Dopamine potentially unites two important roles: one in addiction, being involved in most substances of abuse including alcohol, and a second one in a specific type of learning, namely model-free temporal-difference r...
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In Fall 2014, a large Midwestern land-grant research university piloted a competency-based model as the foundation for an undergraduate transdisciplinary program focusing on connecting engineering and technology with ...
This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019
This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019
Many applications, such as human action recognition and object detection, can be formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most widely used approaches for multiclass classifica...
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Many applications, such as human action recognition and object detection, can be formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most widely used approaches for multiclass classification due to its simplicity and excellent performance. However, many confusing classes in such applications will degrade its results. For example, hand clap and boxing are two confusing actions. Hand clap is easily misclassified as boxing, and vice versa. Therefore, precisely classifying confusing classes remains a challenging task. To obtain better performance for multiclass classifications that have confusing classes, we first develop a classifier chain model for multiclass classification (CCMC) to transfer class information between classifiers. Then, based on an analysis of our proposed model, we propose an easy-to-hard learning paradigm for multiclass classification to automatically identify easy and hard classes and then use the predictions from simpler classes to help solve harder classes. Similar to CCMC, the classifier chain (CC) model is also proposed by Read et al. (2009) to capture the label dependency for multi-label classification. However, CC does not consider the order of di_culty of the labels and achieves degenerated performance when there are many confusing labels. Therefore, it is non-trivial to learn the appropriate label order for CC. Motivated by our analysis for CCMC, we also propose the easy-to-hard learning paradigm for multi-label classification to automatically identify easy and hard labels, and then use the predictions from simpler labels to help solve harder labels. We also demonstrate that our proposed strategy can be successfully applied to a wide range of applications, such as ordinal classification and relationship prediction. Extensive empirical studies validate our analysis and the efiectiveness of our proposed easy-to-hard learning strategies.
Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients...
Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients. All of this system can be realization by using cryptography. LUC algorithms are introduced by Smith and Lennon in 1993. LUC algorithm is an algorithm that based on the use of Lucas sequence (specific arithmetic operations derived from Lucas row) that rarely used to enhance the security of the messages. In this research, LUC algorithm is combined with visual cryptography to process the encryption and the description of a colored image. Four different images were used in the trial here. The performance of the system is assessed using Structural similarity (SSIM) which has an assessment similar to human eye. If the image quality is the same as the original image, then the SSIM value is one. Whereas if the quality of decryption image is very different from the original image then the value of SSIM is zero.
This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019
This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019
Unlike upper-extremity robotic therapy, robotic therapy of lower extremities has not matched the effectiveness of human-administered approaches. We hypothesize that this may stem from inadvertent interference with nat...
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ISBN:
(纸本)9781509032884
Unlike upper-extremity robotic therapy, robotic therapy of lower extremities has not matched the effectiveness of human-administered approaches. We hypothesize that this may stem from inadvertent interference with natural movement control and investigated the oscillatory dynamics of human locomotion. Specifically, we assessed gait entrainment to periodic mechanical perturbations. Because the treadmills used in most studies necessarily interact with the dynamics of natural walking, we compared our experimental intervention during gait entrainment in treadmill and overground walking. Fourteen healthy subjects walked overground and on a treadmill while wearing an exoskeletal ankle robot which exerted short plantarflexion torque pulses at periods 50 ms shorter or longer than the subjects' preferred stride period. Entrainment to the periodic perturbation occurred in all conditions, however more readily in overground walking. In all cases, the stride period phase-locked with the torque pulse at `push-off' such that it assisted propulsion. This entrainment of the stride period and its sensitivity to context indicate the subtlety and adaptability of human walking. Our observations suggest new avenues for gait rehabilitation and implications for exoskeleton design and legged locomotion research.
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