scholarly journals Complex Risk Statistics with Scenario Analysis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Fei Sun ◽  
Yichuan Dong
Keyword(s):  
Neural Networks ◽  
Risk Management ◽  
Scenario Analysis ◽  
Special Class ◽  
Intelligent Systems ◽  
Deep Neural Networks ◽  
Critical Factor ◽  
New Approach ◽  
Dual Representations

Complex risk is a critical factor for both intelligent systems and risk management. In this paper, we consider a special class of risk statistics, named complex risk statistics. Our result provides a new approach for addressing complex risk, especially in deep neural networks. By further developing the properties related to complex risk statistics, we are able to derive dual representations for such risk.

scholarly journals Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour

2019 ◽  
Author(s):  
Andrea Aler Tubella ◽  
Andreas Theodorou ◽  
Frank Dignum ◽  
Virginia Dignum
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Intelligent Systems ◽  
Deep Neural Networks ◽  
Well Being ◽  
Moral Values ◽  
Human Values ◽  
Agent Based ◽  
Multiple Domains ◽  
Inputs And Outputs

Artificial Intelligence (AI) applications are being used to predict and assess behaviour in multiple domains which directly affect human well-being. However, if AI is to improve people’s lives, then people must be able to trust it, by being able to understand what the system is doing and why. Although transparency is often seen as the requirement in this case, realistically it might not always be possible, whereas the need to ensure that the system operates within set moral bounds remains. In this paper, we present an approach to evaluate the moral bounds of an AI system based on the monitoring of its inputs and outputs. We place a ‘Glass-Box’ around the system by mapping moral values into explicit verifiable norms that constrain inputs and outputs, in such a way that if these remain within the box we can guarantee that the system adheres to the value. The focus on inputs and outputs allows for the verification and comparison of vastly different intelligent systems; from deep neural networks to agent-based systems. The explicit transformation of abstract moral values into concrete norms brings great benefits in terms of explainability; stakeholders know exactly how the system is interpreting and employing relevant abstract moral human values and calibrate their trust accordingly. Moreover, by operating at a higher level we can check the compliance of the system with different interpretations of the same value.

scholarly journals Cryptospace Invertible Steganography with Conditional Generative Adversarial Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ching-Chun Chang
Keyword(s):  
Neural Networks ◽  
Intelligent Systems ◽  
Deep Neural Networks ◽  
Binary Classification ◽  
Classification Problem ◽  
Generative Adversarial Networks ◽  
Discrimination Function ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Bit Planes

Deep neural networks have become the foundation of many modern intelligent systems. Recently, the author has explored adversarial learning for invertible steganography (ALIS) and demonstrated the potential of deep neural networks to reinvigorate an obsolete invertible steganographic method. With the worldwide popularisation of the Internet of things and cloud computing, invertible steganography can be recognised as a favourable way of facilitating data management and authentication due to the ability to embed information without causing permanent distortion. In light of growing concerns over cybersecurity, it is important to take a step forwards to investigate invertible steganography for encrypted data. Indeed, the multidisciplinary research in invertible steganography and cryptospace computing has received considerable attention. In this paper, we extend previous work and address the problem of cryptospace invertible steganography with deep neural networks. Specifically, we revisit a seminal work on cryptospace invertible steganography in which the problem of message decoding and image recovery is viewed as a type of binary classification. We formulate a general expression encompassing spatial, spectral, and structural analyses towards this particular classification problem and propose a novel discrimination function based on a recurrent conditional generative adversarial network (RCGAN) which predicts bit-planes with stacked neural networks in a top-down manner. Experimental results evaluate the performance of various discrimination functions and validate the superiority of neural-network-aided discrimination function in terms of classification accuracy.

scholarly journals Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1560 ◽  
Author(s):  
DaeHan Ahn ◽  
Ji-Young Choi ◽  
Hee-Chul Kim ◽  
Jeong-Seok Cho ◽  
Kwang-Deog Moon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Low Cost ◽  
Estimation Accuracy ◽  
Weight Changes ◽  
New Approach ◽  
Comprehensive Performance ◽  
Increasing Demand

There is an increasing demand for acquiring details of food nutrients especially among those who are sensitive to food intakes and weight changes. To meet this need, we propose a new approach based on deep learning that precisely estimates the composition of carbohydrates, proteins, and fats from hyperspectral signals of foods obtained by using low-cost spectrometers. Specifically, we develop a system consisting of multiple deep neural networks for estimating food nutrients followed by detecting and discarding estimation anomalies. Our comprehensive performance evaluation demonstrates that the proposed system can maximize estimation accuracy by automatically identifying wrong estimations. As such, if consolidated with the capability of reinforcement learning, it will likely be positioned as a promising means for personalized healthcare in terms of food safety.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks
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