Monthly Archives: October 2017

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Cognitive informatics: simulation of cognition through direct simulation of neurons

Shivhare, R., Cherukuri, A.K. & Li, Establishment of Cognitive Relations Based on Cognitive Informatics, J. Cogn Comput (2017) 9: 721, DOI: 10.1007/s12559-017-9498-9.

Cognitive informatics (CI) is an interdisciplinary study on modelling of the brain in terms of knowledge and information processing. In CI, objects/attributes are considered as neurons connected to each other via synapse. The relation represents the synapse in CI. In order to represent new information the brain generates new synapse or relation between the existing neurons. Therefore, the establishment of cognitive relations is essential to represent new information. In order to represent new information, we propose an algorithm which creates cognitive relation between the pair of objects and attributes by using the relational attribute and object method. Further, the cognitive relations between the pair of objects or attributes within the context could be checked with newly defined conditions, i.e. the necessary and sufficient condition. These conditions will evaluate whether the relational object and attribute is adequate to have relations between the pair of objects and attributes. The new information is obtained without increasing the number of neurons in brain. It is achieved by creating cognitive relations between the pair of objects and attributes. The obtained results are beneficial to simulate the intelligence behaviour of brain such as learning and memorizing. Integrating the idea of CI into cognitive relations is a promising and challenging research direction. In this paper, we have discussed it from the aspects of cognitive mechanism, cognitive computing and cognitive process.

On the problem of the future limits of information storage

Cambria, E., Chattopadhyay, A., Linn, E. et al, Storages Are Not Forever, Cogn Comput (2017) 9: 646, DOI: 10.1007/s12559-017-9482-4.

Not unlike the concern over diminishing fossil fuel, information technology is bringing its own share of future worries. We chose to look closely into one concern in this paper, namely the limited amount of data storage. By a simple extrapolatory analysis, it is shown that we are on the way to exhaust our storage capacity in less than two centuries with current technology and no recycling. This can be taken as a note of caution to expand research initiative in several directions: firstly, bringing forth innovative data analysis techniques to represent, learn, and aggregate useful knowledge while filtering out noise from data; secondly, tap onto the interplay between storage and computing to minimize storage allocation; thirdly, explore ingenious solutions to expand storage capacity. Throughout this paper, we delve deeper into the state-of-the-art research and also put forth novel propositions in all of the abovementioned directions, including space- and time-efficient data representation, intelligent data aggregation, in-memory computing, extra-terrestrial storage, and data curation. The main aim of this paper is to raise awareness on the storage limitation we are about to face if current technology is adopted and the storage utilization growth rate persists. In the manuscript, we propose some storage solutions and a better utilization of storage capacity through a global DIKW hierarchy.

An open-source implementation of visual SLAM with a very nice related-work section

R. Mur-Artal and J. D. Tardós, ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras, IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255-1262, DOI: 10.1109/TRO.2017.2705103.

We present ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities. The system works in real time on standard central processing units in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end, based on bundle adjustment with monocular and stereo observations, allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches with map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.

Example of learning a Bayesian network using expert knowledge

H. Amirkhani, M. Rahmati, P. J. F. Lucas and A. Hommersom, Exploiting Experts’ Knowledge for Structure Learning of Bayesian Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 11, pp. 2154-2170, DOI: 10.1109/TPAMI.2016.2636828.

Learning Bayesian network structures from data is known to be hard, mainly because the number of candidate graphs is super-exponential in the number of variables. Furthermore, using observational data alone, the true causal graph is not discernible from other graphs that model the same set of conditional independencies. In this paper, it is investigated whether Bayesian network structure learning can be improved by exploiting the opinions of multiple domain experts regarding cause-effect relationships. In practice, experts have different individual probabilities of correctly labeling the inclusion or exclusion of edges in the structure. The accuracy of each expert is modeled by three parameters. Two new scoring functions are introduced that score each candidate graph based on the data and experts’ opinions, taking into account their accuracy parameters. In the first scoring function, the experts’ accuracies are estimated using an expectation-maximization-based algorithm and the estimated accuracies are explicitly used in the scoring process. The second function marginalizes out the accuracy parameters to obtain more robust scores when it is not possible to obtain a good estimate of experts’ accuracies. The experimental results on simulated and real world datasets show that exploiting experts’ knowledge can improve the structure learning if we take the experts’ accuracies into account.

Dealing with nonlinearities in Kalman filters through Monte Carlo modelling for minimizing divergence

S. Gultekin and J. Paisley, Nonlinear Kalman Filtering With Divergence Minimization, IEEE Transactions on Signal Processing, vol. 65, no. 23, pp. 6319-6331, DOI: 10.1109/TSP.2017.2752729.

We consider the nonlinear Kalman filtering problem using Kullback-Leibler (KL) and α-divergence measures as optimization criteria. Unlike linear Kalman filters, nonlinear Kalman filters do not have closed form Gaussian posteriors because of a lack of conjugacy due to the nonlinearity in the likelihood. In this paper, we propose novel algorithms to approximate this posterior by optimizing the forward and reverse forms of the KL divergence, as well as the α-divergence that contains these two as limiting cases. Unlike previous approaches, our algorithms do not make approximations to the divergences being optimized, but use Monte Carlo techniques to derive unbiased algorithms for direct optimization. We assess performance on radar and sensor tracking, and options pricing, showing general improvement over the extended, unscented, and ensemble Kalman filters, as well as competitive performance with particle filtering.

Empirical evidence of the negative correlation between cognitive workload and attention in humans

Kyle J. Jaquess, Rodolphe J. Gentili, Li-Chuan Lo, Hyuk Oh, Jing Zhang, Jeremy C. Rietschel, Matthew W. Miller, Ying Ying Tan, Bradley D. Hatfield, Empirical evidence for the relationship between cognitive workload and attentional reserve, International Journal of Psychophysiology, Volume 121, 2017, Pages 46-55, DOI: 10.1016/j.ijpsycho.2017.09.007.

While the concepts of cognitive workload and attentional reserve have been thought to have an inverse relationship for some time, such a relationship has never been empirically tested. This was the purpose of the present study. Aspects of the electroencephalogram were used to assess both cognitive workload and attentional reserve. Specifically, spectral measures of cortical activation were used to assess cognitive workload, while amplitudes of the event-related potential from the presentation of unattended “novel” sounds were used to assess attentional reserve. The relationship between these two families of measures was assessed using canonical correlation. Twenty-seven participants performed a flight simulator task under three levels of challenge. Verification of manipulation was performed using self-report measures of task demand, objective task performance, and heart rate variability using electrocardiography. Results revealed a strong, negative relationship between the spectral measures of cortical activation, believed to be representative of cognitive workload, and ERP amplitudes, believed to be representative of attentional reserve. This finding provides support for the theoretical and intuitive notion that cognitive workload and attentional reserve are inversely related. The practical implications of this result include improved state classification using advanced machine learning techniques, enhanced personnel selection/recruitment/placement, and augmented learning/training.