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Deep learning models have achieved high performance across different domains, such as medical decision-making, autonomous vehicles, decision support systems, among many others. However, despite this success, the inner mechanisms of these models are opaque because their internal representations are too complex for a human to understand. This opacity makes it hard to understand the how or the why of the predictions of deep learning models. There has been a growing interest in model-agnostic methods that make deep learning models more transparent and explainable to humans. Some researchers recently argued that for a machine to achieve human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing literature on counterfactuals and causability for explainable artificial intelligence (AI). We performed a Latent Dirichlet topic modelling analysis (LDA) under a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to find the most relevant literature articles. This analysis yielded a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications to real-world data. Our research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Furthermore, our findings suggest that the explanations derived from popular algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous, or even biased explanations. Thus, this paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable AI. • A survey on model-agnostic counterfactual approaches for XAI. • A novel taxonomy for model-agnostic counterfactual approaches for XAI. • A set of properties for causability systems for XAI. • Opportunities and challenges for causability systems based on modelagnostic counterfactual approaches. [ABSTRACT FROM AUTHOR]

• The current 3D human body reconstruction technology requires a large number of 3D scanning equipment and a complex dynamic capture process, which significantly limits its rapid adoption in these application domains. The purpose of this article is to simplify the process of 3D reconstruction. • By combining deep learning methods, an end-to-end 3D human body reconstruction framework is proposed to achieve the goal of reconstructing a high-precision 3D human body model based on RGB images. [Display omitted] In recent years, with the improvement of artificial intelligence technology, it has become possible to reconstruct high-precision 3D human body models based on ordinary RGB images. The current 3D human body reconstruction technology requires complex external equipment to scan all angles of the human body, which is complicated to be implemented and cannot be popularized. In order to solve this problem, this paper applies deep learning models on reconstructing 3D human body based on monocular images. First of all, this paper uses Stacked Hourglass network to perform convolution operations on monocular images collected from different views. Then Multi-Layer Perceptrons (MLPs) are used to decode the encoded high-level images. The feature codes in the two views(main and side) are fused, and the interior and exterior points are classified by the fusion features, so as to obtain the corresponding 3D occupancy field. At last, the Marching Cube algorithm is used for 3D reconstruction with a specific threshold and then we use Laplace smoothing algorithm to remove artifacts. This paper proposes a dense sampling strategy based on the important joint points of the human body, which has a certain optimization effect on the realization of high-precision 3D reconstruction. The performance of the proposed scheme has been validated on the open source datasets, MGN dataset and the THuman dataset, provided by Tsinghua University. The proposed scheme can reconstruct features such as clothing folds, color textures, and facial details,and has great potential to be applied in different applications. [ABSTRACT FROM AUTHOR]

This paper illustrates the efficacy of an artificial intelligence (AI) (a convolutional neural network, based on the U-Net), for the burn-depth assessment using semantic segmentation of polarized high-performance light camera images of burn wounds. The proposed method is evaluated for paediatric scald injuries to differentiate four burn wound depths: superficial partial-thickness (healing in 0-7 days), superficial to intermediate partial-thickness (healing in 8-13 days), intermediate to deep partial-thickness (healing in 14-20 days), deep partial-thickness (healing after 21 days) and full-thickness burns, based on observed healing time. In total 100 burn images were acquired. Seventeen images contained all 4 burn depths and were used to train the network. Leave-one-out cross-validation reports were generated and an accuracy and dice coefficient average of almost 97% was then obtained. After that, the remaining 83 burn-wound images were evaluated using the different network during the cross-validation, achieving an accuracy and dice coefficient, both on average 92%. This technique offers an interesting new automated alternative for clinical decision support to assess and localize burn-depths in 2D digital images. Further training and improvement of the underlying algorithm by e.g., more images, seems feasible and thus promising for the future. [ABSTRACT FROM AUTHOR]

• Introduce the text detection models based on regression and segmentation according to their implementation methods. • Classify mainstream text recognition models and provide a detailed introduction. • Compare the performance of different text detection and recognition methods. • Discuss the future development trends of OCR. Optical Character Recognition (OCR) poses a crucial challenge within the realm of computer vision research, as it plays a pivotal role in converting vast amounts of unstructured text data into structured formats to support diverse artificial intelligence applications. The OCR process encompasses two core components: text detection and text recognition. Text detection involves identifying and extracting text regions, achieved through either object detection or segmentation techniques, while text recognition focuses on accurately deciphering the content within these identified regions. In recent years, remarkable strides have been made in the domain of text recognition, primarily driven by deep learning-based models. These models eliminate the need for manual feature processing and excel in recognizing text even within complex scenes, surpassing the performance of traditional text recognition methods and subsequently emerging as the dominant approach. The objective of this paper is to present a comprehensive survey of both text detection and text recognition models. Firstly, we systematically categorize and provide an overview of existing off-the-shelf text detection methods. Subsequently, we conduct an in-depth investigation of six distinct text recognition models, taking into account their unique implementations. Additionally, we explore and analyze the principal datasets that currently prevail in the field of text detection and recognition. Furthermore, this research entails a meticulous performance comparison of various text detection algorithms on the CTW1500, TotalText, and ICDAR2015 datasets. Additionally, we evaluate and scrutinize the efficacy of mainstream text recognition algorithms on the IIIT-5K, SVT, ICDAR2013, SVT-P, CUTE80, and ICDAR2015 datasets. Finally, we conclude with a discussion on the future development and research trends concerning text detection and recognition, providing insights that can further drive progress in this crucial area. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) is a promising technology to address the resource allocation problem for efficient data transmission in complex network environments. However, most DRL-based resource allocation algorithms suffer from limited feature extraction capabilities and lack scalability and generalization, especially in heterogeneous Industrial Internet of Things (IIoT) environments. In this paper, we develop a lifelong learning architecture that can integrate artificial intelligence (AI) algorithms into the heterogeneous IIoT network for efficient data transmission. Based on this, we propose an intelligent resource allocation algorithm based on dual-attention DRL (DADR) for forwarding node selection and channel access slot allocation in a specific network environment. The proposed DADR algorithm combines the advantages of multi-dimension convolutional attention and multi-head self-attention mechanisms. It can provide local- and global-feature fusion capabilities for distributed nodes while maximizing the performance of data transmission. Furthermore, we present a lifelong federated meta reinforcement learning (LFMRL) that can effectively utilize prior knowledge and enable the DRL agent quickly adapt to a new environment. Specifically, LFMRL adopts a federated meta learning-based knowledge fusion algorithm to fuse the knowledge of learned DADR algorithms and iteratively update the shared model, thereby improving the scalability and generalization of the shared model in heterogeneous IIoT environments. In addition, a simple and efficient knowledge transfer mechanism is enabled to accelerate the DRL model convergence by transferring the knowledge of the shared model to the new environment. Simulation results demonstrate the effectiveness of the proposed algorithms in terms of energy efficiency, data transmission reliability, and network stability. Compared to DADR and FedAvg algorithms, LFMRL algorithm can further reduce the energy consumption, training time, and average forwarding node switching times, while improving packet delivery rate to 99.2%. • A novel lifelong learning architecture for resource allocation in heterogeneous IIoT. • Dual-attention deep reinforcement learning provides local- and global- feature fusion. • Federated meta learning based knowledge fusion learns the commonness of models. • Reinforcement learning based knowledge transfer learns the properties of model. • Knowledge fusion and knowledge transfer provide lifelong learning ability for system. [ABSTRACT FROM AUTHOR]

Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field. [ABSTRACT FROM AUTHOR]

Background: The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over recent decades, there has been a steady increase in the number of research studies that are using machine learning (ML) to read or interrogate ECG data.Objective: The aim of this study is to review the use of ML with ECG data using a time series approach.Methods: Papers that address the subject of ML and the ECG were identified by systematically searching databases that archive papers from January 1995 to October 2019. Time series analysis was used to study the changing popularity of the different types of ML algorithms that have been used with ECG data over the past two decades. Finally, a meta-analysis of how various ML techniques performed for various diagnostic classifications was also undertaken.Results: A total of 757 papers was identified. Based on results, the use of ML with ECG data started to increase sharply (p < 0.001) from 2012. Healthcare applications, especially in heart abnormality classification, were the most common application of ML when using ECG data (p < 0.001). However, many new emerging applications include using ML and the ECG for biometrics and driver drowsiness. The support vector machine was the technique of choice for a decade. However, since 2018, deep learning has been trending upwards and is likely to be the leading technique in the coming few years. Despite the accuracy paradox, accuracy was the most frequently used metric in the studies reviewed, followed by sensitivity, specificity, F1 score and then AUC.Conclusion: Applying ML using ECG data has shown promise. Data scientists and physicians should collaborate to ensure that clinical knowledge is being applied appropriately and is informing the design of ML algorithms. Data scientists also need to consider knowledge guided feature engineering and the explicability of the ML algorithm as well as being transparent in the algorithm's performance to appropriately calibrate human-AI trust. Future work is required to enhance ML performance in ECG classification. [ABSTRACT FROM AUTHOR]

Abstract Background Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comparability of the performance of these algorithms and thereby the technical progress in this field. Methods An electronic questionnaire was sent to dermatologists at 12 German university hospitals. Each questionnaire comprised 100 dermoscopic and 100 clinical images (80 nevi images and 20 biopsy-verified melanoma images, each), all open-source. The questionnaire recorded factors such as the years of experience in dermatology, performed skin checks, age, sex and the rank within the university hospital or the status as resident physician. For each image, the dermatologists were asked to provide a management decision (treat/biopsy lesion or reassure the patient). Main outcome measures were sensitivity, specificity and the receiver operating characteristics (ROC). Results Total 157 dermatologists assessed all 100 dermoscopic images with an overall sensitivity of 74.1%, specificity of 60.0% and an ROC of 0.67 (range = 0.538–0.769); 145 dermatologists assessed all 100 clinical images with an overall sensitivity of 89.4%, specificity of 64.4% and an ROC of 0.769 (range = 0.613–0.9). Results between test-sets were significantly different (P < 0.05) confirming the need for a standardised benchmark. Conclusions We present the first public melanoma classification benchmark for both non-dermoscopic and dermoscopic images for comparing artificial intelligence algorithms with diagnostic performance of 145 or 157 dermatologists. Melanoma Classification Benchmark should be considered as a reference standard for white-skinned Western populations in the field of binary algorithmic melanoma classification. Highlights • This paper provides the first open access melanoma classification benchmark for both non-dermoscopic and dermoscopic images. • Algorithms can now be easily compared to the performance of dermatologists in terms of sensitivity, specificity and ROC. • The melanoma benchmark allows comparability between algorithms of different publications and provides a new reference standard. [ABSTRACT FROM AUTHOR]

Deep learning algorithms are a subset of the machine learning algorithms, which aim at discovering multiple levels of distributed representations. Recently, numerous deep learning algorithms have been proposed to solve traditional artificial intelligence problems. This work aims to review the state-of-the-art in deep learning algorithms in computer vision by highlighting the contributions and challenges from over 210 recent research papers. It first gives an overview of various deep learning approaches and their recent developments, and then briefly describes their applications in diverse vision tasks, such as image classification, object detection, image retrieval, semantic segmentation and human pose estimation. Finally, the paper summarizes the future trends and challenges in designing and training deep neural networks. [ABSTRACT FROM AUTHOR]

Utilizing unlabeled data to train deep neural networks (DNNs) is a crucial but challenging task. In this paper, we propose an end-to-end approach to tackle this problem with consistent inference of latent representations. Specifically, each unlabeled data point is considered as a seed to generate a set of latent labeled data points by adding various random disturbances or transformations. Under the expectation maximization framework, DNNs can be trained in an unsupervised way by minimizing the distances between the data points with the same latent representations. Furthermore, several variants of our approach can be derived by applying regularized and sparse constraints during optimization. Theoretically, the convergence of the proposed method and its variants are fully analyzed. Experimental results show that the proposed approach can significantly improve the performance on various tasks, including image classification and clustering. Such results also indicate that our method can guide DNNs to learn more invariant feature representations in comparison with traditional unsupervised methods. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine. [ABSTRACT FROM AUTHOR]

Detection transformer (DETR) combines convolutional neural network (CNN) with transformer, providing a more advanced idea. In this paper, an object detection model based on DETR is proposed for fire and smoke detection. Compared with other methods that based on deep learning, the proposed one simplifies the pipeline of detection and builds an end-to-end detector. At the same time, the original DETR model usually requires long training time and large amount of computation, resulting in relatively poor performance in detection speed and accuracy, and it shows to be not friendly for small or early fire detection. Therefore, when designing the proposed network model, a normalization-based attention module is added in the feature extraction stage to highlight the effective features, being beneficial to the process of encoding. A multiscale deformable attention is also used in the encoder–decoder structure, accelerating therefore, the process of convergence during the training of the model, which includes the enhancement of the detection of small objects. Also, considering the cost of computation, the number of layers in the encoder–decoder structure is redefined to reduce the complexity of the model, and that also reduces the requirements of the application equipment. Detailed experiments are conducted on three self-built datasets and two public video sets. The results show that, the proposed method has an excellent performance on all of the datasets considered here. [ABSTRACT FROM AUTHOR]

• Little is known about the potential of artificial intelligence in forensic shotgun pattern interpretation. • This proof-of-concept study aimed to explore the potential of neural net architectures to correctly classify shotgun pattern images in terms of shooting distance. • A TinyResNet-based algorithm reached the highest testing accuracy of 94%. • On the basis of these preliminary data, it seems achievable to develop algorithms that would serve as a beneficial tool for forensic investigators when estimating shooting distances from shotgun patterns. Little is known about the potential of artificial intelligence in forensic shotgun pattern interpretation. As shooting distance is among the main factors behind shotgun patterning, this proof-of-concept study aimed to explore the potential of neural net architectures to correctly classify shotgun pattern images in terms of shooting distance. The study material comprised a total of 106 shotgun pattern images from two discrete shooting distances (n = 54 images from 10 m and n = 52 images from 17.5 m) recorded on blank white paper. The dataset was used to train, validate and test deep learning algorithms to correctly classify images in terms of shooting distance. The open source AIDeveloper software was used for the deep learning procedure. In this dataset, a TinyResNet-based algorithm reached the highest testing accuracy of 94%. Of the testing set, the algorithm classified all 10 m patterns correctly, and misclassified one 17.5 m pattern. On the basis of these preliminary data, it seems achievable to develop algorithms that would serve as a beneficial tool for forensic investigators when estimating shooting distances from shotgun patterns. In the future, studies with larger and more complex datasets are needed to develop robust and applicable algorithms for forensic shotgun pattern interpretation. [ABSTRACT FROM AUTHOR]

[Display omitted] • A deep learning-enabled super-resolution (SR) imaging method. • The method applicable to both regular and mini-microscopy. • Network interpolation strategy to incorporate biological cellular features into SR. • Hardware for SR imaging at an extremely low cost (<$10) in mini-microscopy. • Improve volumetric SR imaging for spatial analyses in mini-microscopy. Artificial intelligence algorithms that aid mini-microscope imaging are attractive for numerous applications. In this paper, we optimize artificial intelligence techniques to provide clear, and natural biomedical imaging. We demonstrate that a deep learning-enabled super-resolution method can significantly enhance the spatial resolution of mini-microscopy and regular-microscopy. This data-driven approach trains a generative adversarial network to transform low-resolution images into super-resolved ones. Mini-microscopic images and regular-microscopic images acquired with different optical microscopes under various magnifications are collected as our experimental benchmark datasets. The only input to this generative-adversarial-network-based method are images from the datasets down-sampled by the Bicubic interpolation. We use independent test sets to evaluate this deep learning approach with other deep learning-based algorithms through qualitative and quantitative comparisons. To clearly present the improvements achieved by this generative-adversarial-network-based method, we zoom into the local features to explore and highlight the qualitative differences. We also employ the peak signal-to-noise ratio and the structural similarity, to quantitatively compare alternative super-resolution methods. The quantitative results illustrate that super-resolution images obtained from our approach with interpolation parameter α = 0.25 more closely match those of the original high-resolution images than to those obtained by any of the alternative state-of-the-art method. These results are significant for fields that use microscopy tools, such as biomedical imaging of engineered living systems. We also utilize this generative adversarial network-based algorithm to optimize the resolution of biomedical specimen images and then generate three-dimensional reconstruction, so as to enhance the ability of three-dimensional imaging throughout the entire volumes for spatial-temporal analyses of specimen structures. [ABSTRACT FROM AUTHOR]

The Deep Reinforcement Learning (DRL) algorithm, Proximal Policy Optimization (PPO2), is deployed on a custom spacecraft (S/C) build and loss model to determine if an Artificial Intelligence (AI) can learn to monitor satellite constellation health and determine an optimal replacement strategy. A custom environment is created to simulate how S/C are built, launched, generate revenue, and finally decay. The reinforcement learning agent successfully learned an optimal policy for two models: a Simplified Model where the financial cost of actions is ignored; and an Advanced Model where the financial cost of actions is a major element. In both models the AI monitors the constellations and takes multiple strategic and tactical actions to replace satellites to maintain constellation performance. The Simplified Model showed that the PPO2 algorithm was able to converge on an optimal solution after ∼ 200,000 simulations. The Advanced Model was much more difficult for the AI to learn, and thus, the performance drops during the early episodes, but eventually converges to an optimal policy at ∼ 25,000,000 simulations. With the Advanced Model, the AI is taking actions that are successfully providing strategies for constellation management and satellite replacements which include these actions' financial implications. Thus, the methods in this paper provide initial research developments towards a real-world tool and an AI application that can aid various Aerospace businesses in managing Low Earth Orbit (LEO) constellations. This type of AI application may become imperative for deploying and maintaining small satellite mega-constellations. [ABSTRACT FROM AUTHOR]

• Online network reconfiguration is introduced to manage distribution networks. • A Deep Q-learning algorithm is used to find the optimal network topology. • The proposed algorithm is more scalable than other algorithms. • The computation time of the algorithm is low enough for practical applications. • The algorithm can increase DRES utilization without heavy capital investment. With increasing number of distributed renewable energy sources integrated in power distribution networks, network security issues such as line overloading or bus voltage violations are becoming increasingly common. Traditional capital-intensive system reinforcements could lead to overinvestment. Moreover, active network management solutions, which have emerged as important alternatives, may become a financial burden for distribution system operators or reduce profits for owners of distributed renewable energy sources, or both. To address these limitations, this paper proposes an online network reconfiguration scheme based on a deep reinforcement learning approach. In this scheme, the distribution network operator modifies the network topology to change the power flow when the reliability of network is threatened. Because the variability of distributed renewable energy is large in self-sufficient distribution networks, the reconfiguration process needs to be performed online within short time intervals, which involves the use of conventional algorithms. To solve this problem efficiently, a deep q-learning model is utilized to determine the optimal network topology. Performances of proposed and other algorithms were compared in modified CIGRE 14-bus and IEEE 123-bus test network, as well as varying penalties for frequent switching operation in consideration of physical characteristic of the network. Simulation results demonstrated that the proposed algorithm showed almost identical performances with brute-force search algorithm in both test networks, satisfying network constraints over almost all timespans. Further, the proposed method required very small computation times - under a second per each state and its scalability was verified by comparing the computation time between two test networks. [ABSTRACT FROM AUTHOR]

With advances in smart grid technologies, demand response has played a major role in improving the reliability of grids and reduce the cost for customers. Implementing the demand response scheme for industry is more necessary than for other sectors, because its energy consumption is often considered the largest. This paper proposes a multi-agent deep reinforcement learning based demand response scheme for energy management of discrete manufacturing systems. In this regard, the industrial manufacturing system is initially formulated as a partially-observable Markov game; then, a multi-agent deep deterministic policy gradient algorithm is adopted to obtain the optimal schedule for different machines. A typical lithium-ion battery assembly manufacturing system is used to demonstrate the effectiveness of the proposed scheme. Simulation results show that the presented demand response algorithm can minimize electricity costs and maintain production tasks, as compared to a benchmark without demand response. Moreover, the performance of the multi-agent deep reinforcement learning approach against a mathematical model method is investigated. [ABSTRACT FROM AUTHOR]