A visual anthropomorphic agent with learning capability of cooperative answering strategy through speech dialog

2002 ◽  
Author(s):  
Y. Takama ◽  
H. Dohi ◽  
M. Ishizuka
Keyword(s):  
Learning Capability ◽  
Cooperative Answering

The Impact of Transformational Leadership on Entrepreneurial Orientation via the Mediating Role of Organizational Learning Capability

2020 ◽  
Vol 8 (2) ◽  
pp. 65
Author(s):  
Razan Nofal ◽  
Mais Jaradat
Keyword(s):  
Transformational Leadership ◽  
Organizational Learning ◽  
Entrepreneurial Orientation ◽  
Commercial Banks ◽  
Quantitative Research ◽  
Intellectual Stimulation ◽  
Design Data ◽  
Learning Capability ◽  
Idealized Influence ◽  
The Impact

The current research aims to investigate the effect of transformational leadership on entrepreneurial orientation in Jordanian commercial banks, and whether organizational learning capability mediates the effect of transformational leadership on entrepreneurial orientation. Adopting a quantitative research design, data were collected by means of a questionnaire-based survey of employees in Jordanian commercial banks. Based on 330 usable responses, the results revealed the significant effect of transformational leadership and two of its dimensions (inspirational motivation and intellectual stimulation) on entrepreneurial orientation. Two other dimensions (idealized influence and individualized consideration) did not contribute to entrepreneurial orientation. Additionally, the results showed that transformational leadership has a significant effect on organizational learning capability, and that organizational learning capability in turn affects entrepreneurial orientation. The findings confirm that organizational learning capability fully mediates the effect of transformational leadership on entrepreneurial orientation. A number of recommendations are advanced, the most important of which is that banks should improve and develop managers’ transformational attributes by training them on how to deal with employees in order to increase their entrepreneurial orientation. Banks should also consider improving their learning capability, as this plays a significant role in enhancing and supporting the effect of transformational leadership attributes on entrepreneurial orientation.

Ameliorating Effects of Lithium on the Perinatal Ethanol-Induced Behavioral and Cognitive Dysfunction and Brain Oxidative Stress in Postnatal Developing Mice Pups

2020 ◽  
Vol 21 (13) ◽  
pp. 1325-1332
Author(s):  
Mohammad Ahmad ◽  
Gasem M. Abu Taweel
Keyword(s):  
Oxidative Stress ◽  
Brain Tissue ◽  
Emotional Dysregulation ◽  
Learning Capability ◽  
Weaning Age ◽  
Pregnant Mice ◽  
Term Basis ◽  
Significant Deficit ◽  
Morphological Indices ◽  
And Oxidative Stress

Background: Developmental ethanol (EtOH) exposure can cause lifelong behavioral hyperactivity, cognitive deficits, emotional dysregulation, and more. However, co-treatment with lithium (Li) on the day of EtOH exposure prevents many of the impairments. Methods: Experimental groups of pregnant mice were exposed to EtOH (20% v/v solution at a dose of 2.5 g/kg) in their drinking water and the animals were treated with Li (15 and 30 mg/kg) through IP injection on gestational days14, 16, 18, and 20, and post-natal days (PD) 3, 5, 7, and 9. All treatments with EtOH and exposure to Li doses to pregnant mice started on gestational day 14 and continued until post-natal day 9 (PD9). The effects on some developing morphological indices, nerve reflexes during weaning age, and various cognitive dysfunctions at adolescent ages and biochemical changes in the brain tissue indices of below-mentioned neurotransmitters and oxidative stress in post-natal developing offspring at adolescent age, were studied. Results: Perinatal exposure to EtOH in pregnant mice resulted in several postnatal developing and morphological indices in the developing male pups during their weaning period, like gain in their body weight, delay in appearance of their body hair fuzz and opening of their eyes, and disruptions in their developing motor reflexes. Discussion: During adolescent age, a significant deficit in their learning capability and cognitive behavior, decline in the neurochemical DA and 5-HT in their brain and some indices of oxidative stress TBARS, GSH, GST, CAT, and SOD was observed. Conclusion: These results indicate that Li ameliorates significantly and dose-dependently EtOH induced developmental toxicities like morphological developments and dysfunctions in cognitive retention and oxidative stress on a long-term basis in brain tissue. However, further detailed studies are required for the clinical use of as an ameliorating agent for perinatal EtOH induced dysfunctions.

Fusing Dynamic Images and Depth Motion Maps for Action Recognition in Surveillance Systems

2019 ◽  
Vol 09 ◽  
Author(s):  
Rajat Khurana ◽  
Alok Kumar Singh Kushwaha
Keyword(s):  
Action Recognition ◽  
Depth Map ◽  
Good Choice ◽  
Surveillance Systems ◽  
Activity Detection ◽  
Learning Capability ◽  
Proposed Model ◽  
Care Activity ◽  
Increasing Demand ◽  
Depth Motion Maps

Background & Objective: Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. Motivation of this work is to use most out of the data generated from sensors and use them for recognition of classes. Recognition of actions from videos sequences is a growing field with the upcoming trends of deep neural networks. Automatic learning capability of Convolutional Neural Network (CNN) make them good choice as compared to traditional handcrafted based approaches. With the increasing demand of RGB-D sensors combination of RGB and depth data is in great demand. This work comprises of the use of dynamic images generated from RGB combined with depth map for action recognition purpose. We have experimented our approach on pre trained VGG-F model using MSR Daily activity dataset and UTD MHAD Dataset. We achieve state of the art results. To support our research, we have calculated different parameters apart from accuracy such as precision, F score, recall. Conclusion: Accordingly, the investigation confirms improvement in term of accuracy, precision, F-Score and Recall. The proposed model is 4 Stream model is prone to occlusion, used in real time and also the data from the RGB-D sensor is fully utilized.

scholarly journals Explaining Neural Networks Using Attentive Knowledge Distillation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1280
Author(s):  
Hyeonseok Lee ◽  
Sungchan Kim
Keyword(s):  
Neural Networks ◽  
Model Prediction ◽  
Saliency Map ◽  
Model Parameters ◽  
Learning Capability ◽  
Fine Grained ◽  
Network Layers ◽  
Saliency Maps ◽  
Novel Approach ◽  
Knowledge Distillation

Explaining the prediction of deep neural networks makes the networks more understandable and trusted, leading to their use in various mission critical tasks. Recent progress in the learning capability of networks has primarily been due to the enormous number of model parameters, so that it is usually hard to interpret their operations, as opposed to classical white-box models. For this purpose, generating saliency maps is a popular approach to identify the important input features used for the model prediction. Existing explanation methods typically only use the output of the last convolution layer of the model to generate a saliency map, lacking the information included in intermediate layers. Thus, the corresponding explanations are coarse and result in limited accuracy. Although the accuracy can be improved by iteratively developing a saliency map, this is too time-consuming and is thus impractical. To address these problems, we proposed a novel approach to explain the model prediction by developing an attentive surrogate network using the knowledge distillation. The surrogate network aims to generate a fine-grained saliency map corresponding to the model prediction using meaningful regional information presented over all network layers. Experiments demonstrated that the saliency maps are the result of spatially attentive features learned from the distillation. Thus, they are useful for fine-grained classification tasks. Moreover, the proposed method runs at the rate of 24.3 frames per second, which is much faster than the existing methods by orders of magnitude.

scholarly journals Electromagnetic Actuator System Using Witty Control System

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 65
Author(s):  
Der-Fa Chen ◽  
Shen-Pao-Chi Chiu ◽  
An-Bang Cheng ◽  
Jung-Chu Ting
Keyword(s):  
Control System ◽  
Control Method ◽  
Search Algorithm ◽  
Electromagnetic Actuator ◽  
Control Performance ◽  
Learning Capability ◽  
Learning Rates ◽  
Agricultural Applications ◽  
Actuator System ◽  
Rice Milling

Electromagnetic actuator systems composed of an induction servo motor (ISM) drive system and a rice milling machine system have widely been used in agricultural applications. In order to achieve a finer control performance, a witty control system using a revised recurrent Jacobi polynomial neural network (RRJPNN) control and two remunerated controls with an altered bat search algorithm (ABSA) method is proposed to control electromagnetic actuator systems. The witty control system with finer learning capability can fulfill the RRJPNN control, which involves an attunement law, two remunerated controls, which have two evaluation laws, and a dominator control. Based on the Lyapunov stability principle, the attunement law in the RRJPNN control and two evaluation laws in the two remunerated controls are derived. Moreover, the ABSA method can acquire the adjustable learning rates to quicken convergence of weights. Finally, the proposed control method exhibits a finer control performance that is confirmed by experimental results.

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