Article
Computer Science, Artificial Intelligence
Joaquin Abellan, Serafin Moral-Garcia, Maria D. Benitez
Summary: The Belief Function theory is often used to combine different sources of information, with various rules proposed for combination. Dempster's rule of combination has drawbacks, leading to the development of many other combination rules. This research proposes a new hybrid rule that meets mathematical properties and avoids undesirable behaviors seen in other rules.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Thierry Denoeux
Summary: This study addresses the combination of belief functions in a communication network where agents hold evidence and can only exchange information with neighbors. Distributed implementations of Dempster's rule and the cautious rule are proposed based on average and maximum consensus algorithms. Procedures for agents to agree on a frame of discernment and supported hypotheses are described to reduce data exchange, along with a demonstration of the feasibility of a robust combination procedure using a distributed implementation of the RANSAC algorithm.
INFORMATION FUSION
(2021)
Article
Computer Science, Information Systems
Lipeng Pan, Yong Deng
Summary: This paper extends the Dempster-Shafer evidence theory to the complex domain to effectively describe and process uncertain information in multidimensional characteristic data and periodic data with phase angle changes. It introduces the complex mass function and other basic concepts to describe uncertainty and supplements the complex Dempster rule of combination. A method to generate complex mass function and apply it to target recognition is proposed, showing improved recognition rate compared to the traditional mass function approach.
INFORMATION SCIENCES
(2022)
Article
Mathematics, Interdisciplinary Applications
Qianli Zhou, Yong Deng
Summary: This paper explores the relationship between the Sierpinski gasket and matrix calculus in Dempster-Shafer Theory (DST), connecting fractal theory and DST from a geometric perspective for the first time. Additionally, a method to generate the Sierpinski gasket using the Kronecker product is proposed based on the generation process of matrices.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Engineering, Electrical & Electronic
Yunyang Shi, Yan Tu, Lili Wang, Yin Zhang, Xin Gao
Summary: The experiment showed that image attributes significantly influenced visual discomfort and proposed a recognition model based on the Dempster-Shafer evidence theory, which could effectively identify visual discomfort states using multiple features and weighting coefficients.
JOURNAL OF THE SOCIETY FOR INFORMATION DISPLAY
(2022)
Article
Computer Science, Artificial Intelligence
Hao Luo, Qianli Zhou, Zhen Li, Yong Deng
Summary: Dempster-Shafer Theory (DST) is widely used in decision making and information fusion, but its exponential computational complexity limits real-time application. This paper proposes a VQLS-based method to conduct DST operations with lower error level and fewer quantum resources, suitable for the NISQ era.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Xingyuan Chen, Yong Deng
Summary: The handling of conflict in Dempster-Shafer evidence theory is still an open issue. Various approaches have been proposed, including improving the combination rule and modifying the data model. This paper introduces a novel combination rule that assigns conflicting mass to the power set (ACTP), which offers the advantage of sequential fusion and reduces computational complexity.
Article
Computer Science, Artificial Intelligence
Ildar Batyrshin, Luis Alfonso Villa-Vargas, Marco Antonio Ramirez-Salinas, Moises Salinas-Rosales, Nailya Kubysheva
Summary: The paper introduces the notion of negation of a probability distribution, emphasizing the need for such a negation in knowledge-based systems. The study focuses on transforming probability distributions point by point using decreasing functions defined on [0,1]. The characterization of linear negators is presented as a convex combination of Yager's and uniform negators.
Article
Computer Science, Artificial Intelligence
Zezheng Yan, Hanping Zhao, Xiaowen Mei
Summary: The study proposes an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment. The method involves calculating conflict intensity and evidence unreliability, constructing a redistribution equation for the basic probability assignment, and using information entropy to modify the basic probability assignment for more accurate results.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Yuan-Wei Du, Jiao-Jiao Zhong
Summary: This study establishes a generalized combination (GC) rule with both weight and reliability to address the infeasibilities in the evidential reasoning (ER) approach, showing superiority through numerical comparisons and discussions.
INFORMATION SCIENCES
(2021)
Article
Management
Xingli Wu, Huchang Liao
Summary: This study proposes a new evidence combination method that controls the degrees of compensation between conflicting pieces of evidence through adjustment coefficients, incorporating information reliability and importance parameters. Through two case studies, the advantages of the proposed method in dealing with high levels of conflicting evidence are verified.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2022)
Article
Computer Science, Artificial Intelligence
Yuanpeng He, Fuyuan Xiao
Summary: An improved method is proposed for addressing the conflicting management issue in the Dempster combination rule, which is crucial in multisource data fusion for applications like group decision making and target recognition. The new combination method presented in this study can handle highly conflicting environments without requiring normalization, offering convenience in computation and higher accuracy in predicting potential possibilities, especially in extreme circumstances. The validity and rationality of the proposed method are confirmed through numerical examples and real benchmark data from the UCI database.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Information Systems
Jie Zhao, Kang Hao Cheong
Summary: The inference of the source in a pandemic outbreak has attracted considerable attention due to its practical potential. We propose an evidential source localization (ESL) model that utilizes evidence theory to determine the source node by information fusion. ESL is characterized by its ability to detect sources of disease at an early stage of the pandemic. Experimental results demonstrate the superiority of ESL compared to other state-of-the-art methods in terms of efficiency and effectiveness.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Thierry Denoeux
Summary: This article presents a neural network model for regression that quantifies prediction uncertainty using GRFNs. The model is competitive in terms of prediction accuracy and calibration error compared to other advanced techniques.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Nimisha Ghosh, Sayantan Saha, Rourab Paul
Summary: The importance of multisensor data fusion in engineering applications and its application in fault diagnosis are discussed in this paper. By proposing an improved combination rule, the problems caused by sensor data conflicts are effectively overcome.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
John Klein, Sebastien Destercke, Olivier Colot
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2018)
Article
Computer Science, Artificial Intelligence
Sebastien Destercke, Frederic Pichon, John Klein
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2019)
Article
Computer Science, Artificial Intelligence
John Klein
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2019)
Article
Engineering, Electrical & Electronic
Anatole Desreveaux, Alain Bouscayrol, Rochdi Trigui, Elodie Castex, John Klein
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Article
Engineering, Biomedical
Kevin N. D. Brou Boni, John Klein, Ludovic Vanquin, Antoine Wagner, Thomas Lacornerie, David Pasquier, Nick Reynaert
PHYSICS IN MEDICINE AND BIOLOGY
(2020)
Article
Computer Science, Artificial Intelligence
Mahmoud Albardan, John Klein, Olivier Colot
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Radiology, Nuclear Medicine & Medical Imaging
Evin N. D. Brou Boni, John Klein, Akos Gulyban, Nick Reynaert, David Pasquier
Summary: MR-to-CT synthesis using an Augmented CycleGAN model showed good performance in generating synthetic CT images from unpaired data, indicating potential for generalization across different scanners and sequences. The mean absolute errors for sCT image generation were 59.8 HU and 65.8 HU for different test sites, with low dose differences and high gamma pass rates observed in dose distribution analysis.
Article
Biology
Arnaud Deleruyelle, Cristian Versari, John Klein
Summary: This study introduces a neural pipeline for micro-capsule image segmentation, which utilizes synthetic or indirect supervision to improve model generalization. Experimental results demonstrate significant accuracy improvement, indicating the potential of replacing human annotations.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Engineering, Electrical & Electronic
Rui Min, Christelle Garnier, Francois Septier, John Klein
Summary: This study proposes a data-driven partitioning method based on constrained spectral clustering to automatically provide an appropriate partition, helping BPF escape the curse of dimensionality. The method successfully groups the most correlated state variables, reducing the variance of the filtering distribution estimate and limiting the level of bias.
Article
Computer Science, Theory & Methods
Solene Bernard, Patrick Bas, John Klein, Tomas Pevny
Summary: The minmax protocol automatically optimizes steganographic algorithms against various steganalytic detectors, while Backpack provides a theoretically sound solution to address the flaws in the protocol. Experimental verification shows that Backpack performs better than ADV-EMB and enhances the security of steganographic algorithms.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2022)
Article
Computer Science, Theory & Methods
Solene Bernard, Patrick Bas, John Klein, Tomas Pevny
Summary: The proposed algorithm aims to enhance the practical security of classical steganographic methods by selecting the least detectable stego image to simulate the game between Alice and Eve. Through extensive evaluation, the algorithm shows potential to increase practical security by effectively hiding information from classifiers.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
John Klein, Mahmoud Albardan, Benjamin Guedj, Olivier Colot
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2019, PT I
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Solene Bernard, Tomas Pevny, Patrick Bas, John Klein
IH&MMSEC '19: PROCEEDINGS OF THE ACM WORKSHOP ON INFORMATION HIDING AND MULTIMEDIA SECURITY
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Sebastien Destercke, Frederic Pichon, John Klein
BELIEF FUNCTIONS: THEORY AND APPLICATIONS, BELIEF 2018
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Cyrille Feudjio, Alain Tiedeu, John Klein, Olivier Colot
2017 13TH INTERNATIONAL CONFERENCE ON SIGNAL-IMAGE TECHNOLOGY AND INTERNET-BASED SYSTEMS (SITIS)
(2017)
Article
Computer Science, Artificial Intelligence
Timotheus Kampik, Kristijonas Cyras, Jose Ruiz Alarcon
Summary: This paper presents a formal approach to explaining changes in inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). The approach traces the causes of strength inconsistencies and provides explanations for them.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Xiangnan Zhou, Longchun Wang, Qingguo Li
Summary: This paper aims to establish a closer connection between domain theory and Formal Concept Analysis (FCA) by introducing the concept of an optimized concept for a formal context. With the utilization of optimized concepts, it is demonstrated that the class of formal contexts directly corresponds to algebraic domains. Additionally, two subclasses of formal contexts are identified to characterize algebraic L-domains and Scott domains. An application is presented to address the open problem of reconstructing bounded complete continuous domains using attribute continuous contexts, and the presentation of algebraic domains is extended to a categorical equivalence.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Sihan Wang, Zhong Yuan, Chuan Luo, Hongmei Chen, Dezhong Peng
Summary: Anomaly detection is widely used in various fields, but most current methods only work for specific data and ignore uncertain information such as fuzziness. This paper proposes an anomaly detection algorithm based on fuzzy rough entropy, which effectively addresses the similarity between high-dimensional objects using distance and correlation measures. The algorithm is compared and analyzed with mainstream anomaly detection algorithms on publicly available datasets, showing superior performance and flexibility.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Mario Alviano, Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Daniele Theseider Dupre
Summary: This paper investigates the relationships between a multipreferential semantics in defeasible reasoning and a multilayer neural network model. Weighted knowledge bases are considered for a simple description logic with typicality under a concept-wise multipreference semantics. The semantics is used to interpret MultiLayer Perceptrons (MLPs) preferentially. Model checking and entailment based approach are employed in verifying conditional properties of MLPs.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Bazin Alexandre, Galasso Jessie, Kahn Giacomo
Summary: Formal concept analysis is a mathematical framework that represents the information in binary object-attribute datasets using a lattice of formal concepts. It has been extended to handle more complex data types, such as relational data and n-ary relations. This paper presents a framework for polyadic relational concept analysis, which extends relational concept analysis to handle relational datasets consisting of n-ary relations.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Ander Gray, Marcelo Forets, Christian Schilling, Scott Ferson, Luis Benet
Summary: The presented method combines reachability analysis and probability bounds analysis to handle imprecisely known random variables. It can rigorously compute the temporal evolution of p-boxes and provide interval probabilities for formal verification problems. The method does not impose strict constraints on the input probability distribution or p-box and can handle multivariate p-boxes with a consonant approximation method.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Laszlo Csato
Summary: This paper studies a special type of incomplete pairwise comparison matrices and proposes a new method to determine the missing elements without violating the ordinal property.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)