Reviewerly/exHarmony huggingface dataset:
Combined particle image velocimetry/digital image correlation for load estimation

Particle image velocimetry (PIV) and digital image correlation (DIC) are widely used experimental techniques in fluid mechanics and structural dynamics, respectively. PIV is capable of resolving detailed velocity fields around structures, from which the hydrodynamic loading can be reconstructed. However, PIV is ill-suited to capturing the structural response, which is critical for a complete understanding of the bidirectional coupling between the fluid flow and the structural dynamics. On the other hand, DIC can accurately quantify local deformation of the structure, but does not afford the precise identification of the hydrodynamic loading due to the ill-posed nature of the inverse load estimation process. Here, we explore the feasibility of a combined PIV/DIC technique for the investigation of fluid-structure interactions. Specifically, we study fluid-structure interactions associated with a flexible cantilever plate immersed in a steady unidirectional flow. We demonstrate that the combination of pressure estimation from PIV and deformation measurement through DIC enables the precise identification of the hydrodynamic loading and structural response. The proposed methodology may help in improving our understanding of a number of fluid-structure interaction problems, such as biomimetic propulsion, aeroelasticity of airfoils, and hydrodynamic impact on marine structures.
Emotion-aware system design for the battlefield environment

With the increasing amount of information and the growth of data sources on the battlefield, it is important to achieve better battlefield decision-making through the faster and more accurate emotion awareness provided by big data. Existing battlefield systems are mainly focused on logical information acquisition, rarely considering emotion factors. In this paper, an emotion-aware system for the battlefield environment (ESBE) is proposed to achieve various functions, including target localization, target recognition, motion behavior identification, etc., to support intelligent decision-making based on the emotional state of soldiers and other valuable information about the battlefield environment. The ESBE architecture consists of three layers: data-sensing, data-transmission, and data-processing. A heterogeneous network is introduced in data-transmission layer to speed up the transmission ratio and increase the network throughput. In the data-processing layer, cloud technology is introduced to store the big data while information fusion based on a variety of technologies is executed to process the big data. Then, the elaborated function of each architecture layer, such as the fundamental process of the ESBE system, as well as some function provided by the ESBE, is presented separately. Last but not least, the ESBE system is compared with four other existing systems in terms of functions and technologies.
An eye-tracking study on the road examining the effects of handsfree phoning on visual attention

One well documented source of distraction in traffic is phoning. The negative effect of phoning is not only attributed to the effects of operating the device, but it is also caused by the division of attention between driving and the conversation. Therefore, one could argue that handsfree phoning is not per se safer than handheld phoning. Indeed, a number of studies has indicated that handsfree phoning and handheld phoning produce the same performance deficits. In the present study, we examine the effects of handsfree phoning on eye-movement patterns while driving in traffic. Thirty participants made two consecutive trips of about 14 km on a three-lane highway. During one of these two trips, they received a phone call on a handsfree device in the vehicle. The analysis of the eye-tracking variables indicate that road signs, other vehicles, and the speedometer are fixated less. At the same time, the visual scan pattern suggests a wider spatial distribution of eye fixations during a handsfree phone call. Taken together, these findings seem to suggest that during handsfree phoning, the gaze behavior is determined to a lesser degree by the driving task; drivers seem to fixate less on traffic related information. However, future research is needed to provide further evidence to confirm this conclusion and to investigate if (or under which conditions) these differences will actually result in less safer driving when phoning handsfree.
Trust Region Evolution Strategies

Evolution Strategies (ES), a class of black-box optimization algorithms, has recently been demonstrated to be a viable alternative to popular MDP-based RL techniques such as Qlearning and Policy Gradients. ES achieves fairly good performance on challenging reinforcement learning problems and is easier to scale in a distributed setting. However, standard ES algorithms perform one gradient update per data sample, which is not very efficient. In this paper, with the purpose of more efficient using of sampled data, we propose a novel iterative procedure that optimizes a surrogate objective function, enabling to reuse data sample for multiple epochs of updates. We prove monotonic improvement guarantee for such procedure. By making several approximations to the theoretically-justified procedure, we further develop a practical algorithm called Trust Region Evolution Strategies (TRES). Our experiments demonstrate the effectiveness of TRES on a range of popular MuJoCo locomotion tasks in the OpenAI Gym, achieving better performance than ES algorithm.
Examining the impact of error estimation on the effects of self-controlled feedback

Two experiments were conducted that examined the motivational and informational perspectives concerning learning advantages from self-controlled practice. Three groups were tasked with learning a novel skill; self-controlled (SC), yoked traditional (YT), and yoked with error estimation required during the acquisition phase (YE). Results from the delayed learning measures showed the YE group performed better than the SC and YT groups, for Expt. 1. A similar pattern emerged for Expt. 2, albeit, this was not significant. While there were no motivation differences across the groups in either experiment, a strong correlation in Expt. 2 was shown between error estimation capabilities, which were best for the YE group, and learning. These combined results suggest that informational processes contribute more to the self-controlled feedback learning advantage, relative to motivational contributions.
Reconstruction of isolated moving objects with high 3D frame rate based on phase shifting profilometry

Recently, moving object reconstruction based on PSP has been attracted intensive research. The errors caused by the inner movement of PSP have been addressed successfully. However, when the object with discontinuities or isolated surface is measured and the temporal phase unwrapping method is applied, additional fringe patterns are required to unwrap the phase map. The object movement between the PSP fringe patterns and additional fringe patterns will cause unwrapping errors. This paper proposes a new method to reconstruct the moving object with discontinuous or isolated surface. The object movement is tracked and the influence on the phase map caused by the movement is analyzed. Then, the phase variation caused by the movement is obtained. The phase map of the object before movement is obtained by compensating the phase map of the object after movement based on the phase variations. Finally, the object is reconstructed by dual-frequency phase unwrapping method. A new projection strategy increasing the efficiency of the 3D frame rate is also presented in this paper. The 3D frame rate achieves half of the camera capture speed. The proposed method has high potential to be applied in industrial applications for real-time measurement of moving objects. Experiments are presented to verify the effectiveness.
Laryngeal Pressure Estimation With a Recurrent Neural Network

Quantifying the physical parameters of voice production is essential for understanding the process of phonation and can aid in voice research and diagnosis. As an alternative to invasive measurements, they can be estimated by formulating an inverse problem using a numerical forward model. However, high-fidelity numerical models are often computationally too expensive for this. This paper presents a novel approach to train a long short-term memory network to estimate the subglottal pressure in the larynx at massively reduced computational cost using solely synthetic training data. We train the network on synthetic data from a numerical two-mass model and validate it on experimental data from 288 high-speed ex vivo video recordings of porcine vocal folds from a previous study. The training requires significantly fewer model evaluations compared with the previous optimization approach. On the test set, we maintain a comparable performance of 21.2% versus previous 17.7% mean absolute percentage error in estimating the subglottal pressure. The evaluation of one sample requires a vanishingly small amount of computation time. The presented approach is able to maintain estimation accuracy of the subglottal pressure at significantly reduced computational cost. The methodology is likely transferable to estimate other parameters and training with other numerical models. This improvement should allow the adoption of more sophisticated, high-fidelity numerical models of the larynx. The vast speedup is a critical step to enable a future clinical application and knowledge of parameters such as the subglottal pressure will aid in diagnosis and treatment selection.
Monitor-Based Spiking Recurrent Network for the Representation of Complex Dynamic Patterns

Neural networks are powerful computation tools for mimicking the human brain to solve realistic problems. Since spiking neural networks are a type of brain-inspired network, called the novel spiking system, Monitor-based Spiking Recurrent network (MbSRN), is derived to learn and represent patterns in this paper. This network provides a computational framework for memorizing the targets using a simple dynamic model that maintains biological plasticity. Based on a recurrent reservoir, the MbSRN presents a mechanism called a ‘monitor’ to track the components of the state space in the training stage online and to self-sustain the complex dynamics in the testing stage. The network firing spikes are optimized to represent the target dynamics according to the accumulation of the membrane potentials of the units. Stability analysis of the monitor conducted by limiting the coefficient penalty in the loss function verifies that our network has good anti-interference performance under neuron loss and noise. The results of solving some realistic tasks show that the MbSRN not only achieves a high goodness-of-fit of the target patterns but also maintains good spiking efficiency and storage capacity.
Follow the Money

The move towards digital payments and mobile money, and away from physical cash and banking services offers users opportunities to change the ways that they can spend, save and manage their money through a variety of personal financial management services. However, set against ordinary, everyday patterns of spending, saving and other forms of financial transaction, it is not clear how users might interact with, understand, or value financial management services that utilise rich data and connected digital content for their personal use. In order to explore how people might engage with such systems, we conducted a study of financial activity, following people's transactional activity over time, and interviewing them about their practices, understandings, needs, concerns and expectations of current and future financial technologies. Drawing from the everyday activities and practices observed, we identify implications for the design of digitally enabled, personal financial systems.
Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling

Plant phenotyping is the quantitative description of a plant's physiological, biochemical, and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based pipeline is presented which aims to contribute to reducing the bottleneck associated with phenotyping of architectural traits. The pipeline provides a fully automated response to photometric data acquisition and the recovery of three-dimensional (3D) models of plants without the dependency of botanical expertise, whilst ensuring a non-intrusive and non-destructive approach. Access to complete and accurate 3D models of plants supports computation of a wide variety of structural measurements. An Active Vision Cell (AVC) consisting of a camera-mounted robot arm plus combined software interface and a novel surface reconstruction algorithm is proposed. This pipeline provides a robust, flexible, and accurate method for automating the 3D reconstruction of plants. The reconstruction algorithm can reduce noise and provides a promising and extendable framework for high throughput phenotyping, improving current state-of-the-art methods. Furthermore, the pipeline can be applied to any plant species or form due to the application of an active vision framework combined with the automatic selection of key parameters for surface reconstruction.
Spatio-temporal context-aware collaborative QoS prediction

With the exponential growth of Web services, various collaborative QoS prediction methods have been suggested to make an efficient evaluation of quality-of-services (QoS) and assist users selecting appropriate services. It is still a technical challenge to be taken into account the impact of complex spatio-temporal contexts of service invocations and make use of their characteristics in the forecasting process. To this end, we propose two universal spatio-temporal context-aware collaborative neural models (STCA-1 and STCA-2) to make QoS prediction by considering invocation time and multiple spatial features both of service-side and user-side. Our proposed models utilize hierarchical neural networks to realize the embedding expression of original features, the generation of second-order features, the fusion of first-order and second-order features, the interaction between spatial features and temporal features layer by layer. In particular, attention mechanism is introduced to automatically assign weights to spatial features and realize the discriminative application in feature fusion. Experiments on a large-scale dataset demonstrate the effectiveness of the proposed method: (1) The prediction error can be significantly reduced in comparison with the baseline methods particularly in the case of sparse training data, where our models achieve a performance improvement by about 10.9–21.0% in term of MAE and NMAE, and by 2.4–7.8% in term of RMSE. (2) Attention mechanisms enable us to give intuitive explanations of the effectiveness of feature fusion more reasonably and thus strengthen the interpretability of the prediction models.
Visual Attention Prediction for Stereoscopic Video by Multi-Module Fully Convolutional Network

Visual attention is an important mechanism in the human visual system (HVS) and there have been numerous saliency detection algorithms designed for 2D images/video recently. However, the research for fixation detection of stereoscopic video is still limited and challenging due to the complicated depth and motion information. In this paper, we design a novel multi-module fully convolutional network (MM-FCN) for fixation detection of stereoscopic video. Specifically, we design a fully convolutional network for spatial saliency prediction (S-FCN), where the initial spatial saliency map of stereoscopic video is learned by image database of object detection. Furthermore, the fully convolutional network for temporal saliency prediction (T-FCN) is constructed by combining saliency results from S-FCN and motion information from video frames. Finally, the fully convolutional network for depth fixation prediction (D-FCN) is designed to compute the final fixation map of stereoscopic video by learning depth features with spatiotemporal features from T-FCN. The experimental results show that the proposed MM-FCN can predict fixation results for stereoscopic video more effectively and efficiently than other related fixation prediction methods.
Relation Extraction via Domain-aware Transfer Learning

Relation extraction in knowledge base construction has been researched for the last decades due to its applicability to many problems. Most classical works, such as supervised information extraction and distant supervision, focus on how to construct the knowledge base (KB) by utilizing the large number of labels or certain related KBs. However, in many real-world scenarios, the existing methods may not perform well when a new knowledge base is required but only scarce labels or few related KBs available. In this paper, we propose a novel approach called, Relation Extraction via Domain-aware Transfer Learning (ReTrans), to extract relation mentions from a given text corpus by exploring the experience from a large amount of existing KBs which may not be closely related to the target relation. We first propose to initialize the representation of relation mentions from the massive text corpus and update those representations according to existing KBs. Based on the representations of relation mentions, we investigate the contribution of each KB to the target task and propose to select useful KBs for boosting the effectiveness of the proposed approach. Based on selected KBs, we develop a novel domain-aware transfer learning framework to transfer knowledge from source domains to the target domain, aiming to infer the true relation mentions in the unstructured text corpus. Most importantly, we give the stability and generalization bound of ReTrans. Experimental results on the real world datasets well demonstrate that the effectiveness of our approach, which outperforms all the state-of-the-art baselines.
Design and validation of a surgical navigation system for brachytherapy based on mixed reality

Purpose An accurate position of the needle is vitally important in low‐dose‐rate seed implantation brachytherapy. Our paper aims to implement a mixed reality navigation system to assist with the placement of the I 125 seed implantation thoracoabdominal tumor brachytherapy needle and to validate the accuracy and quality of this type of method. Methods With the surgical navigation system, based on mixed reality through a novel modified multi‐information fusion method, the fusion of virtual organs and a preoperative plan for a real patient and the tracking of surgical tools in real time were achieved. Personalized image recognition and pose estimation were used to track needle punctures in real time and to perform registration processes. After a one‐time registration with a hexagonal prism tracker that used an iterative closest point algorithm, all information, including medical images and volume renderings of organs, needles, and seeds, was precisely merged with the patient. Doctors were able to observe the tumor target and to visualize the preoperative plan. This system was validated in both phantom and animal experiments. The accuracy of this system was validated by calculating the positional and rotational error of each needle insertion. The accuracy of implantation of each seed was determined in an animal experiment to test the accuracy in low‐dose‐rate brachytherapy. The efficiency of this system was also validated through time consumption assessments. Results In the phantom experiment, the average error of the needle locations was 0.664 mm and the angle error was 4.74°, average time consumption was 16.1 min with six needles inserted. Based on the results of the animal experiment, the accuracy of the needle insertion was 1.617 mm, while the angle error was 5.574° and the average error of the seed positions was 1.925 mm. Conclusions This paper describes the design and experimental validation of a novel surgical navigation system based on mixed reality for I 125 seed brachytherapy for thoracoabdominal tumors. This system was validated through a series of experiments, including phantom experiments and animal experiments. Compared with the traditional image‐guided system, the procedure presented here is convenient, displays clinically acceptable accuracy and reduces the number of CT scans, allowing doctors to perform surgery based on a visualized plan. All the experimental results indicated that the procedure is ready to be applied in further clinical studies.
Pay Attention! - Robustifying a Deep Visuomotor Policy Through Task-Focused Visual Attention

Several recent studies have demonstrated the promise of deep visuomotor policies for robot manipulator control. Despite impressive progress, these systems are known to be vulnerable to physical disturbances, such as accidental or adversarial bumps that make them drop the manipulated object. They also tend to be distracted by visual disturbances such as objects moving in the robot's field of view, even if the disturbance does not physically prevent the execution of the task. In this paper, we propose an approach for augmenting a deep visuomotor policy trained through demonstrations with Task Focused visual Attention (TFA). The manipulation task is specified with a natural language text such as "move the red bowl to the left". This allows the visual attention component to concentrate on the current object that the robot needs to manipulate. We show that even in benign environments, the TFA allows the policy to consistently outperform a variant with no attention mechanism. More importantly, the new policy is significantly more robust: it regularly recovers from severe physical disturbances (such as bumps causing it to drop the object) from which the baseline policy, i.e. with no visual attention, almost never recovers. In addition, we show that the proposed policy performs correctly in the presence of a wide class of visual disturbances, exhibiting a behavior reminiscent of human selective visual attention experiments.
Crowd Counting via Multi-view Scale Aggregation Networks

Crowd counting, aiming at estimating the total number of people in unconstrained crowded scenes, has increasingly received attention. But it is greatly challenged by the huge variation in people scale. In this paper, we propose a novel Multi-View Scale Aggregation Network (MVSAN), which handle the scale variation from feature, input and criterion view comprehensively. Firstly, we design a simple but effective Multi-Scale Feature Encoder, which exploits dilated convolution layers with various dilation rates to improve the representation ability and scale diversity of features. Secondly, we feed multiple scales of input images into networks to generate high-quality density maps in a coarse-to-fine manner. Finally, we propose a Multi-Scale Structural Similarity loss to force our networks to learn the local correlation of density maps. Extensive experiments on two standard benchmarks show that the proposed method can generate high-quality crowd density map and accurate count estimation, outperforming the state-of-the-art methods with a large margin.