion (IF 13.669) Pub Date : 2021-03-27 Muhammad Toaha Raza Khan, Malik Muhammad Saad, Muhammad Ashar Tariq, Junaid Akram, Dongkyun Kim
Internet of things (IoT) application in e-health can play a vital role in countering rapidly spreading diseases that can effectively manage health emergency scenarios like pandemics. Efficient disease control also requires monitoring of Standard operating procedure (SOP) follow-up of the population in the disease-prone area with a cost-effective reporting and responding mechanism to register any violation
-
A joint introduction to Gaussian Processes and Relevance Vector Machines with connections to Kalman filtering and other kernel smoothers Inform. Fusion (IF 13.669) Pub Date : 2021-03-31 Luca Martino, Jesse Read
The expressive power of Bayesian kernel-based methods has led them to become an important tool across many different facets of artificial intelligence, and useful to a plethora of modern application domains, providing both power and interpretability via uncertainty analysis. This article introduces and discusses two methods which straddle the areas of probabilistic Bayesian schemes and kernel methods
-
Rotation Forest for Big Data Inform. Fusion (IF 13.669) Pub Date : 2021-03-27 Mario Juez-Gil, Álvar Arnaiz-González, Juan J. Rodríguez, Carlos López-Nozal, César García-Osorio
The Rotation Forest classifier is a successful ensemble method for a wide variety of data mining applications. However, the way in which Rotation Forest transforms the feature space through PCA, although powerful, penalizes training and prediction times, making it unfeasible for Big Data. In this paper, a MapReduce Rotation Forest and its implementation under the Spark framework are presented. The
-
An overview of air quality analysis by big data techniques: Monitoring, forecasting, and traceability Inform. Fusion (IF 13.669) Pub Date : 2021-04-03 Wei Huang, Tianrui Li, Jia Liu, Peng Xie, Shengdong Du, Fei Teng
With the rapid development of economy and the frequent occurrence of air pollution incidents, the problem of air pollution has become a hot issue of concern to the whole people. The air quality big data is generally characterized by multi-source heterogeneity, dynamic mutability, and spatial–temporal correlation, which usually uses big data technology for air quality analysis after data fusion. In
-
Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion Inform. Fusion (IF 13.669) Pub Date : 2021-03-27 Francesco Piccialli, Fabio Giampaolo, Edoardo Prezioso, David Camacho, Giovanni Acampora
Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context
-
Multimodal spatio-temporal-spectral fusion for deep learning applications in physiological time series processing: A case study in monitoring the depth of anesthesia Inform. Fusion (IF 13.669) Pub Date : 2021-03-23 Nooshin Bahador, Jarno Jokelainen, Seppo Mustola, Jukka Kortelainen
Physiological signals processing brings challenges including dimensionality (due to the number of channels), heterogeneity (due to the different range of values) and multimodality (due to the different sources). In this regard, the current study intended, first, to use time-frequency ridge mapping in exploring the use of fused information from joint EEG-ECG recordings in tracking the transition between
-
LPPTE: A lightweight privacy-preserving trust evaluation scheme for facilitating distributed data fusion in cooperative vehicular safety applications Inform. Fusion (IF 13.669) Pub Date : 2021-03-27 Zhiquan Liu, Jianfeng Ma, Jian Weng, Feiran Huang, Yongdong Wu, Linfeng Wei, Yuxian Li
Vehicular networks have tremendous potential to improve road safety, traffic efficiency, and driving comfort, where cooperative vehicular safety applications are a significant branch. In cooperative vehicular safety applications, through the distributed data fusion for large amounts of data from multiple nearby vehicles, each vehicle can intelligently perceive the surrounding conditions beyond the
-
Multi-source brain computing with systematic fusion for smart health Inform. Fusion (IF 13.669) Pub Date : 2021-03-27 Hongzhi Kuai, Ning Zhong, Jianhui Chen, Yang Yang, Xiaofei Zhang, Peipeng Liang, Kazuyuki Imamura, Lianfang Ma, Haiyuan Wang
-
Fusing functional connectivity with network nodal information for sparse network pattern learning of functional brain networks Inform. Fusion (IF 13.669) Pub Date : 2021-03-27 Xiaofeng Zhu, Hongming Li, Heng Tao Shen, Zheng Zhang, Yanli Ji, Yong Fan
Sparse learning methods have been powerful tools for learning compact representations of functional brain networks consisting of a set of brain network nodes and a connectivity matrix measuring functional coherence between the nodes. However, these tools typically focus on the functional connectivity measures alone, ignoring the brain network nodal information that is complementary to the functional
-
On the aggregation of compositional data Inform. Fusion (IF 13.669) Pub Date : 2021-03-10 Raúl Pérez-Fernández, Marek Gagolewski, Bernard De Baets
Compositional data naturally appear in many fields of application. For instance, in chemistry, the relative contributions of different chemical substances to a product are typically described in terms of a compositional data vector. Although the aggregation of compositional data frequently arises in practice, the functions formalizing this process do not fit the standard order-based aggregation framework
-
A distributed particle-PHD filter using arithmetic-average fusion of Gaussian mixture parameters Inform. Fusion (IF 13.669) Pub Date : 2021-02-27 Tiancheng Li, Franz Hlawatsch
We propose a particle-based distributed PHD filter for tracking the states of an unknown, time-varying number of targets. To reduce communication, the local PHD filters at neighboring sensors communicate Gaussian mixture (GM) parameters. In contrast to most existing distributed PHD filters, our filter employs an “arithmetic average” fusion. For particles–GM conversion, we use a method that avoids particle
-
RFN-Nest: An end-to-end residual fusion network for infrared and visible images Inform. Fusion (IF 13.669) Pub Date : 2021-03-01 Hui Li, Xiao-Jun Wu, Josef Kittler
In the image fusion field, the design of deep learning-based fusion methods is far from routine. It is invariably fusion-task specific and requires a careful consideration. The most difficult part of the design is to choose an appropriate strategy to generate the fused image for a specific task in hand. Thus, devising learnable fusion strategy is a very challenging problem in the community of image
-
The fractal dimension of complex networks: A review Inform. Fusion (IF 13.669) Pub Date : 2021-02-17 Tao Wen, Kang Hao Cheong
The fractal property is one of the most important properties in complex networks. It describes the power law relationship between characteristics of the box and the box size. There are numerous research studies focusing on the fractal property in networks through different dimensions. In order to study the problems across various disciplines, fractal dimension and local dimension are proposed to study
-
Incremental learning for exudate and hemorrhage segmentation on fundus images Inform. Fusion (IF 13.669) Pub Date : 2021-03-10 Wanji He, Xin Wang, Lin Wang, Yelin Huang, Zhiwen Yang, Xuan Yao, Xin Zhao, Huimin Lu, Zongyuan Ge
Deep-learning-based segmentation methods have shown great success across many medical image applications. However, the custom training paradigms suffer from a well-known constraint of the requirement of pixel-wise annotations, which is labor-intensive, especially when they are required to learn new classes incrementally. Contemporary incremental learning focuses on dealing with catastrophic forgetting
-
Risk Prediction of Diabetes: Big data mining with fusion of multifarious physical examination indicators Inform. Fusion (IF 13.669) Pub Date : 2021-03-06 Hui Yang, Yamei Luo, Xiaolei Ren, Ming Wu, Xiaolin He, Bowen Peng, Kejun Deng, Dan Yan, Hua Tang, Hao Lin
Diabetes is a global epidemic. Long-term exposure to hyperglycemia can cause chronic damage to various tissues. Thus, early diagnosis of diabetes is crucial. In this study, we designed a computational system to predict diabetes risk by fusing multifarious types of physical examination data. We collected 1,507,563 physical examination data of healthy people and diabetes patients, as well as 387,076
-
Multimodal feature-wise co-attention method for visual question answering Inform. Fusion (IF 13.669) Pub Date : 2021-03-01 Sheng Zhang, Min Chen, Jincai Chen, Fuhao Zou, Yuan-Fang Li, Ping Lu
VQA attracts lots of researchers in recent years. It could be potentially applied to the remote consultation of COVID-19. Attention mechanisms provide an effective way of utilizing visual and question information selectively in visual question and answering (VQA). The attention methods of existing VQA models generally focus on spatial dimension. In other words, the attention is modeled as spatial probabilities
-
An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion Inform. Fusion (IF 13.669) Pub Date : 2021-03-01 Fang Hu, Mingfang Huang, Jing Sun, Xiong Zhang, Jifen Liu
Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning
-
A review of multimodal image matching: Methods and applications Inform. Fusion (IF 13.669) Pub Date : 2021-03-01 Xingyu Jiang, Jiayi Ma, Guobao Xiao, Zhenfeng Shao, Xiaojie Guo
Multimodal image matching, which refers to identifying and then corresponding the same or similar structure/content from two or more images that are of significant modalities or nonlinear appearance difference, is a fundamental and critical problem in a wide range of applications, including medical, remote sensing and computer vision. An increasing number and diversity of methods have been proposed
-
A trusted consensus fusion scheme for decentralized collaborated learning in massive IoT domain Inform. Fusion (IF 13.669) Pub Date : 2021-02-27 Ke Wang, Chien-Ming Chen, Zuodong Liang, Mohammad Mehedi Hassan, Giuseppe M.L. Sarné, Lidia Fotia, Giancarlo Fortino
In a massive IoT systems, large amount of data are collected and stored in clouds, edge devices, and terminals, but the data are mostly isolated. For many new demands of various intelligent applications, self-organized collaborated learning on those data to achieve group decisions has been a new trend. However, in order to reach the goal of group decisions, trust problems on data fusion and model fusion
-
Optimizing consensus reaching in the hybrid opinion dynamics in a social network• Inform. Fusion (IF 13.669) Pub Date : 2021-02-25 Yi Liu, Haiming Liang, Lei Gao, Zhaoxia Guo
Hybrid opinion dynamics which involves two types of individuals (i.e., leaders and followers) communicate in real time and share opinions and knowledge have been widely used in diverse applications. In real applications of hybrid opinion dynamics, one of the main demands is how to manage a consensus among individuals. This paper aims at proposing a novel consensus reaching strategy for the hybrid opinion
-
COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images Inform. Fusion (IF 13.669) Pub Date : 2021-02-25 Ghulam Muhammad, M. Shamim Hossain
COVID-19 or related viral pandemics should be detected and managed without hesitation, since the virus spreads very rapidly. Often with insufficient human and electronic resources, patients need to be checked from stable patients using vital signs, radiographic photographs, or ultrasound images. Vital signs do not often offer the right outcome, and radiographic photos have a variety of other problems
-
Image synthesis with adversarial networks: A comprehensive survey and case studies Inform. Fusion (IF 13.669) Pub Date : 2021-02-27 Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, Huiyu Zhou, Ruili Wang, M. Emre Celebi, Jie Yang
Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, transforming an object or person to a desired shape become a well-studied research in the GANs. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. However, there is a
-
Image fusion based on generative adversarial network consistent with perception Inform. Fusion (IF 13.669) Pub Date : 2021-02-27 Yu Fu, Xiao-Jun Wu, Tariq Durrani
Deep learning is a rapidly developing approach in the field of infrared and visible image fusion. In this context, the use of dense blocks in deep networks significantly improves the utilization of shallow information, and the combination of the Generative Adversarial Network (GAN) also improves the fusion performance of two source images. We propose a new method based on dense blocks and GANs , and
-
Fairness Concern: An Equilibrium Mechanism for Consensus-Reaching Game in Group Decision-Making Inform. Fusion (IF 13.669) Pub Date : 2021-02-25 Fuying Jing, Xiangrui Chao
For many group decision-making (GDM) issues, such as water-resource allocation, urban resettlement, and traffic-route planning, the benefits of the decision makers (DMs) are closely related to the collective decision-making result. In fact, the consensus-reaching process is a game between the decision makers and the moderator. The fairness concern in GDM impacts DMs’ preference modification and influences
-
Benchmarking and comparing multi-exposure image fusion algorithms Inform. Fusion (IF 13.669) Pub Date : 2021-02-22 Xingchen Zhang
Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to multi-exposure image fusion. However, although much efforts have been made on developing MEF algorithms, the lack of benchmarking study makes it difficult to perform fair and comprehensive
-
Network traffic classification for data fusion: A survey Inform. Fusion (IF 13.669) Pub Date : 2021-02-12 Jingjing Zhao, Xuyang Jing, Zheng Yan, Witold Pedrycz
Traffic classification groups similar or related traffic data, which is one main stream technique of data fusion in the field of network management and security. With the rapid growth of network users and the emergence of new networking services, network traffic classification has attracted increasing attention. Many new traffic classification techniques have been developed and widely applied. However
-
On learning effective ensembles of deep neural networks for intrusion detection Inform. Fusion (IF 13.669) Pub Date : 2021-02-12 F. Folino, G. Folino, M. Guarascio, F.S. Pisani, L. Pontieri
Classification-oriented Machine Learning methods are a precious tool, in modern Intrusion Detection Systems (IDSs), for discriminating between suspected intrusion attacks and normal behaviors. Many recent proposals in this field leveraged Deep Neural Network (DNN) methods, capable of learning effective hierarchical data representations automatically. However, many of these solutions were validated
-
DMRFNet: Deep Multimodal Reasoning and Fusion for Visual Question Answering and explanation generation Inform. Fusion (IF 13.669) Pub Date : 2021-02-12 Weifeng Zhang, Jing Yu, Wenhong Zhao, Chuan Ran
Visual Question Answering (VQA), which aims to answer questions in natural language according to the content of image, has attracted extensive attention from artificial intelligence community. Multimodal reasoning and fusion is a central component in recent VQA models. However, most existing VQA models are still insufficient to reason and fuse clues from multiple modalities. Furthermore, they are lack
-
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects Inform. Fusion (IF 13.669) Pub Date : 2021-02-12 Yassine Himeur, Abdullah Alsalemi, Ayman Al-Kababji, Faycal Bensaali, Abbes Amira, Christos Sardianos, George Dimitrakopoulos, Iraklis Varlamis
Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems’ performance
-
Two-stage stochastic minimum cost consensus models with asymmetric adjustment costs Inform. Fusion (IF 13.669) Pub Date : 2021-02-12 Huanhuan Li, Ying Ji, Zaiwu Gong, Shaojian Qu
When dealing with consensus cost problems with asymmetric adjustment costs, the uncertain scenarios with certain probabilities which are becoming a serious problem decision-makers have to face. However, existing optimization-based consensus models have failed to consider uncertain factors that could influence the final consensus and total consensus cost. In order to better deal with these issues, it
-
ANFIS fusion algorithm for eye movement recognition via soft multi-functional electronic skin Inform. Fusion (IF 13.669) Pub Date : 2021-02-12 Wentao Dong, Lin Yang, Raffaele Gravina, Giancarlo Fortino
Eye movement detection has attracted increasing attention in the fields of safety driving, eye motion tracking, psychological assessment and telemedicine. Soft multi-functional electronic skin (SMFES) is designed to collect electrooculogram (EOG), skin temperature and sweat signals simultaneously for eye movement detection. Serpentine structure is adopted to ensure the stretchability of SMFES for satisfying
-
Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis Inform. Fusion (IF 13.669) Pub Date : 2021-02-06 Weiwei Zhang, Guang Yang, Nan Zhang, Lei Xu, Xiaoqing Wang, Yanping Zhang, Heye Zhang, Javier Del Ser, Victor Hugo C. de Albuquerque
-
An infrared and visible image fusion method based on multi-scale transformation and norm optimization Inform. Fusion (IF 13.669) Pub Date : 2021-02-09 Guofa Li, Yongjie Lin, Xingda Qu
In this paper, we propose a new infrared and visible image fusion method based on multi-scale transformation and norm optimization. In this method, a new loss function is designed with contrast fidelity (L2 norm) and sparse constraint (L1 norm), and the split Bregman method is used to optimize the loss function to obtain pre-fusion images. The final fused base layer is obtained by using a multi-level
-
Ontology Integration: Approaches and Challenging Issues Inform. Fusion (IF 13.669) Pub Date : 2021-01-23 Inès Osman, Sadok Ben Yahia, Gayo Diallo
In recent years, the decentralized development of ontologies has led to the generation of multiple ontologies of overlapping knowledge. This heterogeneity problem can be tackled by integrating existing ontologies to build a single coherent one. Ontology integration has been investigated during the last two decades, but it is still a challenging task. In this article, we provide a comprehensive survey
-
Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI Inform. Fusion (IF 13.669) Pub Date : 2021-01-27 Andreas Holzinger, Bernd Malle, Anna Saranti, Bastian Pfeifer
-
A multimodal-Siamese Neural Network (mSNN) for person verification using signatures and EEG Inform. Fusion (IF 13.669) Pub Date : 2021-01-19 Debashis Das Chakladar, Pradeep Kumar, Partha Pratim Roy, Debi Prosad Dogra, Erik Scheme, Victor Chang
Signatures have long been considered to be one of the most accepted and practical means of user verification, despite being vulnerable to skilled forgers. In contrast, EEG signals have more recently been shown to be more difficult to replicate, and to provide better biometric information in response to known a stimulus. In this paper, we propose combining these two biometric traits using a multimodal
-
A fusion method for multi-valued data Inform. Fusion (IF 13.669) Pub Date : 2021-01-15 Martin Papčo, Iosu Rodríguez-Martínez, Javier Fumanal-Idocin, Abdulrahman H. Altalhi, Humberto Bustince
In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data. Our objective is both to improve the results obtained by other methods that try to select the best aggregation function for a particular set of data, such as penalty functions, and to reduce the temporal complexity required by such approaches. We discuss how this
-
Performance assessment of a system for reasoning under uncertainty Inform. Fusion (IF 13.669) Pub Date : 2021-01-15 Branko Ristic, Christopher Gilliam, Marion Byrne
From the early developments of machines for reasoning and decision making in higher-level information fusion, there was a need for a systematic and reliable evaluation of their performance. Performance evaluation is important for comparison and assessment of alternative solutions to real-world problems. In this paper we focus on one aspect of performance assessment for reasoning under uncertainty:
-
Phonetic-enriched text representation for Chinese sentiment analysis with reinforcement learning Inform. Fusion (IF 13.669) Pub Date : 2021-01-14 Haiyun Peng, Yukun Ma, Soujanya Poria, Yang Li, Erik Cambria
The Chinese pronunciation system offers two characteristics that distinguish it from other languages: deep phonemic orthography and intonation variations. In this paper, we hypothesize that these two important properties can play a major role in Chinese sentiment analysis. In particular, we propose two effective features to encode phonetic information and, hence, fuse it with textual information. With
-
SeDID: An SGX-enabled decentralized intrusion detection framework for network trust evaluation Inform. Fusion (IF 13.669) Pub Date : 2021-01-13 Gao Liu, Zheng Yan, Wei Feng, Xuyang Jing, Yaxing Chen, Mohammed Atiquzzaman
In order to evaluate network trust, different intrusion detection methods have been proposed. However, it is difficult for a single detection node to collect massive data and perform detection and evaluation in a large-scale network. In addition, disclosure of security-related data and detection pattern might weaken data provision incentives due to privacy concern, which could result in deliberately
-
Multi-body sensor data fusion to evaluate the hippotherapy for motor ability improvement in children with cerebral palsy Inform. Fusion (IF 13.669) Pub Date : 2021-01-16 Jie Li, Zhelong Wang, Sen Qiu, Hongyu Zhao, Jiaxin Wang, Xin Shi, Bing Liang, Giancarlo Fortino
Hippotherapy is a new rehabilitation therapy for children with cerebral palsy (CP). Although it has been proved to be effective in clinical research, a quantitative evaluation of such results is still lacking in previous studies. In this research, one method for evaluating the effectiveness of hippotherapy based on body sensor network (BSN) is proposed. The method adopts distributed magnetic, angular
-
A spatial-channel progressive fusion ResNet for remote sensing classification Inform. Fusion (IF 13.669) Pub Date : 2020-12-28 Hao Zhu, Mengru Ma, Wenping Ma, Licheng Jiao, Shikuan Hong, Jianchao Shen, Biao Hou
In recent years, the panchromatic (PAN) and the multispectral (MS) remote sensing images classification has become a research hotspot. In this paper, we propose a spatial-channel progressive fusion residual network (SCPF-ResNet) for multi-resolution remote sensing classification. Firstly, for the inputs of the proposed network, the interactive data fusion strategy (IDFS) combines generalized-intensity-hue-saturation
-
Enhancing the security of blockchain-based software defined networking through trust-based traffic fusion and filtration Inform. Fusion (IF 13.669) Pub Date : 2020-12-28 Weizhi Meng, Wenjuan Li, Jianying Zhou
With the rapid development of Internet-of-Things (IoT), more smart devices can be connected to the Internet, resulting in a dramatic increase of data transmission and communication. Software-Defined Networking (SDN), which separates the control planes and data planes, is considered as a promising solution to provide the scale and versatility necessary for IoT. However, SDN still suffers from several
-
DeepOption: A novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods Inform. Fusion (IF 13.669) Pub Date : 2020-12-29 Ji Hyun Jang, Jisang Yoon, Jungeun Kim, Jinmo Gu, Ha Young Kim
The remarkable performance of deep learning is based on its ability to learn high-level features by processing large amounts of data. This exceptionally superior performance has attracted the attention of researchers studying option pricing. However, option data are more expensive and less accessible than other types of data and are imbalanced because of the liquidity of options. This motivated us
-
Linear uncertain extensions of the minimum cost consensus model based on uncertain distance and consensus utility Inform. Fusion (IF 13.669) Pub Date : 2020-12-24 Weiwei Guo, Zaiwu Gong, Xiaoxia Xu, Ondrej Krejcar, Enrique Herrera-Viedma
Uncertainty theory adopts the belief degree and uncertainty distribution to ensure good alignment with a decision-maker’s uncertain preferences, making the final decisions obtained from the consensus-reaching process closer to the actual decision-making scenarios. Under the constraints of the uncertain distance measure and consensus utility, this article explores the minimum-cost consensus model under
-
An integrated framework of learning and evidential reasoning for user profiling using short texts Inform. Fusion (IF 13.669) Pub Date : 2020-12-24 Duc-Vinh Vo, Jessada Karnjana, Van-Nam Huynh
Inferring user profiles based on texts created by users on social networks has a variety of applications in recommender systems such as job offering, item recommendation, and targeted advertisement. The problem becomes more challenging when working with short texts like tweets on Twitter, or posts on Facebook. This work aims at proposing an integrated framework based on Dempster–Shafer theory of evidence
-
A selection framework of sensor combination feature subset for human motion phase segmentation Inform. Fusion (IF 13.669) Pub Date : 2020-12-24 Jiaxin Wang, Zhelong Wang, Sen Qiu, Jian Xu, Hongyu Zhao, Giancarlo Fortino, Masood Habib
Motion phase plays an important role in the spatial–temporal parameters of human motion analysis. Multi-sensor fusion technology based on inertial sensors frees the monitoring of the human body phase from space constraints and improves the flexibility of the system. However, human phase segmentation methods usually rely on the determination of the positioning of the sensor and the number of sensors
-
Multilevel projections with adaptive neighbor graph for unsupervised multi-view feature selection Inform. Fusion (IF 13.669) Pub Date : 2020-12-29 Han Zhang, Danyang Wu, Feiping Nie, Rong Wang, Xuelong Li
Multi-view feature selection aims at obtaining a subset of informative features from heterogeneous feature domains. Recent graph based approaches mostly learn view-specific feature selection matrices by virtue of prepared single-view graphs, and weight the view-wise objectives to discriminate them. However, the majority of them encounter that (i) the dimensions of vectors for evaluating features in
-
Managing noncooperative behaviors in large-scale group decision-making with linguistic preference orderings: The application in Internet Venture Capital Inform. Fusion (IF 13.669) Pub Date : 2020-12-25 Xunjie Gou, Zeshui Xu
In Internet venture capital (VC), it is very important to fully analyze and evaluate the influential factors. Because this activity usually involves amounts of experts, it makes sense to incorporate it into large-scale group decision-making (LSGDM). In this process, how to deal with the noncooperative behaviors and express the preference information are two important issues that need to be addressed
-
RXDNFuse: A aggregated residual dense network for infrared and visible image fusion Inform. Fusion (IF 13.669) Pub Date : 2020-11-27 Yongzhi Long, Haitao Jia, Yida Zhong, Yadong Jiang, Yuming Jia
This study proposes a novel unsupervised network for IR/VIS fusion task, termed as RXDNFuse, which is based on the aggregated residual dense network. In contrast to conventional fusion networks, RXDNFuse is designed as an end-to-end model that combines the structural advantages of ResNeXt and DenseNet. Hence, it overcomes the limitations of the manual and complicated design of activity-level measurement
-
Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation Inform. Fusion (IF 13.669) Pub Date : 2020-12-07 Iván Palomares, Carlos Porcel, Luiz Pizzato, Ido Guy, Enrique Herrera-Viedma
There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a large city. Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related
-
Adaptive ensemble of classifiers with regularization for imbalanced data classification Inform. Fusion (IF 13.669) Pub Date : 2020-12-13 Chen Wang, Chengyuan Deng, Zhoulu Yu, Dafeng Hui, Xiaofeng Gong, Ruisen Luo
The dynamic ensemble selection of classifiers is an effective approach for processing label-imbalanced data classifications. However, such a technique is prone to overfitting, owing to the lack of regularization methods and the dependence on local geometry of data. In this study, focusing on binary imbalanced data classification, a novel dynamic ensemble method, namely adaptive ensemble of classifiers
-
Variational multimodal machine translation with underlying semantic alignment Inform. Fusion (IF 13.669) Pub Date : 2020-12-08 Xiao Liu, Jing Zhao, Shiliang Sun, Huawen Liu, Hao Yang
Capturing the underlying semantic relationships of sentences is helpful for machine translation. Variational neural machine translation approaches provide an effective way to model the uncertain underlying semantics in languages by introducing latent variables. Multitask learning is applied in multimodal machine translation to integrate multimodal data. However, these approaches usually lack a strong
-
A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals Inform. Fusion (IF 13.669) Pub Date : 2020-11-27 Yassin Khalifa, Danilo Mandic, Ervin Sejdić
Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms
-
Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China Inform. Fusion (IF 13.669) Pub Date : 2020-12-01 Zhen-Song Chen, Xiao-Lu Liu, Kwai-Sang Chin, Witold Pedrycz, Kwok-Leung Tsui, Miroslaw J. Skibniewski
High-speed rail (HSR) has become an essential mode of public transportation in China and is likely to remain so for the foreseeable future. To promote the development of the HSR industry, a high level of passenger satisfaction must be ensured, which means that passenger satisfaction must be assured. Focusing on HSR in-cabin factors that affect the travel experience of HSR passengers, this study aims
-
Recent advances and new guidelines on hyperspectral and multispectral image fusion Inform. Fusion (IF 13.669) Pub Date : 2020-11-13 Renwei Dian, Shutao Li, Bin Sun, Anjing Guo
Hyperspectral image (HSI) with high spectral resolution often suffers from low spatial resolution owing to the limitations of imaging sensors. Image fusion is an effective and economical way to enhance the spatial resolution of HSI, which combines HSI with higher spatial resolution multispectral image (MSI) of the same scenario. In the past years, many HSI and MSI fusion algorithms are introduced to
Contents have been reproduced by permission of the publishers.