Brainware Computing: Concepts, Scopes and Challenges

Huh, Eui‐Nam; Hossain, Imtiaz

doi:10.3390/app11115303

Cited by 7 publications

(2 citation statements)

References 34 publications

(57 reference statements)

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“…An example is proposed by Bogue (2022), where the author discusses cobots importance and some application linked to artificial intelligence enabled robots. By considering the same topics, also a computer architecture is proposed by Huh and Hossain (2021).…”

Section: Artificial Intelligence (Ai) and Machine Learning (Ml)mentioning

confidence: 99%

Human factors in cobot era: a review of modern production systems features

et al. 2022

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Collaborative robots are increasingly common in modern production systems, since they allow to merge the productivity of automated systems with the flexibility and dexterity of manual ones. The direct interaction between the human and the robot can be the greatest advantage and the greatest limit of collaborative systems at the same time, depending on how it affects human factors like ergonomics and mental stress. This work presents an overview of collaborative robotics considering three main dimensions: robot features, modern production systems characteristics and human factors. A literature review on how such dimensions interact is addressed and a discussion on the current state of the art is presented, showing the topics that have been already widely explored and the research gaps that should be fulfilled in the future.

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Section: Artificial Intelligence (Ai) and Machine Learning (Ml)mentioning

confidence: 99%

Human factors in cobot era: a review of modern production systems features

et al. 2022

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“…However, the heavy and complex deep architecture demands high computational resources and energy consumption. This fact makes the large and complex deep architectures unsuitable to deploy at low computational resource-based and real-time applications such as 5G mobile communication and edge computing devices [20].…”

Section: Introductionmentioning

confidence: 99%

NeuRes: Highly Activated Neurons Responses Transfer via Distilling Sparse Activation Maps

Akhter

Hossain

et al. 2022

IEEE Access

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In recent years, Knowledge Distillation has obtained a significant interest in deep learningbased applications on mobile and IoT devices due to its ability to transfer knowledge from the large and complex teacher to the lightweight student network. Intuitively, Knowledge Distillation refers to forcing the student to mimic the teacher's neuron responses to improve the generalization of the student by deploying the distillation losses as the regularization terms. However, the non-linearity of the hidden layers and the high dimensionality of the feature maps make the knowledge transfer a rigorous task. Though numerous methods have been proposed to transfer the teacher's neuron responses in the form of diverse feature characteristics such as attention, contrastive representation, and so on, to the best of our knowledge, no prior works considered feature-level non-linearity during distillation. In this work, we ask, does feature-level non-linearity-based approaches can improve student performance? To investigate this concern, we propose a novel knowledge distillation technique called the NeuRes (Neuron's Responses) via distilling the Sparse Activation Maps (SAMs) to transfer the highly activated Neurons Responses to the student to enhance the representation capability. Proposed NeuRes selects the highly activated neuron responses that produce Sparse Activation Maps (SAMs) while transferring the knowledge based on activation normalization. Our proposed NeuRes also transfers the translation invariant features using auxiliary classifiers and augmented data to improve students' generalization. The details ablation studies and extensive experiments on model compression, transferability, adversarial robustness, and few-shot learning verify that NeuRes performs better compared to the existing state-of-the-art distillation techniques on the standard benchmark datasets.

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