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Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities.

Deep learning architecture with convolutional neural network (CNN) achieves outstanding success in the field of computer vision. Where U-Net, an encoder-decoder architecture structured by CNN, makes a great breakthrough in biomedical image segmentation and has been applied in a wide range of practical scenarios. However, the equal design of every downsampling layer in the encoder part and simply stacked convolutions do not allow U-Net to extract sufficient information of features from different depths. The increasing complexity of medical images brings new challenges to the existing methods. In this paper, we propose a deeper and more compact split-attention u-shape network (DCSAU-Net), which efficiently utilises low-level and high-level semantic information based on two novel frameworks: primary feature conservation and compact split-attention block. We evaluate the proposed model on CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018 and SegPC-2021 datasets. As a result, DCSAU-Net displays better performance than other state-of-the-art (SOTA) methods in terms of the mean Intersection over Union (mIoU) and F1-socre. More significantly, the proposed model demonstrates excellent segmentation performance on challenging images. The code for our work and more technical details can be found at https://github.com/xq141839/DCSAU-Net.

Harmonizing the analysis of data, especially of 3-D image volumes, consisting of different number of slices and annotated per volume, is a significant problem in training and using deep neural networks in various applications, including medical imaging. Moreover, unifying the decision making of the networks over different input datasets is crucial for the generation of rich data-driven knowledge and for trusted usage in the applications. This paper presents a new deep neural architecture, named RACNet, which includes routing and feature alignment steps and effectively handles different input lengths and single annotations of the 3-D image inputs, whilst providing highly accurate decisions. In addition, through latent variable extraction from the trained RACNet, a set of anchors are generated providing further insight on the network's decision making. These can be used to enrich and unify data-driven knowledge extracted from different datasets. An extensive experimental study illustrates the above developments, focusing on COVID-19 diagnosis through analysis of 3-D chest CT scans from databases generated in different countries and medical centers.

Following the success of Natural Language Processing (NLP) transformers pretrained via self-supervised learning, similar models have been proposed recently for speech processing such as Wav2Vec2, HuBERT and UniSpeech-SAT. An interesting yet unexplored area of application of these models is Spoken Dialogue Systems, where the users’ audio signals are typically just mapped to word-level features derived from an Automatic Speech Recogniser (ASR), and then processed using NLP techniques to generate system responses. This paper reports a comprehensive comparison of dialogue policies trained using ASR-based transcriptions and extended with the aforementioned audio processing transformers in the DSTC2 task. Whilst our dialogue policies are trained with supervised and policy-based deep reinforcement learning, they are assessed using both automatic task completion metrics and a human evaluation. Our results reveal that using audio embeddings is more beneficial than detrimental in most of our trained dialogue policies, and that the benefits are stronger for supervised learning than reinforcement learning.

Classifiers trained on disjointed classes with few labelled data points are used in one-shot learning to identify visual concepts from other classes. Recently, Siamese networks and similarity layers have been used to solve the one-shot learning problem, achieving state-of-the-art performance on visual-character recognition datasets. Various techniques have been developed over the years to improve the performance of these networks on fine-grained image classification datasets. They focused primarily on improving the loss and activation functions, augmenting visual features, employing multiscale metric learning, and pre-training and fine-tuning the backbone network. We investigate similarity layers for one-shot learning tasks and propose two frameworks for combining these layers into a MergedNet network. On all four datasets used in our experiment, MergedNet outperformed the baselines based on classification accuracy, and it generalises to other datasets when trained on miniImageNet.

Neural Networks (NNs) are now being extensively utilised in various Artificial Intelligence platforms specifically in the area of image classification and real-time object tracking. We propose a novel design to address the problem of real time Unmanned Aerial Vehicle (UAV) monitoring and detection using a Zynq Ultrascale FPGA-based Convolutional Neural Network (CNN) implementation. The biggest challenge while implementing real-time algorithms on FPGAs is the limited DSP hardware resources available on FPGA platforms. Our proposed design overcomes the challenge of autonomous real time UAV detection and tracking on a Xilinx's Zynq Ultrascale XCZU9EG System on a Chip (SoC) platform. Our proposed design explores and provides a solution for overcoming the challenge of limited floating-point resources, whilst maintaining real-time performance. The solution consists of two modules: the UAV tracking and the neural network based UAV detection module. The tracking module uses our novel background-differencing algorithm whilst the UAV detection is based on a modified CNN algorithm, designed to give the maximum Field-Programmable Gate Array (FPGA) performance. These two modules are designed to complement each other and are enabled simultaneously to provide an enhanced real-time UAV detection for any given video input. The proposed system has been tested on detecting real-life flying UAVs, achieving an accuracy of 82\%, running at the full frame rate of the input camera for both tracking and Neural Network (NN) detection, achieving similar performance than an equivalent Deep Learning Processor Unit (DPU) Ultrascale FPGA based HD video and tracking implementation, but with lower resource utilisation as shown by our results.

Tea chrysanthemum detection at its flowering stage is one of the key components for selective chrysanthemum harvesting robot development. However, it is a challenge to detect flowering chrysanthemums under unstructured field environments given variations on illumination, occlusion and object scale. In this context, we propose a highly fused and lightweight deep learning architecture based on YOLO for tea chrysanthemum detection (TC-YOLO). First, in the backbone component and neck component, the method uses the Cross-Stage Partially Dense network (CSPDenseNet) and the Cross-Stage Partial ResNeXt network (CSPResNeXt) as the main networks, respectively, and embeds custom feature fusion modules to guide the gradient flow. In the final head component, the method combines the recursive feature pyramid (RFP) multiscale fusion reflow structure and the Atrous Spatial Pyramid Pool (ASPP) module with cavity convolution to achieve the detection task. The resulting model was tested on 300 field images using a data enhancement strategy combining flipping and rotation, showing that under the NVIDIA Tesla P100 GPU environment, if the inference speed is 47.23 FPS for each image (416 × 416), TC-YOLO can achieve the average precision (AP) of 92.49% on our own tea chrysanthemum dataset. Through further validation, it was found that overlap had the least effect on tea chrysanthemum detection, and illumination had the greatest effect on tea chrysanthemum detection. In addition, this method (13.6 M) can be deployed on a single mobile GPU, and it could be further developed as a perception system for a selective chrysanthemum harvesting robot in the future.

Marker-less motion capture is a rapidly advancing field that can take simple RGB image sequences, or more advanced Red Green Blue Depth (RGB-D) image sequences obtained using depth sensors, and outputs an estimated human pose. This method of human pose estimation allows for the extraction of biomechanical features which can then be analysed by clinicians to give more insights into a patient’s movement capabilities. When compared to other, more clinically proven technologies such as the Knee Kinesiography (KneeKG), biomechanics presented have the advantage of being more representative of natural movement without the obstructive markers placed on the body. This Significant difference of up to 10 degrees in a range of motion for the knee could be the key to better identifying a person’s gait or tracking their natural walking pattern over time, while also being more robust and better suited to a smaller clinical environment.

Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter’s Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.

To address one of the most challenging industry problems, we develop an enhanced training algorithm for anomaly detection in unlabelled sequential data such as time-series. We propose the outputs of a well-designed system are drawn from an unknown probability distribution, U , in normal conditions. We introduce a probability criterion based on the classical central limit theorem that allows evaluation of the likelihood that a data-point is drawn from U . This enables the labelling of the data on the fly. Non-anomalous data is passed to train a deep Long Short-Term Memory (LSTM) autoencoder that distinguishes anomalies when the reconstruction error exceeds a threshold. To illustrate our algorithm’s efficacy, we consider two real industrial case studies where gradually-developing and abrupt anomalies occur. Moreover, we compare our algorithm’s performance with four of the recent and widely used algorithms in the domain. We show that our algorithm achieves considerably better results in that it timely detects anomalies while others either miss or lag in doing so.

Most deep-learning frameworks for understanding biological swarms are designed to fit perceptive models of group behavior to individual-level data (e.g., spatial coordinates of identified features of individuals) that have been separately gathered from video observations. Despite considerable advances in automated tracking, these methods are still very expensive or unreliable when tracking large numbers of animals simultaneously. Moreover, this approach assumes that the human-chosen features include sufficient features to explain important patterns in collective behavior. To address these issues, we propose training deep network models to predict system-level states directly from generic graphical features from the entire view, which can be relatively inexpensive to gather in a completely automated fashion. Because the resulting predictive models are not based on human-understood predictors, we use explanatory modules (e.g., Grad-CAM) that combine information hidden in the latent variables of the deep-network model with the video data itself to communicate to a human observer which aspects of observed individual behaviors are most informative in predicting group behavior. This represents an example of augmented intelligence in behavioral ecology -- knowledge co-creation in a human-AI team. As proof of concept, we utilize a 20-day video recording of a colony of over 50 Harpegnathos saltator ants to showcase that, without any individual annotations provided, a trained model can generate an "importance map" across the video frames to highlight regions of important behaviors, such as dueling (which the AI has no a priori knowledge of), that play a role in the resolution of reproductive-hierarchy re-formation. Based on the empirical results, we also discuss the potential use and current challenges., Best Paper Winner at the 4th International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2021)

The analysis of retinal Spectral-Domain Optical Coherence Tomography (SD-OCT) images by trained medical professionals can be used to provide useful insights into various diseases. It is the most popular method of retinal imaging due to its non-invasive nature and the useful information it provides for making an accurate diagnosis. A deep learning approach for automating the segmentation of Cystoid Macular Edema (fluid) in retinal OCT B-Scan images was developed that is consequently used for volumetric analysis of OCT scans. This solution is a fast and accurate semantic segmentation network that makes use of a shortened encoder-decoder UNet like architecture with an integrated Dense ASPP module and Attention Gate for producing an accurate and refined retinal fluid segmentation map. Our system was evaluated against both publicly and privately available datasets; on the former the network achieved a Dice coefficient of 0.804, thus making it the current best performing approach on this dataset, and on the very small and challenging private dataset, it achieved a score of 0.691. Due to the lack of publicly available data in this domain, a Graphical User Interface that aims to semi-automate the labelling process of OCT images was also created, thus greatly simplifying the process of the dataset creation and potentially leading to an increase in labelled data production.

This paper explores the application of unsupervised learning to detecting anomalies in mouse video data. The two models presented in this paper are a dual stream, 3D convolutional autoencoder (with residual connections) and a dual stream, 2D convolutional autoencoder. The publicly available dataset used here contains twelve videos of a single home-caged mice alongside frame level annotations. Under the pretext that the autoencoder only sees normal events, the video data was handcrafted to treat each behaviour as a pseudo-anomaly thereby eliminating them from the others during training. The results are presented for one conspicuous behaviour (hang) and one inconspicuous behaviour (groom). The performance of these models is compared to a single stream autoencoder and a supervised learning model, which are both based on the custom CAE encoder. Both models are also tested on the CUHK Avenue dataset were found to perform as well as some state-of-the-art architectures.

Purpose: Echocardiography is the most commonly used modality for assessing the heart in clinical practice. In an echocardiographic exam, an ultrasound probe samples the heart from different orientations and positions, thereby creating different viewpoints for assessing the cardiac function. The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis.\ud \ud Approach: In this study, convolutional neural networks are used for the automated identification of 14 different anatomical echocardiographic views (larger than any previous study) in a dataset of 8732 videos acquired from 374 patients. Differentiable architecture search approach was utilized to design small neural network architectures for rapid inference while maintaining high accuracy. The impact of the image quality and resolution, size of the training dataset, and number of echocardiographic view classes on the efficacy of the models were also investigated.\ud \ud Results: In contrast to the deeper classification architectures, the proposed models had significantly lower number of trainable parameters (up to 99.9% reduction), achieved comparable classification performance (accuracy 88.4% to 96%, precision 87.8% to 95.2%, recall 87.1% to 95.1%) and real-time performance with inference time per image of 3.6 to 12.6 ms.\ud \ud Conclusion: Compared with the standard classification neural network architectures, the proposed models are faster and achieve comparable classification performance. They also require less training data. Such models can be used for real-time detection of the standard views.

The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus increasing the demand for rapid testing, diagnosis, and treatment. As COVID-19 can cause severe pneumonia, early diagnosis is essential for correct treatment, as well as to reduce the stress on the healthcare system. Along with COVID-19, other etiologies of pneumonia and Tuberculosis (TB) constitute additional challenges to the medical system. Pneumonia (viral as well as bacterial) kills about 2 million infants every year and is consistently estimated as one of the most important factor of childhood mortality (according to the World Health Organization). Chest X-ray (CXR) and computed tomography (CT) scans are the primary imaging modalities for diagnosing respiratory diseases. Although CT scans are the gold standard, they are more expensive, time consuming, and are associated with a small but significant dose of radiation. Hence, CXR have become more widespread as a first line investigation. In this regard, the objective of this work is to develop a new deep transfer learning pipeline, named DenResCov-19, to diagnose patients with COVID-19, pneumonia, TB or healthy based on CXR images. The pipeline consists of the existing DenseNet-121 and the ResNet-50 networks. Since the DenseNet and Resnet have orthogonal performances in some instances, in the proposed model we have created an extra layer with convolutional neural network (CNN) blocks to join these two models together to establish superior performance as compared to the two individual networks. This strategy can be applied universally in cases where two competing networks are observed. We have tested the performance of our proposed network on two-class (pneumonia and healthy), three-class (COVID-19 positive, healthy, and pneumonia), as well as four-class (COVID-19 positive, healthy, TB, and pneumonia) classification problems. We have validated that our proposed network has been able to successfully classify these lung-diseases on our four datasets and this is one of our novel findings. In particular, the AUC-ROC are 99.60, 96.51, 93.70, 96.40% and the F1 values are 98.21, 87.29, 76.09, 83.17% on our Dataset X-Ray 1, 2, 3, and 4 (DXR1, DXR2, DXR3, DXR4), respectively., Graphical abstract

Retail food packaging contains information which informs choice and can be vital to consumer health, including product name, ingredients list, nutritional information, allergens, preparation guidelines, pack weight, storage and shelf life information (use-by/best before dates). The presence and accuracy of such information is critical to ensure a detailed understanding of the product and to reduce the potential for health risks. Consequently, erroneous or illegible labeling has the potential to be highly detrimental to consumers and many other stakeholders in the supply chain. In practice, due to the high volume of food packages that go through the supply chain, mistakes do occur therefore good quality of images are needed to verify the correctness of the information. In this paper, a multi-source deep learning-based domain adaptation system is proposed and tested to identify and verify the presence and legibility of use-by date information from food packaging photos taken as part of the validation process as the products pass along the food production line. This was achieved by improving the generalization of the techniques via incorporating new loss functions and making use of multi-source datasets in order to extract domain-invariant representations for all domains and aligning distribution of all pairs of source and target domains in a common feature space, along with the class boundaries. The proposed system performed very well in the conducted experiments, for automating the verification process and reducing labeling errors that could otherwise threaten public health and contravene legal requirements for food packaging information and accuracy. Comprehensive experiments on our food packaging datasets demonstrate that the proposed multi-source deep domain adaptation method significantly improves the classification accuracy and therefore has great potential for application and beneficial impact in food manufacturing control systems.

The study presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge extracted by deep convolutional and recurrent neural networks when trained with medical images, such as magnetic resonance images and dopamine transporters scans. Internal representations of the trained DNNs constitute the extracted knowledge which is used in a transfer learning and domain adaptation manner, so as to create a unified framework for prediction of Parkinson's across different medical environments. A large experimental study is presented illustrating the ability of the proposed approach to effectively predict Parkinson's, using different medical image sets from real environments.

While a number of studies have investigated driving behaviors, detailed microscopic driving data has only recently become available for analysis. Through Basic Safety Message (BSM) data from the Michigan Safety Pilot Program, this study applies a Markov Decision Process (MDP) framework to understand driving behavior in terms of acceleration, deceleration and maintaining speed decisions. Personally Revealed Choices (PRC) that maximize the expected sum of rewards for individual drivers are obtained by analyzing detailed data from 120 trips and the application of MDP. Specifically, this paper defines states based on the number of objects around the host vehicle and the distance to the front object. Given the states, individual drivers’ reward functions are estimated using the multinomial logit model and used in the MDP framework. Optimal policies (i.e. PRC) are obtained through a value iteration algorithm. The results show that as the number of objects increases around a host vehicle, the driver prefer to accelerate in order to escape the crowdedness around them. In addition, when trips are segmented based on the level of crowdedness, increased levels of trip crowdedness results in a fewer number of drivers accelerating because the traffic conditions constrain them to maintaining constant speed or deceleration. One potential application of this study is to generate short-term predictive driver decision information through historical driving performance, which can be used to warn a host vehicle driver when the person substantially deviates from their own historical PRC. This information could also be disseminated to surrounding vehicles as well, enabling them to foresee the states and actions of other drivers and potentially avoid collisions.

Insects of the family Orthoptera: Acrididae including grasshoppers and locust devastate crops and eco-systems around the globe. The effective control of these insects requires large numbers of trained extension agents who try to spot concentrations of the insects on the ground so that they can be destroyed before they take flight. This is a challenging and difficult task. No automatic detection system is yet available to increase scouting productivity, data scale and fidelity. Here we demonstrate MAESTRO, a novel grasshopper detection framework that deploys deep learning within RBG images to detect insects. MAESTRO uses a state-of-the-art two-stage training deep learning approach. The framework can be deployed not only on desktop computers but also on edge devices without internet connection such as smartphones. MAESTRO can gather data using cloud storage for further research and in-depth analysis. In addition, we provide a challenging new open dataset (GHCID) of highly variable grasshopper populations imaged in Inner Mongolia. The detection performance of the stationary method and the mobile App are 78 and 49 percent respectively; the stationary method requires around 1000 ms to analyze a single image, whereas the mobile app uses only around 400 ms per image. The algorithms are purely data-driven and can be used for other detection tasks in agriculture (e.g. plant disease detection) and beyond. This system can play a crucial role in the collection and analysis of data to enable more effective control of this critical global pest.

The autonomous landing of an Unmanned Aerial Vehicle (UAV) on a marker is one of the most challenging problems in robotics. Many solutions have been proposed, with the best results achieved via customized geometric features and external sensors. This paper discusses for the first time the use of deep reinforcement learning as an end-to-end learning paradigm to find a policy for UAVs autonomous landing. Our method is based on a divide-and-conquer paradigm that splits a task into sequential sub-tasks, each one assigned to a Deep Q-Network (DQN), hence the name Sequential Deep Q-Network (SDQN). Each DQN in an SDQN is activated by an internal trigger, and it represents a component of a high-level control policy, which can navigate the UAV towards the marker. Different technical solutions have been implemented, for example combining vanilla and double DQNs, and the introduction of a partitioned buffer replay to address the problem of sample efficiency. One of the main contributions of this work consists in showing how an SDQN trained in a simulator via domain randomization, can effectively generalize to real-world scenarios of increasing complexity. The performance of SDQNs is comparable with a state-of-the-art algorithm and human pilots while being quantitatively better in noisy conditions.

Joint object transfer is a complex task, which is less structured and less specific than what is existing in several industrial settings. When two humans are involved in such a task, they cooperate through different modalities to understand the interaction states during operation and mutually adapt to one another's actions. Mutual adaptation implies that both partners can identify how well they collaborate (i.e. infer about the interaction state) and act accordingly. These interaction states can define whether the partners work in harmony, face conflicts, or remain passive during interaction. Understanding how two humans work together during physical interactions is important when exploring the ways a robotic assistant should operate under similar settings. This study acts as a first step to implement an automatic classification mechanism during ongoing collaboration to identify the interaction state during object co-manipulation. The classification is done on a dataset consisting of data from 40 subjects, who are partnered to form 20 dyads. The dyads experiment in a physical human-human interaction (pHHI) scenario to move an object in an haptics-enabled virtual environment to reach predefined goal configurations. In this study, we propose a sliding-window approach for feature extraction and demonstrate the online classification methodology to identify interaction patterns. We evaluate our approach using 1) a support vector machine classifier (SVMc) and 2) a Gaussian Process classifier (GPc) for multi-class classification, and achieve over ${\bf 80}\%$ accuracy with both classifiers when identifying general interaction types.

Trainable chatbots that exhibit fluent and human-like conversations remain a big challenge in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing this challenge, but its successful application remains an open question. This article describes a novel ensemble-based approach applied to value-based DRL chatbots, which use finite action sets as a form of meaning representation. In our approach, while dialogue actions are derived from sentence clustering, the training datasets in our ensemble are derived from dialogue clustering. The latter aim to induce specialised agents that learn to interact in a particular style. In order to facilitate neural chatbot training using our proposed approach, we assume dialogue data in raw text only -- without any manually-labelled data. Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent. In addition to evaluations using held-out data, our results are further supported by a human evaluation that rated dialogues in terms of fluency, engagingness and consistency -- which revealed that our proposed dialogue rewards strongly correlate with human judgements., arXiv admin note: text overlap with arXiv:1908.10331

The water-circulating heat exchangers employed in petrochemical industrials have attracted great attentions in condition monitoring and fault diagnosis. In this paper, an approach based on vibration signals is proposed. By the proposed method, vibration signals are collected for different conditions through various high-precision wireless sensors mounted on the surface of the heat exchanger. Furthermore, by analyzing the characteristics of the vibration signals, a database of fault patterns is established, which therefore provides a scheme for conditional monitoring of the heat exchanger. An experimental platform is set up to evaluate the feasibility and effectiveness of the proposed approach, and support vector machine based on dimensionless parameters is developed for fault classification. The results have shown that the proposed method is efficient and has achieved a high accuracy for benchmarking vibration signals under both normal and faulty conditions.

Highlights • Early SARS-CoV-2 prediction from monocytes-leukocytes-eosinophils variable: AUC 84%. • Prediction in regular ward using machine learning from complete blood count: AUC 94%. • Predominance of monocytes in SARS-CoV-2 positive patients similar to SARS and MERS., Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The dataset included in the analysis and training contains anonymized full blood counts results from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 rt-PCR test during a visit to the hospital. Patient data was anonymised by the hospital, clinical data was standardized to have a mean of zero and a unit standard deviation. This data was made public with the aim to allow researchers to develop ways to enable the hospital to rapidly predict and potentially identify SARS-CoV-2 positive patients. We find that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94–95%) and those not admitted to hospital or in the community (AUC = 80–86%). Here, AUC is the Area Under the receiver operating characteristics Curve and a measure for model performance. Moreover, a simple linear combination of 4 blood counts can be used to have an AUC of 85% for patients within the community. The normalised data of different blood parameters from SARS-CoV-2 positive patients exhibit a decrease in platelets, leukocytes, eosinophils, basophils and lymphocytes, and an increase in monocytes. SARS-CoV-2 positive patients exhibit a characteristic immune response profile pattern and changes in different parameters measured in the full blood count that are detected from simple and rapid blood tests. While symptoms at an early stage of infection are known to overlap with other common conditions, parameters of the full blood counts can be analysed to distinguish the viral type at an earlier stage than current rt-PCR tests for SARS-CoV-2 allow at present. This new methodology has potential to greatly improve initial screening for patients where PCR based diagnostic tools are limited.

Grasping is an essential component for robotic manipulation and has been investigated for decades. Prior work on grasping often assumes that a sufficient amount of training data is available for learning and planning robotic grasps. However, since constructing such an exhaustive training dataset is very challenging in practice, it is desirable that a robotic system can autonomously learn and improves its grasping strategy. In this paper, we address this problem using reinforcement learning. Although recent work has presented autonomous data collection through trial and error, such methods are often limited to a single grasp type, e.g., vertical pinch grasp. We present a hierarchical policy search approach for learning multiple grasping strategies. Our framework autonomously constructs a database of grasping motions and point clouds of objects to learn multiple grasping types autonomously. We formulate the problem of selecting the grasp location and grasp policy as a bandit problem, which can be interpreted as a variant of active learning. We applied our reinforcement learning to grasping both rigid and deformable objects. The experimental results show that our framework autonomously learns and improves its performance through trial and error and can grasp previously unseen objects with a high accuracy.

In this paper, we present a human-in-the-loop learning framework for mobile robots to generate effective local policies in order to recover from navigation failures in long-term autonomy. We present an analysis of failure and recovery cases derived from long-term autonomous operation of a mobile robot, and propose a two-layer learning framework that allows to detect and recover from such navigation failures. Employing a learning by demonstration (LbD) approach, our framework can incrementally learn to autonomously recover from situations it initially needs humans to help with. The learning framework allows for both real-time failure detection and regression using Gaussian processes (GPs). Our empirical results on two different failure scenarios indicate that given 40 failure state observations, the true positive rate of the failure detection model exceeds 90%, ending with successful recovery actions in more than 90% of all detected cases.

This paper presents a novel class of systems assisting diagnosis and personalised assessment of diseases in healthcare. The targeted systems are end-to-end deep neural architectures that are designed (trained and tested) and subsequently used as whole systems, accepting raw input data and producing the desired outputs. Such architectures are state-of-the-art in image analysis and computer vision, speech recognition and language processing. Their application in healthcare for prediction and diagnosis purposes can produce high accuracy results and can be combined with medical knowledge to improve effectiveness, adaptation and transparency of decision making. The paper focuses on neurodegenerative diseases, particularly Parkinson’s, as the development model, by creating a new database and using it for training, evaluating and validating the proposed systems. Experimental results are presented which illustrate the ability of the systems to detect and predict Parkinson’s based on medical imaging information.

Retail food packages contain various types of information such as food name, ingredients list and use by dates. Such information is critical to ensure proper distribution of products to the market and eliminate health risks due to erroneous mislabelling. The latter is considerably detrimental to both consumers and suppliers alike. In this paper, an adaptable deep learning based system is proposed and tested across various possible scenarios: a) for the identification of blurry images and/or missing information from food packaging photos. These were captured during the validation process in supply chains; b) for deep neural network adaptation. This was achieved through a novel methodology that utilises facets of the same convolutional neural network architecture. Latent variables were extracted from different datasets and used as input into a Λ-means clustering and Λ-nearest neighbour classification algorithm, to compute a new set of centroids which better adapts to the target dataset's distribution. Furthermore, visualisation and analysis of network adaptation provides insight into how higher accuracy was achieved when compared to the original deep neural network. The proposed system performed very well in the conducted experiments, showing that it can be deployed in real-world supply chains, for automating the verification process, cutting down costs and eliminating errors that could prove risky for public health.

As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation. We intend this paper to serve two audiences. First, we want to familiarize machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, we want to give roboticists and experts in applied artificial intelligence a broader appreciation for the frameworks and tools available for imitation learning., 187 pages. Published in Foundations and Trends in Robotics

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation. We intend this paper to serve two audiences. First, we want to familiarize machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, we want to give roboticists and experts in applied artificial intelligence a broader appreciation for the frameworks and tools available for imitation learning. We pay particular attention to the intimate connection between imitation learning approaches and those of structured prediction Daumé III et al. [2009]. To structure this discussion, we categorize imitation learning techniques based on the following key criteria which drive algorithmic decisions: 1) The structure of the policy space. Is the learned policy a time-index trajectory (trajectory learning), a mapping from observations to actions (so called behavioral cloning [Bain and Sammut, 1996]), or the result of a complex optimization or planning problem at each execution as is common in inverse optimal control methods [Kalman, 1964, Moylan and Anderson, 1973]. 2) The information available during training and testing. In particular, is the learning algorithm privy to the full state that the teacher possess? Is the learner able to interact with the teacher and gather corrections or more data? Does the learner have a (typically a priori) model of the system with which it interacts? Does the learner have access to the reward (cost) function that the teacher is attempting to optimize? 3) The notion of success. Different algorithmic approaches provide varying guarantees on the resulting learned behavior. These guarantees range from weaker (e.g., measuring disagreement with the agent’s decision) to stronger (e.g., providing guarantees on the performance of the learner with respect to a true cost function, either known or unknown). We organize our work by paying particular attention to distinction (1): dividing imitation learning into directly replicating desired behavior (sometimes called behavioral cloning) and learning the hidden objectives of the desired behavior from demonstrations (called inverse optimal control or inverse reinforcement learning [Russell, 1998]). In the latter case, behavior arises as the result of an optimization problem solved for each new instance that the learner faces. In addition to method analysis, we discuss the design decisions a practitioner must make when selecting an imitation learning approach. Moreover, application examples—such as robots that play table tennis [Kober and Peters, 2009], programs that play the game of Go [Silver et al., 2016], and systems that understand natural language [Wen et al., 2015]— illustrate the properties and motivations behind different forms of imitation learning. We conclude by presenting a set of open questions and point towards possible future research directions for machine learning.

Many of the recent trajectory optimization algorithms alternate between linear approximation of the system dynamics around the mean trajectory and conservative policy update. One way of constraining the policy change is by bounding the Kullback-Leibler (KL) divergence between successive policies. These approaches already demonstrated great experimental success in challenging problems such as end-to-end control of physical systems. However, these approaches lack any improvement guarantee as the linear approximation of the system dynamics can introduce a bias in the policy update and prevent convergence to the optimal policy. In this article, we propose a new model-free trajectory-based policy optimization algorithm with guaranteed monotonic improvement. The algorithm backpropagates a local, quadratic and time-dependent Q-Function learned from trajectory data instead of a model of the system dynamics. Our policy update ensures exact KL-constraint satisfaction without simplifying assumptions on the system dynamics. We experimentally demonstrate on highly non-linear control tasks the improvement in performance of our algorithm in comparison to approaches linearizing the system dynamics. In order to show the monotonic improvement of our algorithm, we additionally conduct a theoretical analysis of our policy update scheme to derive a lower bound of the change in policy return between successive iterations.

IEEE robotics & automation magazine 24(3), 146-156 (2017). doi:10.1109/MRA.2016.2636359, Published by IEEE, New York, NY

Purpose: In the United Kingdom (UK), the National Health Service (NHS) Diabetic Eye Screening Program o ers an annual eye examination to all people with diabetes aged 12 and over aimed at the early detection of people at high risk of visual loss due to diabetic retinopathy. The purpose of this study was to design a model for predicting patients at risk of developing retinopathy using patient characteristics and clinical measurements. Methods: We appraised data collected from the population-based Diabetic Eye Screening Program in East Anglia between 2011 and 2016. The data comprised reti- nal photographic screening results, patient characteristics of gender and age of the subject as well as duration and type of diabetes, and routine biochemical measures of Hemoglobin A1c (HbA1c), blood pressure, Albumin to Creatinine ratio (ACR), es- timated Glomerular Filtration rate (eGFR), serum creatinine, cholesterol, and Body Mass Index (BMI). Individuals were classified according to the presence or absence of retinopathy as indicated by their retinal photographic screening results. A logistic regression with Least Absolute Shrinkage and Selection Operator (lasso) regulariza- tion, random forest, gradient boosting machine, and regularized gradient boosting model were deployed and cross-validated for their predictive ability. Results: A total of 6375 subjects with recorded information for all available biochemical measures were identified from the cohorts. Of these, 5969 individuals had no signs of diabetic retinopathy. Of the remaining 406 individuals with signs of diabetic retinopathy, 352 had background diabetic retinopathy and 54 had referable diabetic retinopathy. The highest value of the ten-fold cross-validated area under the curve (AUC) of the receiver operating curve (ROC), 0.73 ± 0.03, was achieved by the gradient boosting machine and the minimum required set of variables to yield this perfor- mance included: duration of diabetes, HbA1c, ACR, and age. A subsequent analysis on the predictive power of the biochemical measures showed that when HbA1c and ACR measurements were available for longer time periods, the performance of the models was enhanced. When HbA1c and ACR measurements for a five-year period prior to the event of study were available, gradient boosting machine cross-validated AUC was 0.77 ± 0.04 in comparison to the cross-validated AUC of 0.68 ± 0.04 when only information for the one-year period for these variables was available. Similarly, an increment from 0.70 ± 0.02 to 0.75 ± 0.04 was observed with random forest. The dataset with the one-year measurements comprised 4857 subjects, of whom 4572 had no retinopathy and the remaining 285 had signs of retinopathy. The dataset with the five-year measurements comprised 757 subjects, of whom 696 had no retinopathy and the remaining 51 had signs of retinopathy. Conclusion: Patient information and routine biochemical measures can be used to identify patients at risk of developing retinopathy with a significant reduction of the number of screening visits. Keywords: area under the ROC curve (AUC), classification, gradient boosting, lasso, prevalence and risk of diabetic retinopathy, random forest, retinal screening

Learning complex control policies from non-linear and redundant sensory input is an important challenge for reinforcement learning algorithms. Non-parametric methods that approximate values functions or transition models can address this problem, by adapting to the complexity of the data set. Yet, many current non-parametric approaches rely on unstable greedy maximization of approximate value functions, which might lead to poor convergence or oscillations in the policy update. A more robust policy update can be obtained by limiting the information loss between successive state-action distributions. In this paper, we develop a policy search algorithm with policy updates that are both robust and non-parametric. Our method can learn non- parametric control policies for infinite horizon continuous Markov decision processes with non-linear and redundant sensory representations. We investigate how we can use approximations of the kernel function to reduce the time requirements of the demanding non-parametric computations. In our experiments, we show the strong performance of the proposed method, and how it can be approximated efficiently. Finally, we show that our algorithm can learn a real-robot under-powered swing-up task directly from image data.

Many studies have been conducted, investigating the effects that diabetes has to the retinal vasculature. Identifying and quantifying the retinal vascular changes remains a very challenging task, due to the heterogeneity of the retina. Monitoring the progression requires follow-up studies of progressed patients, since human retina naturally adapts to many different stimuli, making it hard to associate any changes with a disease. In this novel study, data from twenty five diabetic patients, who progressed to diabetic retinopathy, were used. The progression was evaluated using multiple geometric features, like vessels widths and angles, tortuosity, central retinal artery and vein equivalent, fractal dimension, lacunarity, in addition to the corresponding descriptive statistics of them. A statistical mixed model design was used to evaluate the significance of the changes between two periods: 3 years before the onset of diabetic retinopathy and the first year of diabetic retinopathy. Moreover, the discriminative power of these features was evaluated using a random forests classifier and also a penalized logistic regression. The area under the ROC curve after running a ten-fold cross validation was 0.7925 and 0.785 respectively.

Many reinforcement learning (RL) tasks, especially in robotics, consist of multiple sub-tasks that are strongly structured. Such task structures can be exploited by incorporating hierarchical policies that consist of gating networks and sub-policies. However, this concept has only been partially explored for real world settings and complete methods, derived from first principles, are needed. Real world settings are challenging due to large and continuous state-action spaces that are prohibitive for exhaustive sampling methods. We define the problem of learning sub-policies in continuous state action spaces as finding a hierarchical policy that is composed of a high-level gating policy to select the low-level sub-policies for execution by the agent. In order to efficiently share experience with all sub-policies, also called inter-policy learning, we treat these sub-policies as latent variables which allows for distribution of the update information between the sub-policies. We present three different variants of our algorithm, designed to be suitable for a wide variety of real world robot learning tasks and evaluate our algorithms in two real robot learning scenarios as well as several simulations and comparisons.

A decentralized approach for multi-view multi-person tracking in VSNs.Design overcomplete dictionaries matched to the structure of likelihoods functions.Obtain sparse representation of likelihoods to reduce communication in the network.Comparison on well-known L1-solvers to choose a fast and reliable method.Qualitatively and quantitatively, our framework outperforms previous approaches. Recent developments in low-cost CMOS cameras have created the opportunity of bringing imaging capabilities to sensor networks and a new field called visual sensor networks (VSNs) has emerged. VSNs consist of image sensors, embedded processors, and wireless transceivers which are powered by batteries. Since energy and bandwidth resources are limited, setting up a tracking system in VSNs is a challenging problem. In this paper, we present a framework for human tracking in VSN environments. The traditional approach of sending compressed images to a central node has certain disadvantages such as decreasing the performance of further processing (i.e., tracking) because of low quality images. Instead, in our decentralized tracking framework, each camera node performs feature extraction and obtains likelihood functions. We propose a sparsity-driven method that can obtain bandwidth-efficient representation of likelihoods extracted by the camera nodes. Our approach involves the design of special overcomplete dictionaries that match the structure of the likelihoods and the transmission of likelihood information in the network through sparse representation in such dictionaries. We have applied our method for indoor and outdoor people tracking scenarios and have shown that it can provide major savings in communication bandwidth without significant degradation in tracking performance. We have compared the tracking results and communication loads with a block-based likelihood compression scheme, a decentralized tracking method and a distributed tracking method. Experimental results show that our sparse representation framework is an effective approach that can be used together with any probabilistic tracker in VSNs.

Learning complex control policies from highdimensional sensory input is a challenge for reinforcement learning algorithms. Kernel methods that approximate values functions or transition models can address this problem. Yet, many current approaches rely on instable greedy maximization. In this paper, we develop a policy search algorithm that integrates robust policy updates and kernel embeddings. Our method can learn nonparametric control policies for infinite horizon continuous MDPs with high-dimensional sensory representations. We show that our method outperforms related approaches, and that our algorithm can learn an underpowered swing-up task task directly from highdimensional image data.

In this paper, we propose the use of Tikhonov regularization in conjunction with node count as a general complexity measure in multiobjective genetic programming. We demonstrate that employing this general complexity yields mean squared test error measures over a range of regression problems, which are typically superior to those from conventional nodecount (but never statistically worse). We also analyze the reason that our new method outperforms the conventional complexity measure and conclude that it forms a decision mechanism that balances both syntactic and semantic information.

International audience; The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method.

As the population increases in the world, the ratio of health carers is rapidly decreasing. Therefore, there is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies, usually due to stressful situations during everyday activities including work. This paper presents a machine learning approach for stress detection on people using wearable physiological sensors with the �final aim of improving their quality of life. The presented technique can monitor the state of the subject continuously and classify it into "stressful" or "non-stressful" situations. Our classification results show that this method is a good starting point towards real-time stress detection.

This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens. In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality.

Policy search is a subfield in reinforcement learning which focuses on finding good parameters for a given policy parametrization. It is well suited for robotics as it can cope with high-dimensional state and action spaces, one of the main challenges in robot learning. We review recent successes of both model-free and model-based policy search in robot learning. Model-free policy search is a general approach to learn policies based on sampled trajectories. We classify model-free methods based on their policy evaluation strategy, policy update strategy, and exploration strategy and present a unified view on existing algorithms. Learning a policy is often easier than learning an accurate forward model, and, hence, model-free methods are more frequently used in practice. However, for each sampled trajectory, it is necessary to interact with the * Both authors contributed equally. robot, which can be time consuming and challenging in practice. Modelbased policy search addresses this problem by first learning a simulator of the robot’s dynamics from data. Subsequently, the simulator generates trajectories that are used for policy learning. For both modelfree and model-based policy search methods, we review their respective properties and their applicability to robotic systems.

In this paper, we propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care, assistive living application. Initially, a single camera covering the full view of the room environment is used for the video recording of an elderly person's daily activities for a certain time period. The recorded video is then manually segmented into short video clips containing normal postures, which are used to compose the normal dataset. We use the codebook background subtraction technique to extract the human body silhouettes from the video clips in the normal dataset and information from ellipse fitting and shape description, together with position information, is used to provide features to describe the extracted posture silhouettes. The features are collected and an online one class support vector machine (OCSVM) method is applied to find the region in feature space to distinguish normal daily postures and abnormal postures such as falls. The resultant OCSVM model can also be updated by using the online scheme to adapt to new emerging normal postures and certain rules are added to reduce false alarm rate and thereby improve fall detection performance. From the comprehensive experimental evaluations on datasets for 12 people, we confirm that our proposed person-specific fall detection system can achieve excellent fall detection performance with 100% fall detection rate and only 3% false detection rate with the optimally tuned parameters. This work is a semiunsupervised fall detection system from a system perspective because although an unsupervised-type algorithm (OCSVM) is applied, human intervention is needed for segmenting and selecting of video clips containing normal postures. As such, our research represents a step toward a complete unsupervised fall detection system.

We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.

In this paper, we introduce a video-based robust fall detection system for monitoring an elderly person in a smart room environment. Video features, namely the centroid and orientation of a voxel person, are extracted. The boundary method, which is an example one class classification technique, is then used to determine whether the incoming features lie in the ‘fall region’ of the feature space, and thereby effectively distinguishing a fall from other activities, such as walking, sitting, standing, crouching or lying. Four different types of boundary methods, k-center, k-th nearest neighbor, one class support vector machine and single class minimax probability machine are assessed on representative test datasets. The comparison is made on the following three aspects: 1). True positive rate, false positive rate and geometric means in detection 2). Robustness to noise in the training dataset 3). The computational time for the test phase. From the comparison results, we show that the single class minimax probability machine achieves the best overall performance. By applying one class classification techniques with 3-d features, we can obtain a more efficient fall detection system with acceptable performance, as shown in the experimental part; besides, it can avoid the drawbacks of other traditional fall detection methods.