Статті в журналах: "Belief-Tracking"

1

Baltag, Alexandru, Nina Gierasimczuk, and Sonja Smets. "Truth-Tracking by Belief Revision." Studia Logica 107, no. 5 (July 20, 2018): 917–47. http://dx.doi.org/10.1007/s11225-018-9812-x.

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2

Xu, Jian-min, Shu-fang Wu, and Yu Hong. "Topic tracking with Bayesian belief network." Optik 125, no. 9 (May 2014): 2164–69. http://dx.doi.org/10.1016/j.ijleo.2013.10.044.

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3

Adams, Fred, John A. Barker, and Murray Clarke. "Knowledge as Fact-Tracking True Belief." Manuscrito 40, no. 4 (December 2017): 1–30. http://dx.doi.org/10.1590/0100-6045.2017.v40n4.fa.

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4

Brafman, Ronen I., and Guy Shani. "Online belief tracking using regression for contingent planning." Artificial Intelligence 241 (December 2016): 131–52. http://dx.doi.org/10.1016/j.artint.2016.08.005.

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5

Grainger, Sarah A., Julie D. Henry, Claire K. Naughtin, Marita S. Comino, and Paul E. Dux. "Implicit false belief tracking is preserved in late adulthood." Quarterly Journal of Experimental Psychology 71, no. 9 (January 1, 2018): 1980–87. http://dx.doi.org/10.1177/1747021817734690.

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Анотація:

It is now well established that relative to their younger counterparts, older adults experience difficulties on tasks that require the conscious and explicit processing of others’ mental states (e.g., beliefs, intentions; theory of mind [ToM]). Despite the importance of relatively automatic and unconscious mental state attribution processes in everyday life, no study to date has tested whether tasks that require the implicit processing of others’ belief states also show age-related changes. In this study, younger and older adults completed an implicit false belief task, in which their eye movement patterns were monitored while they passively viewed true and false belief movies. In addition, they were assessed on measures of explicit ToM processing. While older adults showed impairments in explicit ToM processing relative to younger adults, both age groups demonstrated a similar capacity for implicit false belief processing. These findings suggest that implicit components of ToM are preserved in late adulthood and are consistent with dual process models of ageing that emphasise age-related stability in automatic processing and declines in more controlled and effortful cognitive operations. We discuss the potential implications of these findings for social interactions in old age.

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6

KHONGKRAPHAN, Kittiya, and Pakorn KAEWTRAKULPONG. "Efficient Human Body Tracking by Quick Shift Belief Propagation." IEICE Transactions on Information and Systems E94-D, no. 4 (2011): 905–12. http://dx.doi.org/10.1587/transinf.e94.d.905.

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7

Schneider, Dana, Virginia P. Slaughter, and Paul E. Dux. "What do we know about implicit false-belief tracking?" Psychonomic Bulletin & Review 22, no. 1 (May 22, 2014): 1–12. http://dx.doi.org/10.3758/s13423-014-0644-z.

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8

Xue, Jianru, Nanning Zheng, Jason Geng, and Xiaopin Zhong. "Tracking Multiple Visual Targets via Particle-Based Belief Propagation." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 1 (February 2008): 196–209. http://dx.doi.org/10.1109/tsmcb.2007.910533.

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9

Jogan, Matjaz, Alan He, Alexander Tank, and Alan Stocker. "Humans maintain probabilistic belief states when tracking occluded objects." Journal of Vision 15, no. 12 (September 1, 2015): 188. http://dx.doi.org/10.1167/15.12.188.

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10

Xue, Jianru, Nanning Zheng, and Xiaopin Zhong. "Sequential stratified sampling belief propagation for multiple targets tracking." Science in China Series F 49, no. 1 (January 2006): 48–62. http://dx.doi.org/10.1007/s11432-004-0140-6.

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11

Dallil, Ahmed, Abdelaziz Ouldali, and Mourad Oussalah. "Data Association in Multi-target Tracking Using Belief Function." Journal of Intelligent & Robotic Systems 67, no. 3-4 (November 18, 2011): 219–27. http://dx.doi.org/10.1007/s10846-011-9640-y.

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12

Bonet, B., and H. Geffner. "Belief Tracking for Planning with Sensing: Width, Complexity and Approximations." Journal of Artificial Intelligence Research 50 (August 31, 2014): 923–70. http://dx.doi.org/10.1613/jair.4475.

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Анотація:

We consider the problem of belief tracking in a planning setting where states are valuations over a set of variables that are partially observable, and beliefs stand for the sets of states that are possible. While the problem is intractable in the worst case, it has been recently shown that in deterministic conformant and contingent problems, belief tracking is exponential in a width parameter that is often bounded and small. In this work, we extend these results in two ways. First, we introduce a width notion that applies to non-deterministic problems as well, develop a factored belief tracking algorithm that is exponential in the problem width, and show how it applies to existing benchmarks. Second, we introduce a meaningful, powerful, and sound approximation scheme, beam tracking, that is exponential in a smaller parameter, the problem causal width, and has much broader applicability. We illustrate the value of this algorithm over large instances of problems such as Battleship, Minesweeper, and Wumpus, where it yields state-of-the-art performance in real-time.

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13

Király, Ildikó, Katalin Oláh, Gergely Csibra, and Ágnes Melinda Kovács. "Retrospective attribution of false beliefs in 3-year-old children." Proceedings of the National Academy of Sciences 115, no. 45 (October 15, 2018): 11477–82. http://dx.doi.org/10.1073/pnas.1803505115.

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Анотація:

A current debate in psychology and cognitive science concerns the nature of young children’s ability to attribute and track others’ beliefs. Beliefs can be attributed in at least two different ways: prospectively, during the observation of belief-inducing situations, and in a retrospective manner, based on episodic retrieval of the details of the events that brought about the beliefs. We developed a task in which only retrospective attribution, but not prospective belief tracking, would allow children to correctly infer that someone had a false belief. Eighteen- and 36-month-old children observed a displacement event, which was witnessed by a person wearing sunglasses (Experiment 1). Having later discovered that the sunglasses were opaque, 36-month-olds correctly inferred that the person must have formed a false belief about the location of the objects and used this inference in resolving her referential expressions. They successfully performed retrospective revision in the opposite direction as well, correcting a mistakenly attributed false belief when this was necessary (Experiment 3). Thus, children can compute beliefs retrospectively, based on episodic memories, well before they pass explicit false-belief tasks. Eighteen-month-olds failed in such a task, suggesting that they cannot retrospectively attribute beliefs or revise their initial belief attributions. However, an additional experiment provided evidence for prospective tracking of false beliefs in 18-month-olds (Experiment 2). Beyond identifying two different modes for tracking and updating others’ mental states early in development, these results also provide clear evidence of episodic memory retrieval in young children.

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14

Zani, Giovanni, Stephen A. Butterfill, and Jason Low. "Mindreading in the balance: adults' mediolateral leaning and anticipatory looking foretell others’ action preparation in a false-belief interactive task." Royal Society Open Science 7, no. 1 (January 2020): 191167. http://dx.doi.org/10.1098/rsos.191167.

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Анотація:

Anticipatory looking on mindreading tasks can indicate our expectation of an agent's action. The challenge is that social situations are often more complex, involving instances where we need to track an agent's false belief to successfully identify the outcome to which an action is directed. If motor processes can guide how action goals are understood, it is conceivable—where that kind of goal ascription occurs in false-belief tasks—for motor representations to account for someone's belief-like state. Testing adults ( N = 42) in a real-time interactive helping scenario, we discovered that participants' early mediolateral motor activity (leftwards–rightwards leaning on balance board) foreshadowed the agent's belief-based action preparation. These results suggest fast belief-tracking can modulate motor representations generated in the course of one's interaction with an agent. While adults' leaning, and anticipatory looking, revealed the contribution of fast false-belief tracking, participants did not correct the agent's mistake in their final helping action. These discoveries suggest that adults may not necessarily use another's belief during overt social interaction or find reflecting on another's belief as being normatively relevant to one's own choice of action. Our interactive task design offers a promising way to investigate how motor and mindreading processes may be variously integrated.

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15

Bonet, Blai, and Hector Geffner. "Width and Complexity of Belief Tracking in Non-Deterministic Conformant and Contingent Planning." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1756–62. http://dx.doi.org/10.1609/aaai.v26i1.8365.

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Анотація:

It has been shown recently that the complexity of belief tracking in deterministic conformant and contingent planning is exponential in a width parameter that is often bounded and small. In this work, we introduce a new width notion that applies to non-deterministic conformant and contingent problems as well. We also develop a belief tracking algorithm for non-deterministic problems that is exponential in the problem width, analyze the width of non-deterministic benchmarks, compare the new notion to the previous one over deterministic problems, and present experimental results.

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16

Albore, Alexandre, Miquel Ramírez, and Hector Geffner. "Effective Heuristics and Belief Tracking for Planning with Incomplete Information." Proceedings of the International Conference on Automated Planning and Scheduling 21 (March 22, 2011): 2–9. http://dx.doi.org/10.1609/icaps.v21i1.13473.

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Conformant planning can be formulated as a path-finding problem in belief space where the two main challenges are the heuristics to guide the search, and the representation and update of beliefs. In the translation-based approach recently introduced by Palacios and Geffner, the two aspects are handled together by translating conformant problems into classical ones that are solved with classical planners. While competitive with state-of-the-art methods, the translation-based approach runs however into three difficulties. First, complete translations are expensive for problems with high width; second, incomplete translations can generate infinite heuristic values for problems that are solvable; and third, aspects that are specific to the conformant setting, such as the cardinality of beliefs, are not accounted for.In this work, we build on the translation-based approach but not for solving conformant problems with a classical planner but for deriving heuristics and computing beliefs in the context of a standard belief-space planner. For this, a novel translation KSi is introduced that is always complete, but which is sound for problems with width bounded by i. A new conformant planner, called T1, builds then on this translation for i=1, extending the heuristic that results with a second heuristic obtained from invariant "oneof expressions". A number of experiments is performed to compare T1 with state-of-the-art conformant planners.

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17

Soldi, Giovanni, Florian Meyer, Paolo Braca, and Franz Hlawatsch. "Self-Tuning Algorithms for Multisensor-Multitarget Tracking Using Belief Propagation." IEEE Transactions on Signal Processing 67, no. 15 (August 1, 2019): 3922–37. http://dx.doi.org/10.1109/tsp.2019.2916764.

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18

SU, Zhenzhen, Hongbing JI, and Yongquan ZHANG. "Loopy belief propagation based data association for extended target tracking." Chinese Journal of Aeronautics 33, no. 8 (August 2020): 2212–23. http://dx.doi.org/10.1016/j.cja.2020.01.004.

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19

Yin, Ting, Decai Zou, Xiaochun Lu, and Cheng Bi. "A Multisensor Fusion-Based Cooperative Localization Scheme in Vehicle Networks." Electronics 11, no. 4 (February 16, 2022): 603. http://dx.doi.org/10.3390/electronics11040603.

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Utilizing the measured distance and information exchanged between two different nodes to cooperatively locate in a mobile network has become a solution to replace global navigation satellite system (GNSS) positioning. However, the localization accuracy of the belief propagation-based cooperative localization scheme is substantially influenced by the number of neighbors. In this paper, we propose a cooperative localization scheme combined with a trajectory tracking algorithm. With an insufficient number of neighbors, the trajectory tracking algorithm is utilized to participate in the positioning process of agents. Concretely, we carry out sensor information fusion and utilize quantum-behaved, particle-swarm-optimized, bidirectional long short-term memory (QPSO–BiLSTM) as a trajectory tracking strategy, to precisely predict the positions of agents. It is evident from simulations and results that the proposed cooperative localization scheme performs better than the belief propagation (BP)-based cooperative localization scheme in position error.

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20

Klein, John, Christèle Lecomte, and Pierre Miché. "Hierarchical and conditional combination of belief functions induced by visual tracking." International Journal of Approximate Reasoning 51, no. 4 (March 2010): 410–28. http://dx.doi.org/10.1016/j.ijar.2009.12.001.

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21

Bernier, Olivier, Pascal Cheung-Mon-Chan, and Arnaud Bouguet. "Fast nonparametric belief propagation for real-time stereo articulated body tracking." Computer Vision and Image Understanding 113, no. 1 (January 2009): 29–47. http://dx.doi.org/10.1016/j.cviu.2008.07.001.

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22

Alshamaa, Daniel, Farah Mourad-Chehade, and Paul Honeine. "Tracking of Mobile Sensors Using Belief Functions in Indoor Wireless Networks." IEEE Sensors Journal 18, no. 1 (January 1, 2018): 310–19. http://dx.doi.org/10.1109/jsen.2017.2766630.

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23

Edwards, Katheryn, and Jason Low. "Level 2 perspective-taking distinguishes automatic and non-automatic belief-tracking." Cognition 193 (December 2019): 104017. http://dx.doi.org/10.1016/j.cognition.2019.104017.

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24

Savic, Vladimir, Henk Wymeersch, and Santiago Zazo. "Belief consensus algorithms for fast distributed target tracking in wireless sensor networks." Signal Processing 95 (February 2014): 149–60. http://dx.doi.org/10.1016/j.sigpro.2013.09.005.

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25

Wu, Bo, Yanpeng Feng, and Hongyan Zheng. "POSTERIOR BELIEF CLUSTERING ALGORITHM FOR ENERGY-EFFICIENT TRACKING IN WIRELESS SENSOR NETWORKS." International Journal on Smart Sensing and Intelligent Systems 7, no. 3 (2014): 628–41. http://dx.doi.org/10.21307/ijssis-2017-688.

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26

Yi, Lingzhi, Weihong Xiao, Wenxin Yu, and Binren Wang. "Dynamical analysis, circuit implementation and deep belief network control of new six-dimensional hyperchaotic system." Journal of Algorithms & Computational Technology 12, no. 4 (July 25, 2018): 361–75. http://dx.doi.org/10.1177/1748301818788649.

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In this paper, a new six-dimensional hyperchaotic system is proposed and some basic dynamical properties including bifurcation diagrams, Lyapunov exponents and phase portraits are investigated. Furthermore, the electronic circuit of this novel hyperchaotic system is simulated on the Multisim platform, and the simulation results are agreed well with the numerical simulation of the same hyperchaotic system on the Matlab platform. Finally, a control method based on Deep Belief Network is proposed to track and control the proposed hyperchaotic system. In this method, the function of the hyperchaotic system is studied by Deep Belief Network and a high precision fitting function is obtained. Then a controller which is composed of the fitting function and the tracking reference signal is designed to achieve the tracking control of hyperchaotic systems. Simulation results verify the effectiveness and feasibility of this method.

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27

Lee, Yutae. "Dynamic Spectrum Access Using Belief Vector Tracking Method for Other Competing Secondary Users." Journal of the Korea Institute of Information and Communication Engineering 17, no. 11 (November 30, 2013): 2547–52. http://dx.doi.org/10.6109/jkiice.2013.17.11.2547.

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28

Renna, Ilaria, Ryad Chellali, and Catherine Achard. "Combination of Annealing Particle Filter and Belief Propagation for 3D Upper Body Tracking." Applied Bionics and Biomechanics 9, no. 4 (2012): 443–56. http://dx.doi.org/10.1155/2012/178981.

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3D upper body pose estimation is a topic greatly studied by the computer vision society because it is useful in a great number of applications, mainly for human robots interactions including communications with companion robots. However there is a challenging problem: the complexity of classical algorithms that increases exponentially with the dimension of the vectors’ state becomes too difficult to handle. To tackle this problem, we propose a new approach that combines several annealing particle filters defined independently for each limb and belief propagation method to add geometrical constraints between individual filters. Experimental results on a real human gestures sequence will show that this combined approach leads to reliable results.

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29

Boumediene, Mohammed, Jean-Philippe Lauffenburger, Jeremie Daniel, Christophe Cudel, and Abdelaziz Ouamri. "Multi-ROI Association and Tracking With Belief Functions: Application to Traffic Sign Recognition." IEEE Transactions on Intelligent Transportation Systems 15, no. 6 (December 2014): 2470–79. http://dx.doi.org/10.1109/tits.2014.2320536.

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30

Yao, Rui, Shixiong Xia, Zhen Zhang, and Yanning Zhang. "Real-Time Correlation Filter Tracking by Efficient Dense Belief Propagation With Structure Preserving." IEEE Transactions on Multimedia 19, no. 4 (April 2017): 772–84. http://dx.doi.org/10.1109/tmm.2016.2631727.

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31

Motamed, Cina. "Motion detection and tracking using belief indicators for an automatic visual-surveillance system." Image and Vision Computing 24, no. 11 (November 2006): 1192–201. http://dx.doi.org/10.1016/j.imavis.2005.06.005.

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32

Liang, Jun, Minzhe Li, Zhongliang Jing, and Han Pan. "Multi-Target Joint Detection; Tracking and Classification Based on Marginal GLMB Filter and Belief Function Theory." Sensors 20, no. 15 (July 29, 2020): 4235. http://dx.doi.org/10.3390/s20154235.

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This paper proposes a new solution to multi-target joint detection, tracking and classification based on labeled random finite set (RFS) and belief function theory. A class dependent multi-model marginal generalized labeled multi-Bernoulli (MGLMB) filter is developed to analytically calculate the multi-target number, state estimates and model probabilities. In addition, a two-level classifier based on continuous transferable belief model (cTBM) is designed for target classification. To make full use of the kinematic characteristics for classification, both the dynamic modes and states are considered in the classifier, the model dependent class beliefs are computed on the continuous state feature subspace corresponding to different dynamic modes and then fused. As a result that the uncertainty about the classes is well described for decision, the classification results are more reasonable and robust. Moreover, as the estimation and classification problems are jointly solved, the tracking and classification performance are both improved. In the simulation, a scenario contains multi-target with miss detection and dense clutter is used. The performance of multi-target detection, tracking and classification is better than traditional methods based on Bayesian classifier or single model. Simulation results are illustrated to demonstrate the effectiveness and superiority of the proposed algorithm.

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33

Cai, Yingfeng, Hai Wang, Xiao-qiang Sun, and Long Chen. "Visual Vehicle Tracking Based on Deep Representation and Semisupervised Learning." Journal of Sensors 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/6471250.

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Анотація:

Discriminative tracking methods use binary classification to discriminate between the foreground and background and have achieved some useful results. However, the use of labeled training samples is insufficient for them to achieve accurate tracking. Hence, discriminative classifiers must use their own classification results to update themselves, which may lead to feedback-induced tracking drift. To overcome these problems, we propose a semisupervised tracking algorithm that uses deep representation and transfer learning. Firstly, a 2D multilayer deep belief network is trained with a large amount of unlabeled samples. The nonlinear mapping point at the top of this network is subtracted as the feature dictionary. Then, this feature dictionary is utilized to transfer train and update a deep tracker. The positive samples for training are the tracked vehicles, and the negative samples are the background images. Finally, a particle filter is used to estimate vehicle position. We demonstrate experimentally that our proposed vehicle tracking algorithm can effectively restrain drift while also maintaining the adaption of vehicle appearance. Compared with similar algorithms, our method achieves a better tracking success rate and fewer average central-pixel errors.

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34

Zhong, Bineng, Shengnan Pan, Hongbo Zhang, Tian Wang, Jixiang Du, Duansheng Chen, and Liujuan Cao. "Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision." BioMed Research International 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/9406259.

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In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

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35

RENNA, ILARIA, RYAD CHELLALI, and CATHERINE ACHARD. "REAL AND SIMULATED UPPER BODY TRACKING WITH ANNEALING PARTICLE FILTER AND BELIEF PROPAGATION FOR HUMAN–ROBOT INTERACTION." International Journal of Humanoid Robotics 08, no. 01 (March 2011): 127–46. http://dx.doi.org/10.1142/s0219843611002368.

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This article presents an algorithm for 3D upper body tracking. This algorithm is a combination of two well-known methods: annealing particle filter and belief propagation. It is worth to underline that the 3D body tracking presents a challenging problem because of the high dimensionality of state space and so because of the huge computational time. In this work, we show that with our algorithm, it is possible to tackle this problem. Experiments both on real and synthetic human gesture sequences demonstrate that this combined approach leads to reliable results, as it reduces computational time without loosing robustness.

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36

Lakshminarasimhan, Kaushik J., Eric Avila, Erin Neyhart, Gregory C. DeAngelis, Xaq Pitkow, and Dora E. Angelaki. "Tracking the Mind’s Eye: Primate Gaze Behavior during Virtual Visuomotor Navigation Reflects Belief Dynamics." Neuron 106, no. 4 (May 2020): 662–74. http://dx.doi.org/10.1016/j.neuron.2020.02.023.

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37

WILLIAMS, JOHN N. "FACT-TRACKING BELIEF AND THE BACKWARD CLOCK: A REPLY TO ADAMS, BARKER AND CLARKE." Manuscrito 41, no. 3 (October 8, 2018): 29–50. http://dx.doi.org/10.1590/0100-6045.2018.v41n3.jw.

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38

Yih-Shyh Chiou and Fuan Tsai. "A Reduced-Complexity Data-Fusion Algorithm Using Belief Propagation for Location Tracking in Heterogeneous Observations." IEEE Transactions on Cybernetics 44, no. 6 (June 2014): 922–35. http://dx.doi.org/10.1109/tcyb.2013.2276749.

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39

Sendi, Mondher, Mohamed Nazih Omri, and Mourad Abed. "Discovery and tracking of temporal topics of interest based on belief-function and aging theories." Journal of Ambient Intelligence and Humanized Computing 10, no. 9 (September 22, 2018): 3409–25. http://dx.doi.org/10.1007/s12652-018-1050-6.

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40

Smets, Philippe, and Branko Ristic. "Kalman filter and joint tracking and classification based on belief functions in the TBM framework." Information Fusion 8, no. 1 (January 2007): 16–27. http://dx.doi.org/10.1016/j.inffus.2005.06.004.

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41

Liao, Lizi, Le Hong Long, Yunshan Ma, Wenqiang Lei, and Tat-Seng Chua. "Dialogue State Tracking with Incremental Reasoning." Transactions of the Association for Computational Linguistics 9 (2021): 557–69. http://dx.doi.org/10.1162/tacl_a_00384.

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Анотація:

Abstract Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method outperforms the state-of-the-art methods in terms of joint belief accuracy for MultiWOZ 2.1, a large-scale human--human dialogue dataset across multiple domains.

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42

Wheeler, Billy. "Truth Tracking and Knowledge from Virtual Reality." Logos & Episteme 11, no. 3 (2020): 369–88. http://dx.doi.org/10.5840/logos-episteme202011327.

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Анотація:

Is it possible to gain knowledge about the real world based solely on experiences in virtual reality? According to one influential theory of knowledge, you cannot. Robert Nozick's truth-tracking theory requires that, in addition to a belief being true, it must also be sensitive to the truth. Yet beliefs formed in virtual reality are not sensitive: in the nearest possible world where P is false, you would have continued to believe that P. This is problematic because there is increasing awareness from philosophers and technologists that virtual reality is an important way in which we can arrive at beliefs and knowledge about the world. Here I argue that a suitably modified version of Nozick's sensitivity condition is able to account for knowledge from virtual reality.

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43

Cao, Chenghu, and Yongbo Zhao. "A Generalized Labeled Multi-Bernoulli Filter Based on Track-before-Detect Measurement Model for Multiple-Weak-Target State Estimate Using Belief Propagation." Remote Sensing 14, no. 17 (August 26, 2022): 4209. http://dx.doi.org/10.3390/rs14174209.

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In this paper, we propose the specific recursion formula for the generalized labeled multi-Bernoulli filter based on the track-before-detect strategy (GLMB-TBD) using a belief propagation algorithm. The proposed method aims to track multiple weak targets with superior performance. Compared to the Murty algorithm-based and Gibbs sampling-based implementation of GLMB-TBD filter, the proposed algorithm improves the tracking accuracy of multiple weak targets without pruning operation to preserve the relevant association information. The superior performance in tracking accuracy of the algorithm is validated for simulated scenarios using OSPA(2) metric. More importantly, the simulation results demonstrate that the proposed algorithm outputs both the Gibbs sampling-based version and Murty algorithm-based version in computational cost due to linear complex in the number of both Bernoulli components and measurements.

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Nilashi, Mehrbakhsh, Hossein Ahmadi, Abbas Sheikhtaheri, Roya Naemi, Reem Alotaibi, Ala Abdulsalam Alarood, Asmaa Munshi, Tarik A. Rashid, and Jing Zhao. "Remote tracking of Parkinson's Disease progression using ensembles of Deep Belief Network and Self-Organizing Map." Expert Systems with Applications 159 (November 2020): 113562. http://dx.doi.org/10.1016/j.eswa.2020.113562.

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Alshamaa, Daniel, Farah Mourad-Chehade, Paul Honeine, and Aly Chkeir. "An Evidential Framework for Localization of Sensors in Indoor Environments." Sensors 20, no. 1 (January 6, 2020): 318. http://dx.doi.org/10.3390/s20010318.

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Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The decision-making process in localization systems relies on data coming from multiple sensors. The data retrieved from these sensors require robust fusion approaches to be processed. One of these approaches is the belief functions theory (BFT), also called the Dempster–Shafer theory. This theory deals with uncertainty and imprecision with a theoretically attractive evidential reasoning framework. This paper investigates the usage of the BFT to define an evidence framework for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.

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Liu, Peixin, Xiaofeng Li, Yang Wang, and Zhizhong Fu. "Multiple Object Tracking for Dense Pedestrians by Markov Random Field Model with Improvement on Potentials." Sensors 20, no. 3 (January 22, 2020): 628. http://dx.doi.org/10.3390/s20030628.

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Pedestrian tracking in dense crowds is a challenging task, even when using a multi-camera system. In this paper, a new Markov random field (MRF) model is proposed for the association of tracklet couplings. Equipped with a new potential function improvement method, this model can associate the small tracklet coupling segments caused by dense pedestrian crowds. The tracklet couplings in this paper are obtained through a data fusion method based on image mutual information. This method calculates the spatial relationships of tracklet pairs by integrating position and motion information, and adopts the human key point detection method for correction of the position data of incomplete and deviated detections in dense crowds. The MRF potential function improvement method for dense pedestrian scenes includes assimilation and extension processing, as well as a message selective belief propagation algorithm. The former enhances the information of the fragmented tracklets by means of a soft link with longer tracklets and expands through sharing to improve the potentials of the adjacent nodes, whereas the latter uses a message selection rule to prevent unreliable messages of fragmented tracklet couplings from being spread throughout the MRF network. With the help of the iterative belief propagation algorithm, the potentials of the model are improved to achieve valid association of the tracklet coupling fragments, such that dense pedestrians can be tracked more robustly. Modular experiments and system-level experiments are conducted using the PETS2009 experimental data set, where the experimental results reveal that the proposed method has superior tracking performance.

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Jeong, Yeon Jin, and Hye Jung Jun. "Factors influencing depression in COVID-19 quarantined persons: Based on the 2020 Community Health Survey." Korea Society of Nursing Research 6, no. 2 (June 30, 2022): 91–102. http://dx.doi.org/10.34089/jknr.2022.6.2.91.

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Purpose : The purpose of this study is to examine the influencing of quarantine experience on depression of subjective health status, fear of COVID-19 (coronavirus disease-19), and belief in COVID-19 response ability. Methods : This study was conducted with 204,424 people with quarantine experience who participated in the 2020 CHS (Community Health Survey). Data were analyzed with the x² test, t-test and multiple logistic regression using the SAS 9.4. Results : The factors associated with depression were age(CI＝1.72-2.37), Social support during quarantine(CI＝1.13-1.93), Subjective health status(CI＝1.74-5.33). As for sociodemographic characteristics, age, social support during quarantine and subjective health status were factors influencing depression. As for COVID-19 characteristics, fear of COVID-19 and belief in ability to respond to COVID-19 were identified as factors affecting depression. Conclusion : It is necessary to prepare a system for screening people who are vulnerable to mental health and a system for tracking and managing mental health after quarantine.

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Luo, ZongWei, Martin Lai, Mary Cheung, ShuiHua Han, Tianle Zhang, Zhongjun Luo, James Ting, et al. "Developing Local Association Network Based IoT Solutions for Body Parts Tagging and Tracking." International Journal of Systems and Service-Oriented Engineering 1, no. 4 (October 2010): 42–64. http://dx.doi.org/10.4018/jssoe.2010100104.

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Traditional Internet is commonly wired with machine to machine persistent connections. Evolving towards mobile and wireless pervasive networks, Internet has to entertain dynamic, transient, and changing interconnections. The vision of the Internet of Things furthers technology development by creating an interactive environment where smart objects are connected and can sense and react to the environment. Adopting such an innovative technology often requires extensive intelligence research. A major value indicator is how the potentials of RFID can translate into actions to improve business operational efficiency (Luo et al., 2008). In this paper, the authors will introduce a local association network with a coordinated P2P message delivery mechanism to develop Internet of Things based solutions body parts tagging and tracking. On site testing and performance evaluation validate the proposed approach. User feedback strengthens the belief that the proposed approach would help facilitate the technology adoption in body parts tagging and tracking.

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Cao, Lin, Danyang Zheng, Zongmin Zhao, Tao Wang, Dongfeng Wang, Chong Fu, and Jianfeng Gu. "Convex Variational Inference for Multi-Hypothesis Fractional Belief Propagation Based Data Association in Multiple Target Tracking Systems." IEEE Sensors Journal 21, no. 17 (September 1, 2021): 19121–33. http://dx.doi.org/10.1109/jsen.2021.3089206.

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Yu, Yihua, and Yuan Liang. "Multisensor-multitarget tracking based on belief propagation against false data injection attacks and denial of service attacks." Digital Signal Processing 126 (June 2022): 103502. http://dx.doi.org/10.1016/j.dsp.2022.103502.

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