| 1. |
- Abril, Daniel, et al.
(författare)
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Supervised Learning Using a Symmetric Bilinear Form for Record Linkage
- 2015
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Ingår i: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 26, s. 144-153
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Tidskriftsartikel (refereegranskat)abstract
- Record Linkage is used to link records of two different files corresponding to the same individuals. These algorithms are used for database integration. In data privacy, these algorithms are used to evaluate the disclosure risk of a protected data set by linking records that belong to the same individual. The degree of success when linking the original (unprotected data) with the protected data gives an estimation of the disclosure risk.In this paper we propose a new parameterized aggregation operator and a supervised learning method for disclosure risk assessment. The parameterized operator is a symmetric bilinear form and the supervised learning method is formalized as an optimization problem. The target of the optimization problem is to find the values of the aggregation parameters that maximize the number of re-identification (or correct links). We evaluate and compare our proposal with other non-parametrized variations of record linkage, such as those using the Mahalanobis distance and the Euclidean distance (one of the most used approaches for this purpose). Additionally, we also compare it with other previously presented parameterized aggregation operators for record linkage such as the weighted mean and the Choquet integral. From these comparisons we show how the proposed aggregation operator is able to overcome or at least achieve similar results than the other parameterized operators. We also study which are the necessary optimization problem conditions to consider the described aggregation functions as metric functions.
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| 2. |
- Ahlberg, Simon, et al.
(författare)
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An information fusion demonstrator for tactical intelligence processing in network-based defense
- 2007
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Ingår i: Information Fusion. - : Elsevier BV. - 1566-2535 .- 1872-6305. ; 8:1, s. 84-107
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Tidskriftsartikel (refereegranskat)abstract
- The Swedish Defence Research Agency (FOI) has developed a concept demonstrator called the Information Fusion Demonstrator 2003 (IFD03) for demonstrating information fusion methodology suitable for a future Network Based Defense (NBD) C4ISR system. The focus of the demonstrator is on real-time tactical intelligence processing at the division level in a ground warfare scenario. The demonstrator integrates novel force aggregation, particle filtering, and sensor allocation methods to create, dynamically update, and maintain components of a tactical situation picture. This is achieved by fusing physically modelled and numerically simulated sensor reports from several different sensor types with realistic a priori information sampled from both a high-resolution terrain model and an enemy organizational and behavioral model. This represents a key step toward the goal of creating in real time a dynamic, high fidelity representation of a moving battalion-sized organization, based on sensor data as well as a priori intelligence and terrain information, employing fusion, tracking, aggregation, and resource allocation methods all built on well-founded theories of uncertainty. The motives behind this project, the fusion methods developed for the system, as well as its scenario model and simulator architecture are described. The main services of the demonstrator are discussed and early experience from using the system is shared.
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| 3. |
- Alcantud, Jose Carlos R., et al.
(författare)
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Decomposition theorems and extension principles for hesitant fuzzy sets
- 2018
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Ingår i: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 41, s. 48-56
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Tidskriftsartikel (refereegranskat)abstract
- We prove a decomposition theorem for hesitant fuzzy sets, which states that every typical hesitant fuzzy set on a set can be represented by a well-structured family of fuzzy sets on that set. This decomposition is expressed by the novel concept of hesitant fuzzy set associated with a family of hesitant fuzzy sets, in terms of newly defined families of their cuts. Our result supposes the first representation theorem of hesitant fuzzy sets in the literature. Other related representation results are proven. We also define two novel extension principles that extend crisp functions to functions that map hesitant fuzzy sets into hesitant fuzzy sets.
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| 4. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Cross-sensor periocular biometrics in a global pandemic : Comparative benchmark and novel multialgorithmic approach
- 2022
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Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 83-84, s. 110-130
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Tidskriftsartikel (refereegranskat)abstract
- The massive availability of cameras and personal devices results in a wide variability between imaging conditions, producing large intra-class variations and a significant performance drop if images from heterogeneous environments are compared for person recognition purposes. However, as biometric solutions are extensively deployed, it will be common to replace acquisition hardware as it is damaged or newer designs appear or to exchange information between agencies or applications operating in different environments. Furthermore, variations in imaging spectral bands can also occur. For example, face images are typically acquired in the visible (VIS) spectrum, while iris images are usually captured in the near-infrared (NIR) spectrum. However, cross-spectrum comparison may be needed if, for example, a face image obtained from a surveillance camera needs to be compared against a legacy database of iris imagery. Here, we propose a multialgorithmic approach to cope with periocular images captured with different sensors. With face masks in the front line to fight against the COVID-19 pandemic, periocular recognition is regaining popularity since it is the only region of the face that remains visible. As a solution to the mentioned cross-sensor issues, we integrate different biometric comparators using a score fusion scheme based on linear logistic regression This approach is trained to improve the discriminating ability and, at the same time, to encourage that fused scores are represented by log-likelihood ratios. This allows easy interpretation of output scores and the use of Bayes thresholds for optimal decision-making since scores from different comparators are in the same probabilistic range. We evaluate our approach in the context of the 1st Cross-Spectral Iris/Periocular Competition, whose aim was to compare person recognition approaches when periocular data from visible and near-infrared images is matched. The proposed fusion approach achieves reductions in the error rates of up to 30%–40% in cross-spectral NIR–VIS comparisons with respect to the best individual system, leading to an EER of 0.2% and a FRR of just 0.47% at FAR = 0.01%. It also represents the best overall approach of the mentioned competition. Experiments are also reported with a database of VIS images from two different smartphones as well, achieving even bigger relative improvements and similar performance numbers. We also discuss the proposed approach from the point of view of template size and computation times, with the most computationally heavy comparator playing an important role in the results. Lastly, the proposed method is shown to outperform other popular fusion approaches in multibiometrics, such as the average of scores, Support Vector Machines, or Random Forest. © 2022 The Authors
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| 5. |
- Bouguelia, Mohamed-Rafik, 1987-, et al.
(författare)
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Mode tracking using multiple data streams
- 2018
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Ingår i: Information Fusion. - Amsterdam : Elsevier BV. - 1566-2535 .- 1872-6305. ; 43, s. 33-46
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Tidskriftsartikel (refereegranskat)abstract
- Most existing work in information fusion focuses on combining information with well-defined meaning towards a concrete, pre-specified goal. In contradistinction, we instead aim for autonomous discovery of high-level knowledge from ubiquitous data streams. This paper introduces a method for recognition and tracking of hidden conceptual modes, which are essential to fully understand the operation of complex environments, and an important step towards building truly intelligent aware systems. We consider a scenario of analyzing usage of a fleet of city buses, where the objective is to automatically discover and track modes such as highway route, heavy traffic, or aggressive driver, based on available on-board signals. The method we propose is based on aggregating the data over time, since the high-level modes are only apparent in the longer perspective. We search through different features and subsets of the data, and identify those that lead to good clusterings, interpreting those clusters as initial, rough models of the prospective modes. We utilize Bayesian tracking in order to continuously improve the parameters of those models, based on the new data, while at the same time following how the modes evolve over time. Experiments with artificial data of varying degrees of complexity, as well as on real-world datasets, prove the effectiveness of the proposed method in accurately discovering the modes and in identifying which one best explains the current observations from multiple data streams.
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| 6. |
- Diez-Olivan, Alberto, et al.
(författare)
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Data Fusion and Machine Learning for Industrial Prognosis : Trends and Perspectives towards Industry 4.0
- 2018
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Ingår i: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 50, s. 92-111
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Tidskriftsartikel (refereegranskat)abstract
- The so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and data fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviors in industrial machinery, tools and processes so as to anticipate critical events and damage, eventually causing important economical losses and safety issues. In this context, data-driven prognosis is gradually gaining attention in different industrial sectors. This paper provides a comprehensive survey of the recent developments in data fusion and machine learning for industrial prognosis, placing an emphasis on the identification of research trends, niches of opportunity and unexplored challenges. To this end, a principled categorization of the utilized feature extraction techniques and machine learning methods will be provided on the basis of its intended purpose: analyze what caused the failure (descriptive), determine when the monitored asset will fail (predictive) or decide what to do so as to minimize its impact on the industry at hand (prescriptive). This threefold analysis, along with a discussion on its hardware and software implications, intends to serve as a stepping stone for future researchers and practitioners to join the community investigating on this vibrant field.
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| 7. |
- Ding, Yijie, et al.
(författare)
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Multi-correntropy fusion based fuzzy system for predicting DNA N4-methylcytosine sites
- 2023
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Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 100, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- The identification of DNA N4-methylcytosine (4mC) sites is an important field of bioinformatics. Statistical learning methods and deep learning have been applied in this direction. The previous methods focused on feature representation and feature selection, and did not take into account the deviation of noise samples for recognition. Moreover, these models were not established from the perspective of prediction error distribution. To solve the problem of complex error distribution, we propose a maximum multi-correntropy criterion based kernelized higher-order fuzzy inference system (MMC-KHFIS), which is constructed with multi-correntropy fusion. There are 6 4mC and 8 UCI data sets are employed to evaluate our model. The MMC-KHFIS achieves better performance in the experiment. © 2023
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| 8. |
- Doherty, Patrick, et al.
(författare)
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Communication between agents with heterogeneous perceptual capabilities
- 2007
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Ingår i: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 8:1, s. 56-69
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Tidskriftsartikel (refereegranskat)abstract
- In real world applications robots and software agents often have to be equipped with higher level cognitive functions that enable them to reason, act and perceive in changing, incompletely known and unpredictable environments. One of the major tasks in such circumstances is to fuse information from various data sources. There are many levels of information fusion, ranging from the fusing of low level sensor signals to the fusing of high level, complex knowledge structures. In a dynamically changing environment even a single agent may have varying abilities to perceive its environment which are dependent on particular conditions. The situation becomes even more complex when different agents have different perceptual capabilities and need to communicate with each other. In this paper, we propose a framework that provides agents with the ability to fuse both low and high level approximate knowledge in the context of dynamically changing environments while taking account of heterogeneous and contextually limited perceptual capabilities. To model limitations on an agent's perceptual capabilities we introduce the idea of partial tolerance spaces. We assume that each agent has one or more approximate databases where approximate relations are represented using lower and upper approximations on sets. Approximate relations are generalizations of rough sets. It is shown how sensory and other limitations can be taken into account when constructing and querying approximate databases for each respective agent. Complex relations inherit the approximativeness of primitive relations used in their definitions. Agents then query these databases and receive answers through the filters of their perceptual limitations as represented by (partial) tolerance spaces and approximate queries. The techniques used are all tractable. © 2005 Elsevier B.V. All rights reserved.
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| 9. |
- Erlandsson, Tina, et al.
(författare)
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Automatic evaluation of air mission routes with respect to combat survival
- 2014
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Ingår i: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 20, s. 88-98
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Tidskriftsartikel (refereegranskat)abstract
- Aircraft flying in hostile environments are exposed to ground-based air defense systems. It is not always possible to both accomplish the mission and fly outside the range of the enemy’s weapon systems, especially if the positions of the enemy’s systems are not perfectly known. Automatic evaluation of mission routes from a combat survival perspective could therefore aid the pilots to plan their missions. When updated information regarding the positions and capabilities of the enemy’s systems is received during flight, the route could be re-evaluated and the mission could be re-planed or aborted if it is assessed to be too dangerous.The survivability model presented here describes the relation between the aircraft and the enemy’s defense systems. It calculates the probabilities that the aircraft is in certain modes along the route, e.g., undetected, tracked or hit. Contrary to previous work, the model is able to capture that the enemy’s systems can communicate and that the enemy must track the aircraft before firing a weapon. The survivability model is used to calculate an expected cost for the mission route. The expected cost has the attractive properties of summarizing the route into a single value and is able to take the pilot’s risk attitude for the mission into account. The evaluation of the route is influenced by uncertainty regarding the locations of the enemy’s sensors and weapons. Monte Carlo simulations are used to capture this uncertainty by calculating the mean and standard deviation for the expected cost. These two parameters give the pilots an assessment of the danger associated with the route as well as the reliability of this assessment. The paper concludes that evaluating routes with the survivability model and the expected cost could aid the pilots to plan and execute their missions.
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| 10. |
- Krish, Ram P., et al.
(författare)
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Improving Automated Latent Fingerprint Identification Using Extended Minutia Types
- 2019
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Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 50, s. 9-19
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Tidskriftsartikel (refereegranskat)abstract
- Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features. © 2018 Elsevier B.V.
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