Ensemble adversarial black-box attacks against deep learning systems

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

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Optimization of Stochastic Computing Based Deep Learning Systems with Parallel Finite State Machine Implementation

Proceedings of the 2020 4th International Conference on Algorithms, Computing and Systems ◽

10.1145/3423390.3426727 ◽

2020 ◽

Author(s):

Jinjie Liu

Keyword(s):

Deep Learning ◽

Finite State Machine ◽

State Machine ◽

Learning Systems ◽

Stochastic Computing ◽

Finite State

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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽

10.3390/s21072514 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2514

Author(s):

Tharindu Kaluarachchi ◽

Andrew Reis ◽

Suranga Nanayakkara

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Review Paper ◽

Learning Systems ◽

Learning Approaches ◽

Application Development ◽

Research Gaps ◽

Domain Experts ◽

Working Definition ◽

Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽

10.3390/e23010018 ◽

2020 ◽

Vol 23 (1) ◽

pp. 18

Author(s):

Pantelis Linardatos ◽

Vasilis Papastefanopoulos ◽

Sotiris Kotsiantis

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Black Box ◽

Learning Systems ◽

Model Complexity ◽

Learning Models ◽

New Methods ◽

Industrial Adoption ◽

Machine Learning Models ◽

The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

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Deep Learning Insights into Lanthanides Complexation Chemistry

Molecules ◽

10.3390/molecules26113237 ◽

2021 ◽

Vol 26 (11) ◽

pp. 3237

Author(s):

Artem A. Mitrofanov ◽

Petr I. Matveev ◽

Kristina V. Yakubova ◽

Alexandru Korotcov ◽

Boris Sattarov ◽

...

Keyword(s):

Deep Learning ◽

Stability Constants ◽

Black Box ◽

Lanthanide Ions ◽

Structure Property ◽

Target Property ◽

Mutual Location ◽

Molecule Structure ◽

Complexation Chemistry ◽

Main Influence

Modern structure–property models are widely used in chemistry; however, in many cases, they are still a kind of a “black box” where there is no clear path from molecule structure to target property. Here we present an example of deep learning usage not only to build a model but also to determine key structural fragments of ligands influencing metal complexation. We have a series of chemically similar lanthanide ions, and we have collected data on complexes’ stability, built models, predicting stability constants and decoded the models to obtain key fragments responsible for complexation efficiency. The results are in good correlation with the experimental ones, as well as modern theories of complexation. It was shown that the main influence on the constants had a mutual location of the binding centers.

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Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine

Oral Radiology ◽

10.1007/s11282-021-00538-2 ◽

2021 ◽

Author(s):

Mizuho Mori ◽

Yoshiko Ariji ◽

Motoki Fukuda ◽

Tomoya Kitano ◽

Takuma Funakoshi ◽

...

Keyword(s):

Deep Learning ◽

Characteristic Curve ◽

Classification Performance ◽

Learning Systems ◽

Learning Technology ◽

System 2 ◽

Potential Benefits ◽

System 1 ◽

Periapical Radiography

Abstract Objectives The aim of the present study was to create and test an automatic system for assessing the technical quality of positioning in periapical radiography of the maxillary canines using deep learning classification and segmentation techniques. Methods We created and tested two deep learning systems using 500 periapical radiographs (250 each of good- and bad-quality images). We assigned 350, 70, and 80 images as the training, validation, and test datasets, respectively. The learning model of system 1 was created with only the classification process, whereas system 2 consisted of both the segmentation and classification models. In each model, 500 epochs of training were performed using AlexNet and U-net for classification and segmentation, respectively. The segmentation results were evaluated by the intersection over union method, with values of 0.6 or more considered as success. The classification results were compared between the two systems. Results The segmentation performance of system 2 was recall, precision, and F measure of 0.937, 0.961, and 0.949, respectively. System 2 showed better classification performance values than those obtained by system 1. The area under the receiver operating characteristic curve values differed significantly between system 1 (0.649) and system 2 (0.927). Conclusions The deep learning systems we created appeared to have potential benefits in evaluation of the technical positioning quality of periapical radiographs through the use of segmentation and classification functions.

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In Machines We Trust: Are Robo-Advisers More Trustworthy Than Human Financial Advisers?

Law, Technology and Humans ◽

10.5204/lthj.v1i0.1261 ◽

2019 ◽

pp. 129-141 ◽

Cited By ~ 1

Author(s):

Hui Xian Chia

Keyword(s):

Deep Learning ◽

Black Box ◽

Best Interests ◽

Decision Making Process ◽

Learning Technology ◽

Technical Expertise ◽

Learning Agents ◽

The People ◽

Self Interest ◽

Deep Learning Model

This article examines the use of artificial intelligence (AI) and deep learning, specifically, to create financial robo-advisers. These machines have the potential to be perfectly honest fiduciaries, acting in their client’s best interests without conflicting self-interest or greed, unlike their human counterparts. However, the application of AI technology to create financial robo-advisers is not without risk. This article will focus on the unique risks posed by deep learning technology. One of the main fears regarding deep learning is that it is a “black box”, its decision-making process is opaque and not open to scrutiny even by the people who developed it. This poses a significant challenge to financial regulators, whom would not be able to examine the underlying rationale and rules of the robo-adviser to determine its safety for public use. The rise of deep learning has been met with calls for ‘explainability’ of how deep learning agents make their decisions. This paper argues that greater explainability can be achieved by describing the ‘personality’ of deep learning robo-advisers, and further proposes a framework for describing the parameters of the deep learning model using concepts that can be readily understood by people without technical expertise. This regards whether the robo-adviser is ‘greedy’, ‘selfish’ or ‘prudent’. Greater understanding will enable regulators and consumers to better judge the safety and suitability of deep learning financial robo-advisers.

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