Search

• We analyzed older driver at-fault crashes at unsignalized intersections in Alabama. • We estimated random parameter models. • We introduced a deep learning approach based on Artificial Neural Networks. • We identified variables that increase the likelihood of injuries and fatalities. • We discussed possible countermeasures to address older driver at-fault crashes. Introduction: The research described in this paper explored the factors contributing to the injury severity resulting from the male and female older driver (65 years and older) at-fault crashes at unsignalized intersections in Alabama. Method: Random parameter logit models of injury severity were estimated. The estimated models identified a variety of statistically significant factors influencing the injury severities resulting from older driver at-fault crashes. Results : According to these models, some variables were found to be significant only in one model (male or female) but not in the other one. For example, variables such as driver under the influence of alcohol/drugs, horizontal curve, and stop sign were found significant only in the male model. On the other hand, variables such as intersection approaches on tangents with flat grade, and driver older than 75 years were found significant only in the female model. In addition, variables such as making turning maneuver, freeway-ramp junction, high speed approach, and so forth were found significant in both models. Estimation findings showed that two parameters in the male model and another two parameters in the female model could be modeled as random parameters, indicating their varying influences on the injury severity due to unobserved effects. In addition to the random parameter logit approach, a deep learning approach based on Artificial Neural Networks was introduced to predict the outcome of the crashes based on 164 variables that are listed in the crash database. The artificial intelligence (AI)-based method achieved an accuracy of 76% indicating the role of the variables in deciding the final outcome. Practical Applications: Future plans are set to study the use of AI on large sized datasets to achieve a relatively high-performance, and hence to be able to identify which variables contribute the most to the final outcome. [ABSTRACT FROM AUTHOR]

An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Machine/deep learning (ML/DL) has been hugely leveraged to solve complex problems involved with processing high-dimensional data. However, reasoning and quantifying different uncertainties to achieve effective decision-making have been much less explored in ML/DL than in other Artificial Intelligence (AI) domains. In particular, belief/evidence theories have been studied in Knowledge representation and reasoning (KRR) since the 1960s to reason and measure uncertainties to enhance decision-making effectiveness. Based on our in-depth literature review, only a few studies have leveraged mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty. Our present survey paper discusses major belief theories and their core ideas dealing with uncertainty causes and types and quantifying them, along with the discussions of their applicability in ML/DL. Particularly, we discuss three main approaches leveraging belief theories in Deep Neural Networks (DNNs), including Evidential DNNs, Fuzzy DNNs, and Rough DNNs, in terms of their uncertainty causes, types, and quantification methods along with their applicability in diverse problem domains. Through an in-depth understanding of the extensive survey on this topic, we discuss insights, lessons learned, limitations of the current state-of-the-art bridging belief theories and ML/DL, and future research directions. This paper conducts an extensive survey by bridging belief theories and deep learning in reasoning and quantifying uncertainty to help researchers initiate uncertainty and decision-making research. • Identifying the causes and types of uncertainty in various belief models. • Uncertainty reasoning and quantification of belief models for decision-making. • Applying uncertainty quantification of belief models into deep learning. • Comparing uncertainty consideration in belief models and deep learning. • Insights, lessons learned, limitations, and future research directions. [ABSTRACT FROM AUTHOR]

Online social networks can be used for mental healthcare monitoring using Artificial Intelligence and Machine Learning techniques for detecting various mental health disorders and corresponding risk assessment. Recent research in this domain has primarily been focused on leveraging deep neural networks and various Transformer based Large Language Models, which have now become state-of-the-art for most natural language processing and computational linguistic tasks due to their unmatched prediction accuracy. Unlike conventional machine learning algorithms, these deep neural networks are black box architectures, where it is difficult to interpret and explain their predicted outcome. However, a black box classification outcome is insufficient for healthcare applications. Such systems will not be widely adopted and trusted by healthcare practitioners if they are not able to understand and explain the reasoning behind the predicted decisions made by an AI and ML based healthcare diagnostic system. The key objective of our research is to demonstrate the applications of model agnostic, post hoc surrogate XAI techniques for providing explainability to classification decisions of pretrained LLMs (Transformers) based mental healthcare diagnostic systems fine-tuned (or trained) to detect depressive and suicidal behavior using UGC from online social networks. For this, we have used the two most recent and popular techniques, SHAP and LIME. We have conducted extensive and in-depth experiments with four datasets and six pretrained LLMs, three of which have already been domain-adapted using mental health related datasets. We have also performed Few Shot Learning experiments with these three pretrained mental health domain-adapted LLMs. The results of qualitative and descriptive data analysis in this paper demonstrate that in order to build a comprehensive understanding of a person's psychological state, emotion, and behavior and to discover the causes, symptoms, and triggers of mental health issues, it is essential to utilize e X pl AI nable (XAI) techniques with Transformer based LLMs (supervised). Alternatively, Transformer based unsupervised topic modeling technique BERTopic may be used for mental health risk monitoring and cause or symptom extraction when supervised training of LLMs is not feasible due to dataset annotation or availability challenges. [ABSTRACT FROM AUTHOR]

Robustness has become an important consideration in deep learning. With the help of explainable AI, mismatches between an explained model's decision strategy and the user's domain knowledge (e.g. Clever Hans effects) have been identified as a starting point for improving faulty models. However, it is less clear what to do when the user and the explanation agree. In this paper, we demonstrate that acceptance of explanations by the user is not a guarantee for a machine learning model to be robust against Clever Hans effects, which may remain undetected. Such hidden flaws of the model can nevertheless be mitigated, and we demonstrate this by contributing a new method, Explanation-Guided Exposure Minimization (EGEM), that preemptively prunes variations in the ML model that have not been the subject of positive explanation feedback. Experiments demonstrate that our approach leads to models that strongly reduce their reliance on hidden Clever Hans strategies, and consequently achieve higher accuracy on new data. • Explainable AI may not detect all Clever Hans effects in practical ML scenarios. • Hidden Clever Hans effects can be mitigated using Exposure Minimization. • We propose a solution in the form of a soft-pruning scheme on network activations. • We demonstrate the practical benefits of our approach on image and text data. [ABSTRACT FROM AUTHOR]

Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach's effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy. • We propose a multi-source time-series fusion methodology relying on Deep Learning. • We design a robust framework for daily 7-days-horizon predictions. • We assess the proposed approach on a real medical bookings dataset. [ABSTRACT FROM AUTHOR]

Early fault detection, both in equipment and the products in process, is of paramount importance in industrial processes to ensure the quality of the final product, avoid abnormal operating conditions, expensive repairs, and even production process shutdown. The growing complexity of industrial systems and the increase in the amount of available data have encouraged the development of intelligent systems for automatic fault prediction/detection, mainly based on Industry 4.0 technologies and, particularly, those based on deep learning methodologies. However, the vast majority of proposals and research carried out to date define specific solutions for specific cases, which still requires a high level of expert knowledge for scaling the solutions to industrial environments. Actually, one of the major issues towards the industrialization of deep learning solutions is the determination of the optimal, or near-optimal, hyper-parameters. In this paper, we propose a low-level set-up effort intelligent failure detection system that integrates deep neural networks with a Bayesian Optimization algorithm for self-tuning of the system hyper-parameters. In addition, to facilitate the industrialization of the proposal and its incorporation into current industrial systems, we embedded the proposal in our previously formulated and tested Simulai architecture which allows for containing and interaction with multiple and heterogeneous technological components of manufacturing processes. Finally, our proposal is tested in two real cases of a different nature. The obtained results show a successful performance and demonstrate the easy online integration and interaction in a real production system. • Industrialization of artificial intelligence through a self-adjusting system. • Improvement of the decision-making process in the detection of industrial failures. • Optimization of deep learning for the improvement of industrial health and quality. • Demonstration of the proposal's adaptability to real cases of different natures. [ABSTRACT FROM AUTHOR]

Recently, facial expression recognition (FER) methods have achieved significant progress. However, FER is still challenged by factors such as uneven illumination and low-quality expression images. Exploring the potential of facial expression features to achieve robust FER is particularly important. Inspired by the Transformer's excellent performance in modeling long-range dependencies and in vision tasks, this paper proposes a Transformer Block Enhancement Module (TBEM) for enhancing the feature representation of facial expressions. The proposed module contains a Channel Enhancement (CE) block and a Spatial Enhancement (SE) block. The CE block can adaptively enhance the expression features on the channel dimension by effectively leveraging the channel dependency information, while the SE block enhances the expression features on the spatial dimension by integrating the spatial dependency information. TBEM can output a more robust expression representation by combining CE and SE. The proposed artificial intelligence learning module greatly improves the recognition accuracy of FER engineering tasks. To further illustrate the application of TBEM in real-world FER engineering, three engineering problems are used for verification. Extensive experiments demonstrate that the proposed method improves FER performance by focusing on more accurate decisional features and can be easily embedded into regular convolutional neural network models to help them improve the accuracy on FER tasks by about 2.64%–3.03%. The results show the proposed method achieves accuracies of 90.57% on FERPlus, 89.41% on RAFDB basic and 68.43% on RAFDB compound, respectively. The proposed method also provides a meaningful reference for further research on applying Transformer to other tasks. [ABSTRACT FROM AUTHOR]

• Data-driven methods offer excellent performance for process monitoring. • Their black-box nature without explanations, however, has limited their industrial adoption. • We propose a method for explaining the predictions by deep neural networks using alarm limits. • Two case studies demonstrate its utility to the plant operator as well as to the model developer. Deep Learning (DL) models are becoming the preferred approach for process monitoring due to their higher prediction accuracy; however, they are still viewed as black boxes. Explainable Artificial Intelligence (XAI) methods seek to address this shortcoming by providing explanations that are either global (explains the entire DL model) or local (explains the result for each individual sample). Due to nonlinearities and other complexities inherent in chemical processes, providing a local explanation is more suitable. This paper proposes a local XAI method that explains process monitoring results from a deep neural network (DNN) based on process alarms. The effectiveness of the proposed method is demonstrated using a CSTR case study and the Tennessee-Eastman benchmark process. Our results show that the explanations proffered by the proposed method assist operators in understanding DNN's prediction during online process monitoring. Additionally, during model development, it can offer insights that enable improvement of the DNN's architecture. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and deep learning (DL) are both branches of AI. As a form of AI, ML automatically adapts to changing datasets with minimal human interference. Deep learning is a subset of ML that uses artificial neural networks to imitate the learning process of the human brain. The 'black box' nature of ML and DL makes their inner workings difficult to understand and interpret. Deploying explainable artificial intelligence (XAI) can help explain why and how the output of ML and DL models are generated. As a result, understanding a model's functioning, behavior, and outputs can be garnered, reducing bias and error and improving confidence in decision-making. Despite providing an improved understanding of model outputs, XAI has received limited attention in construction. This paper presents a narrative review of XAI and a taxonomy of precepts and models to raise awareness about its potential opportunities for use in construction. It is envisaged that the opportunities suggested can stimulate new lines of inquiry to help alleviate the prevailing skepticism and hesitancy toward AI adoption and integration in construction. [ABSTRACT FROM AUTHOR]

Malignancy is the tendency of a medical condition to progressively become fatal and generally refers to the presence of cancerous cells that can spread, invade, and destroy tissues. In the United States, a new cancer diagnosis is given every 30 s, and the rates of diagnosis are not indicating any slowdown. The number of cancer cases is projected to increase by 35% over the next decade. Increasing cancer cases have attracted researchers to find new approaches for their identification and characterization. Skin diseases are a significant healthcare burden, affecting millions of people globally. The early detection and diagnosis of skin diseases are essential to prevent adverse health consequences, including the development of malignant skin tumors. Dermoscopy-based diagnosis of skin diseases is a time-consuming process, particularly in resource-limited healthcare settings. Recent advancements in computer vision algorithms have led to the development of several smartphone-based applications for early skin disease prognosis. In this paper, we have utilized a set of 16 distinctive convolutional neural network models, based on deep learning, to process over 45,000 high-quality images from the HAM10000 dataset, encompassing seven categories of skin diseases. Our models were able to effectively identify and classify skin diseases using lesion images as input. Dermoscopy-based diagnosis of skin diseases is a time-consuming process, particularly in resource-limited healthcare settings. Our proposed application for early skin disease detection aims to prevent unintended health consequences, which can result in wastage of operating hours, resources, and, in some cases, compromised patient health. We have benchmarked our models against one another to evaluate their effectiveness and efficiency, using performance measures such as accuracy, precision, sensitivity, specificity, F-score, and G-Mean. Our results demonstrate that most deployed models achieve an accuracy of up to 99%. [ABSTRACT FROM AUTHOR]

Several sectors have increased the use of lattice structures due its great capacity to reduce the structural mass with reasonable rigidity loss. However, these structures can present complex mechanical behaviors, impossible to be determined analytically. Surrogate models obtained from design of experiments have been shown to be promising but insufficient. Numerical models using finite elements are computationally expensive when optimization model updating is considered. In order to solve these problems, in this paper a deep learning model is trained in order to predict 16 different structural responses (static, dynamic and stability) of a complex composite isogrid structure tube. All input data were generated from a numerical finite element model. The results demonstrated substantial capacity of the deep learning model to fit the isogrid physical behavior and also predict the structural responses. In addition, it was also to predict the design parameters of the isogrid structure from the desired results, also demonstrating the good performance (R2 > 90 %) of the artificial intelligence model to perform this prediction. [ABSTRACT FROM AUTHOR]

• Methodology and model for creating AI expert learning via AR and humans. • Positive climate impact when some services can be carried out remotely over AR calls. • Energy savings and CO₂ emissions reduction have been estimated. • Supporting the development of smart industries and autonomous operations. • Economic benefits as for health improvement and increased business productivity. Initially, Artificial Intelligence (AI) focused on diagnostics during the 70s and 80s. Unfortunately, it did not gain trust and few industries embraced it, mostly due to the extensive manual programming effort that AI required for interpreting data and act. In addition, the computer capacity, for handling the amounts of data necessary to train AI, was lacking the disc dimensions we are used to today, which made it go slowly. Not until the 2000 s confidence in AI was established in parallel with the introduction of new tools that was paving the way for PLS, PCA, ANN and soft sensors. Year 2011, IBM Watson (an AI application) was developed and won over the jeopardy champion. Today's machine learning (ML) such as "deep learning" and artificial neural networks (ANN) have created interesting use cases. AI has therefore regained confidence and industries are beginning to embrace where they see appropriate uses. Simultaneously, Internet of Things (IoT) tools have been introduced and made it possible to develop new capabilities such as virtual reality (VR), augmented reality (AR), mixed reality (MR) and extended reality (XR). These technologies are maturing and could be used in several application areas for the industries and form part of their digitalization journey. Furthermore, it is not only the industries that could benefit from introducing these technologies. Studies also show several areas and use cases where augmented reality has a positive impact, such as on students' learning ability. Yet few teachers know or use this technology. This paper evaluates and analyze AR, remote assistance tool for industrial purposes. The potential of the tool is discussed for frequent maintenance cases in the mining industry. Further on, if we look into the future, it is not surprising if we will be able to see that today's concepts of reality tools have evolved to become smarter by being trained by multimedia recognition and from people who have thus created an AI expert. Where the AI expert will support customers and be able to solve simple errors but also those that occur rarely and thus be a natural part of the solution for future completely autonomous processes for the industry. The article demonstrates a framework for creating smarter tools by combining AR, ML and AI and forms part of the basis creating the smarter industry of the future. Natural Language Processing (NLP) toolbox has been utilized to train and test an AI expert to give suitable resolutions to a specific maintenance request. The motivation for AR is the possible energy savings and reduction of CO₂ emissions in the maintenance field for all business trips that can be avoided. At the same time saving money for the industries and expert manhours that are spent on traveling and finally enhancing the productivity for the industries. Tests cases have verified that with AR, the resolution time could be significantly reduced, minimizing production stoppages by more than 50% of the time, which ultimately has a positive effect on a country's GDP. How much energy can be saved is predicted by the fact that 50% of all the world's business flights are replaced by one of the reality concepts and are estimated to amount to at least 50 Mton CO₂ per year. This figure is probably slightly higher as business trips also take place by other means of transport such as trains, buses, and cars. With today's volatile employees changing jobs more frequently, industry experts are becoming fewer and fewer. Since new employee stays for a maximum of 3–5 years per workplace, they will not stay long enough to become experts. Introducing an AI expert trained by today's experts, there is a chance that this knowledge can be maintained. [ABSTRACT FROM AUTHOR]

How to design System of Systems has been widely concerned in recent years, especially in military applications. This problem is also known as SoS architecting, which can be boiled down to two subproblems: selecting a number of systems from a set of candidates and specifying the tasks to be completed for each selected system. Essentially, such a problem can be reduced to a combinatorial optimization problem. Traditional exact solvers such as branch-bound algorithm are not efficient enough to deal with large scale cases. Heuristic algorithms are more scalable, but if input changes, these algorithms have to restart the searching process. Re-searching process may take a long time and interfere with the mission achievement of SoS in highly dynamic scenarios, e.g., in the Mosaic Warfare. In this paper, we combine artificial intelligence with SoS architecting and propose a deep reinforcement learning approach DRL-SoSDP for SoS design. Deep neural networks and actor–critic algorithms are used to find the optimal solution with constraints. Evaluation results show that the proposed approach is superior to heuristic algorithms in both solution quality and computation time, especially in large scale cases. DRL-SoSDP can find great solutions in a near real-time manner, showing great potential for cases that require an instant reply. DRL-SoSDP also shows good generalization ability and can find better results than heuristic algorithms even when the scale of SoS is much larger than that in training data. [ABSTRACT FROM AUTHOR]

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the 'system of systems' approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems. • Literature on the use of AI in infrastructure sectors is reviewed. • Types of AI, trends across sectors and purposes within infrastructure are identified. • Findings have been evaluated using an analysis framework. • The future of AI and the 'Survey PaperInternet of Things' in infrastructure are discussed. [ABSTRACT FROM AUTHOR]

Rockfall risk management has traditionally been built upon scenarios based on statistical analysis of historic frequency-magnitude rockfall patterns which allows for an estimation of the return period of rockfalls within given ranges of magnitude. Τhe availability of rockfall databases is thereby of crucial importance to quantify rockfall activity. In recent years, engineering geologists have been able to leverage light detection and ranging (LiDAR) technology with automated change detection and clustering workflows, for spatially accurate rockfall monitoring and inventorization. This wealth of digital data generates new research prospects for the development of an artificial intelligence (AI) system able to analyze change detection sequences and map spatio-temporal rockfall patterns by precisely capturing the rock slope evolution in near-real-time. However, prerequisite to such an advancement is the need to efficiently automate the process of classifying surface changes corresponding to rockfall. This task typically requires intensive human effort and is prone to multiple sources of human error like subjectivity, expertise, and experience that may differ from person to person involved in a particular project. This paper investigates the potential of integrating sophisticated deep learning architectures into dynamic rockfall database population processes, with the goal of relieving experts from the daunting task of manually classifying rockfall events within large loads of change detection data. Deep neural networks based on the pioneering PointNet and PointNet++ architectures for 3D point cloud learning are developed for this purpose based on a 5-year change detection database consisting of >8000 rock slope clusters of identified change for training, with scanning intervals ranging from 5 days to 6 months. The models are tested on the 536 clusters from the two last data acquisitions to simulate the real monitoring situation and subsequently on the most frequent of the campaigns to increase the probability of working with single-event clusters. The best-performing model achieves an accuracy of about 89% and 84% on the last and shortest campaign, respectively. The optimized deep learning models are further evaluated on a geologically different rockfall database achieving almost 93% accuracy in a location where discrete geomorphologic features such as steep rock outcrops and erosion channels are present. The study shows that although it is challenging to achieve generalization in rockfall detection, site-specific training of the proposed deep learning architecture can lead to high-level performance and support further advancements in rockfall risk management. • LiDAR monitoring provides detailed 3D point cloud models of rock slope deformations. • High-level feature learning from point clouds is achieved with modern deep learning. • Rockfall detection can be approached as a binary point cloud classification problem. • Global shape feature learning is suggested compared to local texture features. • The prediction confidence increases with the cluster volume. [ABSTRACT FROM AUTHOR]

This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented. [Display omitted] • AI, ML and DL are transforming the building and construction industry 4.0. • Robotics, IoT, Digital Twins, 4D Printing, Cloud AR/VR, Blockchain are future trends. • AI techniques can be used throughout the whole building lifecycle. • Sensor and vision-based data acquisition can facilitate ML/DL implementation. [ABSTRACT FROM AUTHOR]

Deep learning and neural networks are being extended to geometric design and computation. Recent studies show that deep neural networks are vulnerable to adversarial samples. However, for 3D mesh adversarial samples, the most related studies actually attack the 2D victim networks, in which they have to project 3D objects to 2D images. In this paper, we present D3AdvM (Direct 3D adversary for mesh) to directly generate adversarial samples inside mesh data. Specifically, we propose two adversary generation approaches: vertex-based and edge-based. The first one, 3DVP (3D Vertex-based Perturbation), skillfully searches the optimized vertices positions based on opposite gradient fitness. The second one, KES (Key Edge-based Selection), carefully collapses the key feature edges according to importance of edge feature. Thus, our approaches avoid 3D/2D projections and approximation errors. Also, our approach can reduce the computation overhead. In addition, the proposed D3AdvM can control the number of perturbed vertices for real-world engineering designs and applications. Extensive experiments show that the generated 3D meshes are effective to attack classification networks. Furthermore, we evaluate the transferability, in which D3AdvM can attack both mesh-based networks and point cloud-based networks as victim networks. Our findings could benefit 3D geometry design based on the new generation of artificial intelligence and big data. • This work is the first one to directly attack mesh-based neural networks with adversarial meshes in 3D space without the 2D projection. • We explore the characteristics of 3D mesh topological entities and propose both vertex-based and edge-based adversarial approaches. • D3AdvM can constrain the adversarial samples within a specific number to support high feasibility in real-world applications. • D3AdvM shows valuable transferability to attack different versions of mesh-based networks and even the point cloud-based networks. • Our findings could benefit 3D geometry design based on the new generation of artificial intelligence and big data. [ABSTRACT FROM AUTHOR]

In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous. However, despite the significant and undoubted progress of the field in addressing the issue of catastrophic forgetting, benchmarking different continual learning approaches is a difficult task by itself. In fact, given the proliferation of different settings, training and evaluation protocols, metrics and nomenclature, it is often tricky to properly characterize a continual learning algorithm, relate it to other solutions and gauge its real-world applicability. The first Continual Learning in Computer Vision challenge held at CVPR in 2020 has been one of the first opportunities to evaluate different continual learning algorithms on a common hardware with a large set of shared evaluation metrics and 3 different settings based on the realistic CORe50 video benchmark. In this paper, we report the main results of the competition, which counted more than 79 teams registered and 11 finalists. We also summarize the winning approaches, current challenges and future research directions. [ABSTRACT FROM AUTHOR]

Visual design is associated with the use of some basic design elements and principles. Those are applied by the designers in the various disciplines for aesthetic purposes, relying on an intuitive and subjective process. Thus, numerical analysis of design visuals and disclosure of the aesthetic value embedded in them are considered as hard. However, it has become possible with emerging artificial intelligence technologies. This research aims at a neural network model, which recognizes and classifies the design principles over different domains. The domains include artwork produced since the late 20th century; professional photos; and facade pictures of contemporary buildings. The data collection and curation processes, including the production of computationally-based synthetic dataset, is genuine. The proposed model learns from the knowledge of myriads of original designs, by capturing the underlying shared patterns. It is expected to consolidate design processes by providing an aesthetic evaluation of the visual compositions with objectivity. [Display omitted] • This paper examines quantitative aspects of visual design principles (71). • A CNN based AI model is developed to detect fundamental visual features (74). • 9 classes -stem from 3 main principles: emphasis, balance and rhythm are targeted (84). • Visual data from photography, painting and architecture domains are used for ML (82). • High accuracy rates are recorded for top 3 predictions among 5 experiment settings (85). [ABSTRACT FROM AUTHOR]

Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. To leverage these new 'data-driven' technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from 'process-based', mechanistic modelling. This paper revisits those fundamentals and addresses 10 questions that might be posed by earth and environmental scientists, and with the aid of a real-world modelling experiment, it explains some critical, but often ignored, issues DL may face in practice. The overarching objective is to contribute to a future of AI-assisted earth and environmental sciences where AI models can (1) embrace the typically ignored knowledge base available, (2) function credibly in 'true' out-of-sample prediction, and (3) handle non-stationarity in earth and environmental systems. Comparing and contrasting earth and environmental problems with prominent AI applications, such as playing chess and trading in stock markets, provides critical insights for better directing future research in this field. • DL is rooted in connectionism, hyper-flexibility, and vigorous optimization , which are alien to process-based modelling. • A knowledge base is essential to enable predictions of complex, open , partially observable , and non-stationary systems. • Bridging DL and process-based modelling is embryonic but has great potential in an age of big data and computational power. [ABSTRACT FROM AUTHOR]