Reviewerly/exHarmony huggingface dataset:
Spatiotemporal phase-shifting method for accurate phase analysis of fringe pattern

Abstract High accurate phase analysis of waveforms as fringe patterns is essential for a wide range of scientific and engineering disciplines. However, precise phase analysis under extremely low signal-to-noise conditions is a challenging task for conventional phase-shifting methods. Here, a novel accurate phase recovering technique, called the spatiotemporal phase-shifting method (ST-PSM), is developed to measure the phase information robustly by utilizing two-dimensional intensity data in spatial- and temporal-domains simultaneously. Our simulation results indicated that ST-PSM had strong tolerance to random noise, and a self-neutralizing function to eliminate the periodical phase error due to the nonlinearity of detector, intensity saturation, vibration or phase-shifting error. The effectiveness was demonstrated experimentally from a non-contact shape measurement in fringe projection profilometry under extreme underexposure and overexposure recording conditions. Furthermore, by incorporating modern GPU parallel computing technology, a 4-step phase-shifted fringe pattern with 8 K image size can be calculated within one second. Due to its robustness and high accuracy with a fast calculation, therefore, we believe this technique has a significant impact on a variety of research and scientific fields.
Reverse-nearest neighborhood based oversampling for imbalanced, multi-label datasets

In this article, we present a novel reverse-nearest neighborhood based oversampling scheme for the imbalanced labels of a multi-label dataset. Reverse nearest neighborhood of a query point includes all those points which contain the query point as one of their neighbor. It facilitates us to identify an adaptive number of neighbors (according to the density and distribution of points) instead of a fixed number of neighbors. We add label-specific synthetic minority instances in the reverse nearest neighborhood of the minority points of each label. Reverse nearest neighbor configuration also detects the singular minority points, which we avoid as seed points in the oversampling phase. On the oversampled data of each label, we train and invoke a Linear Support Vector Machine to complete the learning and testing. Results of the proposed method against comparing methods on class-imbalance focused metrics indicates its competence in handling differently imbalanced multi-label datasets.
Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections

Uncertainty is a standard condition under which large parts of art-historical and curatorial knowledge creation and communication are operating. In contrast to standard levels of data quality in non-historical research domains, historical object and knowledge collections contain substantial amounts of uncertain, ambiguous, contested, or plainly missing data. Visualization approaches and interfaces to cultural collections have started to represent data quality and uncertainty metrics, yet all existing work is limited to representations for isolated metadata dimensions only. With this article, we advocate for a more systematic, synoptic and self-conscious approach to uncertainty visualization for cultural collections. We introduce omnipresent types of data uncertainty and discuss reasons for their frequent omission by interfaces for galleries, libraries, archives and museums. On this basis we argue for a coordinated counter strategy for uncertainty visualization in this field, which will also raise the efforts going into complex interface design and conceptualization. Building on the PolyCube framework for collection visualization, we showcase how multiple uncertainty representation techniques can be assessed and coordinated in a multi-perspective environment. As for an outlook, we reflect on both the strengths and limitations of making the actual wealth of data quality questions transparent with regard to different target and user groups.
Processing of Redundant Information in Airborne Electronic Systems by means of Neural Networks

Processing of redundant information of the noncollinear measuring instrument developed by the technology of microelectromechanical systems is considered. The algorithm of information processing based on neural networks is proposed. The structural schemes of neural networks for single axis and triaxial inertial sensors are given. The neural network with one hided layer for information processing of noncollinear redundant measuring instrument based on inertial sensors is represented. The features of information processing algorithm are described. A process of a training process is represented. Results of information processing by means of neural networks are given. The obtained results can be useful for improving information used for navigation of moving vehicles of the wide class.
Image Colorization by Capsule Networks

In this paper, a simple topology of Capsule Network (CapsNet) is investigated for the problem of image colorization. The generative and segmentation capabilities of the original CapsNet topology, which is proposed for image classification problem, is leveraged for the colorization of the images by modifying the network as follows: 1) The original CapsNet model is adapted to map the grayscale input to the output in the CIE Lab colorspace, 2) The feature detector part of the model is updated by using deeper feature layers inherited from VGG-19 pre-trained model with weights in order to transfer low-level image representation capability to this model, 3) The margin loss function is modified as Mean Squared Error (MSE) loss to minimize the image-to-image mapping. The resulting CapsNet model is named as Colorizer Capsule Network (ColorCapsNet). The performance of the ColorCapsNet is evaluated on the DIV2K dataset and promising results are obtained to investigate Capsule Networks further for image colorization problem.
Building a Secure Biomedical Data Sharing Decentralized App (DApp): Tutorial

Decentralized apps (DApps) are computer programs that run on a distributed computing system, such as a blockchain network. Unlike the client-server architecture that powers most internet apps, DApps that are integrated with a blockchain network can execute app logic that is guaranteed to be transparent, verifiable, and immutable. This new paradigm has a number of unique properties that are attractive to the biomedical and health care communities. However, instructional resources are scarcely available for biomedical software developers to begin building DApps on a blockchain. Such apps require new ways of thinking about how to build, maintain, and deploy software. This tutorial serves as a complete working prototype of a DApp, motivated by a real use case in biomedical research requiring data privacy. We describe the architecture of a DApp, the implementation details of a smart contract, a sample iPhone operating system (iOS) DApp that interacts with the smart contract, and the development tools and libraries necessary to get started. The code necessary to recreate the app is publicly available.
Coarse-to-fine object detection in unmanned aerial vehicle imagery using lightweight convolutional neural network and deep motion saliency

Unmanned aerial vehicles (UAVs) have been widely applied to various fields, facing mass imagery data, object detection in UAV imagery is under extensive research for its significant status in both theoretical study and practical applications. In order to achieve the accurate object detection in UAV imagery on the premise of real-time processing, a coarse-to-fine object detection method for UAV imagery using lightweight convolutional neural network (CNN) and deep motion saliency is proposed in this paper. The proposed method includes three steps: (1) Key frame extraction using image similarity measurement is performed on the UAV imagery to accelerate the successive object detection procedure; (2) Deep features are extracted by PeleeNet, a lightweight CNN, to achieve the coarse object detection on the key frames; (3) LiteFlowNet and objects prior knowledge is utilized to analyze the deep motion saliency map, which further helps to refine the detection results. The detection results on key frames propagate to the temporally nearest non-key frames to achieve the fine detection. Five experiments are conducted to verify the effectiveness of the proposed method on Stanford drone dataset (SDD). The experimental results demonstrate that the proposed method can achieve comparable detection speed but superior accuracy to six state-of-the-art methods.
Multiple Graph Convolutional Networks for Co-Saliency Detection

Recently, Graph Convolutional Networks (GCNs) have been usually utilized for graph data representation in computer vision area. However, existing graph GCNs generally use a single graph which can be not adapted for the data with multiple graphs. In this paper, we first propose a novel multiple graph convolutional network (MGCN) for multiple graph data representation and learning. MGCN propagates information/knowledge across multiple graphs and obtains a consistent representation and learning by integrating the information of multiple graphs simultaneously. Based on the proposed MGCN, we then propose a new global-local unified graph convolutional learning architecture for image co-saliency detection problem. The main benefits of the proposed co-saliency model are twofold. First, it learns an optimal superpixel feature representation for co-saliency detection problem. Second, it can well exploit both intra-image and inter-image cues for co-saliency detection via a unified network. Promising experiments demonstrate the effectiveness of the proposed MGCN based co-saliency detection approach.
Survey-based personas for a target-group-specific consideration of elderly end users of information and communication systems in the German health-care sector

In many countries, including Germany, older people are increasing in numbers, while fewer caregivers are available. A way to address the problem is to develop new medical assistance and monitoring systems that are operated by the elderly on their own, e.g. in-home aftercare systems.The development of a set of eight data-based personas in terms of a best practice approach is presented."Personas" are an integral method of the user-centered design approach. They address the problem of incomplete knowledge of individual user behaviour by introducing archetypal user groups. Thus, personas can be used at an early stage of development to raise the awareness of developers to the needs, skills, and abilities of the elderly. Personas are also a cost-effective method and quickly and easily accessible. In order to guarantee representativeness the development of personas needs to occur based on a robust data set of a certain user group.This article presents the data-driven development of eight personas. The applied data set results from a nationwide questionnaire study on the elderly's use of information and communication technology, out of elderly people in Germany. The results will be presented in terms of best practice.To conclude, survey-based personas of older end users can play an important role in the research and development of innovative devices.The personas presented in this paper can be used in research and development to raise awareness of the needs and demands of end users.
Leveraging Thermal Imaging for Autonomous Driving

In 2018, vehicles operating in an autonomous mode have been involved in at least five major accidents. Of these, one involved a pedestrian death due to a lack of timely information available to the detection system. Thermal imaging (TI) could have potentially helped with the time of detection of the pedestrian. This paper will argue how TI can provide supplementary information to existing autonomous detection systems to improve their overall performance. It will outline detection considerations for classifying objects in a thermal image which can later be used in sensor fusion applications. A new labelled dataset of thermal images will also be introduced under snowy, overcast, misty, and nighttime driving conditions. A labeled dataset of a golden retriever and a Doberman will be also be introduced. These datasets are publicly available for download.
A Variational EM Framework With Adaptive Edge Selection for Blind Motion Deblurring

Blind motion deblurring is an important problem that receives enduring attention in last decade. Based on the observation that a good intermediate estimate of latent image for estimating motion-blur kernel is not necessarily the one closest to latent image, edge selection has proven itself a very powerful technique for achieving state-of-the-art performance in blind deblurring. This paper presented an interpretation of edge selection/reweighting in terms of variational Bayes inference, and therefore developed a novel variational expectation maximization (VEM) algorithm with built-in adaptive edge selection for blind deblurring. Together with a restart strategy for avoiding undesired local convergence, the proposed VEM method not only has a solid mathematical foundation but also noticeably outperformed the state-of-the-art methods on benchmark datasets.
Multi-party quantum privacy comparison of size based on d-level GHZ states

Quantum privacy comparison (QPC) plays an important role in secret ballot elections, private auctions and so on. To date, many multi-party QPC (MQPC) protocols have been proposed to compare the equality of $$k(k\ge 3)$$ participants. However, few examples of MQPC can be used to compare the sizes or values of their privacies. In this paper, we propose a MQPC protocol by which any $$k(k\ge 3)$$ participants can compare the sizes of their privacies with executing the protocol just once. The proposed MQPC protocol takes the d-level GHZ states as quantum resources, and a semi-honest TP is introduced to help the participants to determine the relationship of their privacies. Furthermore, only single-particle unitary transformations and measurements are involved, and the participants need not to share a private key beforehand which makes the proposed protocol much more efficient. Analysis shows that our protocol is secure against internal and external attack in theory.
Face Detection Method Based on Cascaded Convolutional Networks

Deep learning achieves substantial improvements in face detection. However, the existing methods need to input fixed-size images for image processing and most methods use a single network for feature extraction, which makes the model generalization ability weak. In response to the above problems, our framework leverages a cascaded architecture with three stages of deep convolutional networks to improve detection performance. The network can predict face in a coarse-to-fine manner. We replace the standard convolution with a combination of separable convolution and residual structure in the network. Extensive experiments on the challenging FDDB and WIDER FACE benchmarks demonstrate that our method achieves competitive accuracy to the state-of-the-art techniques while keeps real-time performance.
Conveying spatial awareness cues in xR collaborations

Spatial Augmented Reality (SAR) systems can be suitably combined with other existing Extended Reality (xR) technologies to support collaboration. In existing strategies, users unencumbered by a viewing technology, such as a tablet interface or a head-mounted display, must rely on the transmission of their collaborators' positioning through interpreting a first-person camera view. This design creates a seam between a user's experience of the augmented physical environment in SAR, and their collaborators' experience inside the virtual environment. To assist in development and evaluation of spatial cues to support spatial awareness in SAR environments, an egocentric spatial-communication taxonomy is presented given two determining dimensions, a cue's attachment (physical/virtual) and animation (local/world). We developed four egocentric cues which characterize the four independent dimensions of the matrix: arrow, path, glow, and radial, and a single exocentric world in miniature visualization. Our study shows that virtual attachment cues are preferred, providing the highest accuracy, highest performance when collaborators are occluded, and produce the least mental effort when used with a single virtual collaborator. For multiple collaborators however, the virtual attached, world animated radial cue produces significant increases in mental load and reductions in preference, demonstrating the impact of visual augmentation clutter. The single exocentric visualization produced higher levels of head movement, and poorer accuracy, however the novelty of the visualization produced positive qualitative results.
User Evaluations on Sentiment-based Recommendation Explanations

The explanation interface has been recognized as important in recommender systems because it can allow users to better judge the relevance of recommendations to their preferences and, hence, make more informed decisions. In different product domains, the specific purpose of explanation can be different. For high-investment products (e.g., digital cameras, laptops), how to educate the typical type of new buyers about product knowledge and, consequently, improve their preference certainty and decision quality is essentially crucial. With this objective, we have developed a novel tradeoff-oriented explanation interface that particularly takes into account sentiment features as extracted from product reviews to generate recommendations and explanations in a category structure. In this manuscript, we first reported the results of an earlier user study (in both before-after and counter-balancing setups) that compared our prototype system with the traditional one that purely considers static specifications for explanations. This experiment revealed that adding sentiment-based explanations can significantly increase users’ product knowledge, preference certainty, perceived information usefulness, perceived recommendation transparency and quality, and purchase intention. In order to further identify the reason behind users’ perception improvements on the sentiment-based explanation interface, we performed a follow-up lab controlled eye-tracking experiment that investigated how users viewed information and compared products on the interface. This study shows that incorporating sentiment features into the tradeoff-oriented explanations can significantly affect users’ eye-gaze pattern. They were stimulated to not only notice bottom categories of products, but also, more frequently, to compare products across categories. The results also disclose users’ inherent information needs for sentiment-based explanations, as they allow users to better understand the recommended products and gain more knowledge about static specifications.
Online Multilayered Motion Planning with Dynamic Constraints for Autonomous Underwater Vehicles

Underwater robots are subject to complex hydro-dynamic forces. These forces define how the vehicle moves, so it is important to consider them when planning trajectories. However, performing motion planning considering the dynamics on the robot's onboard computer is challenging due to the limited computational resources available. In this paper an efficient motion planning framework for autonomous underwater vehicles (AUVs) is presented. By introducing a loosely coupled multilayered planning design, our framework is able to generate dynamically feasible trajectories while keeping the planning time low enough for online planning. First, a fast path planner operating in a lower-dimensional projected space computes a lead path from the start to the goal configuration. Then, the lead path is used to bias the sampling of a second motion planner, which takes into account all the dynamic constraints. Furthermore, we propose a strategy for online planning that saves computational resources by generating the final trajectory only up to a finite horizon. By using the finite horizon strategy together with the multilayered approach, the sampling of the second planner focuses on regions where good quality solutions are more likely to be found, significantly reducing the planning time. To provide strong safety guarantees our framework also incorporates the conservative approximations of inevitable collision states (icss). finally, we present simulations and experiments using a real underwater robot to demonstrate the capabilities of our framework.
UWStereoNet: Unsupervised Learning for Depth Estimation and Color Correction of Underwater Stereo Imagery

Stereo cameras are widely used for sensing and navigation of underwater robotic systems. They can provide high resolution color views of a scene; the constrained camera geometry enables metrically accurate depth estimation; they are also relatively cost-effective. Traditional stereo vision algorithms rely on feature detection and matching to enable triangulation of points for estimating disparity. However, for underwater applications, the effects of underwater light propagation lead to image degradation, reducing image quality and contrast. This makes it especially challenging to detect and match features, especially from varying viewpoints. Recently, deep learning has shown success in end-to-end learning of dense disparity maps from stereo images. Still, many state-of-the-art methods are supervised and require ground truth depth or disparity, which is challenging to gather in subsea environments. Simultaneously, deep learning has also been applied to the problem of underwater image restoration. Again, it is difficult or impossible to gather real ground truth data for this problem. In this work, we present an unsupervised deep neural network (DNN) that takes input raw color underwater stereo imagery and outputs dense depth maps and color corrected imagery of underwater scenes. We leverage a model of the process of underwater image formation, image processing techniques, as well as the geometric constraints inherent to the stereo vision problem to develop a modular network that outperforms existing methods.
PEID: A Perceptually Encrypted Image Database for Visual Security Evaluation

Perceptual image encryption provides an efficient and effective way to preserve the confidentiality of visual information, and the measurement of content leakage is of fundamental importance for perceptually encrypted images. Numerous visual security indexes (VSIs) have been proposed to evaluate visual content leakage. Due to the lack of perceptually encrypted image databases, image quality assessment (IQA) databases are widely adopted to evaluate the performance of existing VSIs. However, there are huge differences between VSIs and IQAs. The misuse of databases may lead to an inaccurate evaluation. In this paper, we propose a perceptually encrypted image database (PEID) which contains 1080 encrypted images from 20 plain images with 10 well-known perceptual encryption techniques. Both visual quality and content leakage scores of the encrypted images are obtained through a comprehensive subjective evaluation. We also propose a systemic methodology to accurately evaluate the monotonicity, fitness, and accuracy of VSIs. We conduct extensive experiments on the proposed PEID to evaluate the performance of existing state-of-the-art VSIs. We have made the database publicly available for download and hope that the proposed PEID can facilitate the research of visual security evaluation and beyond.