Reviewerly/exHarmony huggingface dataset:
Multi-Branch Cable Harness Layout Design Based on Genetic Algorithm with Probabilistic Roadmap Method

Abstract Current studies on cable harness layouts have mainly focused on cable harness route planning. However, the topological structure of a cable harness is also extremely complex, and the branch structure of the cable harness can affect the route of the cable harness layout. The topological structure design of the cable harness is a key to such a layout. In this paper, a novel multi-branch cable harness layout design method is presented, which unites the probabilistic roadmap method (PRM) and the genetic algorithm. First, the engineering constraints of the cable harness layout are presented. An obstacle-based PRM used to construct non-interference and near to the surface roadmap is then described. In addition, a new genetic algorithm is proposed, and the algorithm structure of which is redesigned. In addition, the operation probability formula related to fitness is proposed to promote the efficiency of the branch structure design of the cable harness. A prototype system of a cable harness layout design was developed based on the method described in this study, and the method is applied to two scenarios to verify that a quality cable harness layout can be efficiently obtained using the proposed method. In summary, the cable harness layout design method described in this study can be used to quickly design a reasonable topological structure of a cable harness and to search for the corresponding routes of such a harness.
Bidirectional Matrix Feature Pyramid Network for Object Detection

Feature pyramids are widely used to improve scale invariance for object detection. Most methods just map the objects to feature maps with relevant square receptive fields, but rarely pay attention to the aspect ratio variation, which is also an important property of object instances. It will lead to a poor match between rectangular objects and assigned features with square receptive fields, thus preventing from accurate recognition and location. Besides, the information propagation among feature layers is sparse, namely, each feature in the pyramid may mainly or only contain single-level information, which is not representative enough for classification and localization sub-tasks. In this paper, Bidirectional Matrix Feature Pyramid Network (BMFPN) is proposed to address these issues. It consists of three modules: Diagonal Layer Generation Module (DLGM), Top-down Module (TDM) and Bottom-up Module (BUM). First, multi-level features extracted by backbone are fed into DLGM to produce the base features. Then these base features are utilized to construct the final feature pyramid through TDM and BUM in series. The receptive fields of the designed feature layers in BMFPN have various scales and aspect ratios. Objects can be correctly assigned to appropriate and representative feature maps with relevant receptive fields depending on its scale and aspect ratio properties. Moreover, TDM and BUM form bidirectional and reticular information flow, which effectively fuses multi-level information in top-down and bottom-up manner respectively. To evaluate the effectiveness of our proposed architecture, an end-to-end anchor-free detector is designed and trained by integrating BMFPN into FCOS. And the center-ness branch in FCOS is modified with our Gaussian center-ness branch (GCB), which brings another slight improvement. Without bells and whistles, our method gains +3.3%, +2.4% and +2.6% AP on MS COCO dataset from baselines with ResNet-50, ResNet-101 and ResNeXt-101 backbones, respectively.
Bridging the gap between Natural and Medical Images through Deep Colorization

Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario, transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and color discrepancies all at once through pretrained model fine-tuning. In this work, we propose to design a dedicated network module that focuses on color adaptation, thus preprocessing the input into a form (RGB) that is closer to the domain the classification backbone was trained on. We combine learning from scratch of the color module with transfer learning of different classification backbones, obtaining an end-to-end, easy-to-train architecture for diagnostic image recognition on x-ray images. Extensive experiments showed how our approach is particularly efficient in case of data scarcity and provides a new path for further transferring the learned color information across multiple medical datasets.
Multi-Laplacian GAN with Edge Enhancement for Face Super Resolution

Face image super-resolution has become a research hotspot in the field of image processing. Nowadays, more and more researches add additional information, such as landmark, identity, to reconstruct high resolution images from low resolution ones, and have a good performance in quantitative terms and perceptual quality. However, these additional information is hard to obtain in many cases. In this work, we focus on reconstructing face images by extracting useful information from face images directly rather than using additional information. By observing edge information in each scale of face images, we propose a method to reconstruct high resolution face images with enhanced edge information. In additional, with the proposed training procedure, our method reconstructs photo-realistic images in upscaling factor 8× and outperforms state-of-the-art methods both in quantitative terms and perceptual quality.
A cross-modal edge-guided salient object detection for RGB-D image

Salient object detection simulates the attention mechanism of human behavior to grasp the most attractive objects in the images. Recently edge information has been introduced to enhance the sharp contour in RGB image saliency detection. Inspired by it, we probe into the edge-guided RGB-D image saliency detection. There are two key problems need to be solved. One is how to extract edge information from cross-modal color and depth information, the other is how to fuse the edge feature into double-stream saliency detection network. To solve these two issues, a cross-modal edge-guided salient object detection for RGB-D image is proposed. Based on double-stream U-Net framework, edge information is extracted from the deep and shallow block of both modalities. The feature in deep layer contains sematic information implying where are the object boundaries, so the features of both modalities are directly fused. The feature in shallow layer provides more detailed spatial information, so a gated fusion layer is utilized to fuse the features of both modalities to filter out the depth image noise. Extracted edge feature is fed into decoder combining with color and depth feature to achieve edge-guided cross-modal decoding process. Experimental results show our model outperforms SOTA models based on the edge guidance and gated fusion strategies in cross-modal double-stream network.
Vision-Based Layout Detection from Scientific Literature using Recurrent Convolutional Neural Networks

We present an approach for adapting convolutional neural networks for object recognition and classification to scientific literature layout detection (SLLD), a shared subtask of several information extraction problems. Scientific publications contain multiple types of information sought by researchers in various disciplines, organized into an abstract, bibliography, and sections documenting related work, experimental methods, and results; however, there is no effective way to extract this information due to their diverse layout. In this paper, we present a novel approach to developing an end-to-end learning framework to segment and classify major regions of a scientific document. We consider scientific document layout analysis as an object detection task over digital images, without any additional text features that need to be added into the network during the training process. Our technical objective is to implement transfer learning via fine-tuning of pre-trained networks and thereby demonstrate that this deep learning architecture is suitable for tasks that lack very large document corpora for training ab initio. As part of the experimental test bed for empirical evaluation of this approach, we created a merged multi-corpus data set for scientific publication layout detection tasks. Our results show good improvement with fine-tuning of a pre-trained base network using this merged data set, compared to the baseline convolutional neural network architecture.
Disocclusion Headlight for Selection Assistance in VR

We introduce the disocclusion headlight, a method for VR selection assistance based on alleviating occlusions at the center of the user's field of view. The user's visualization of the VE is modified to reduce overlap between objects. This way, selection candidate objects have larger image footprints, which facilitates selection. The modification is confined to the center of the frame, with continuity to the periphery of the frame which is rendered conventionally. The selection assistance is provided automatically, without any interaction from the user. Furthermore, our method disoccludes without destroying the local spatial relationships between selection candidates, which allows solving complex selection queries based on the relative position of objects. We have tested our method on three selection tasks, where we compared it to two state-of-the-art VR selection techniques, i.e., the alpha cursor and the flower cone. Our method showed significant advantages in terms of shorter task completion times, and of fewer selection errors.
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration

Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks. To improve uncertainty estimation, we propose On-Manifold Adversarial Data Augmentation or OMADA, which specifically attempts to generate challenging examples by following an on-manifold adversarial attack path in the latent space of an autoencoder that closely approximates the decision boundaries between classes. On a variety of datasets and for multiple network architectures, OMADA consistently yields more accurate and better calibrated classifiers than baseline models, and outperforms competing approaches such as Mixup, as well as achieving similar performance to (at times better than) postprocessing calibration methods such as temperature scaling. Variants of OMADA can employ different sampling schemes for ambiguous on-manifold examples based on the entropy of their estimated soft labels, which exhibit specific strengths for generalization, calibration of predicted uncertainty, or detection of out-of-distribution inputs.
Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation

The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose values are often missing. Our approach, denoted as MRAP, imputes the values of missing attributes by propagating information across the multi-relational structure of a knowledge graph. It employs regression functions for predicting one node attribute from another depending on the relationship between the nodes and the type of the attributes. The propagation mechanism operates iteratively in a message passing scheme that collects the predictions at every iteration and updates the value of the node attributes. Experiments over two benchmark datasets show the effectiveness of our approach.
Regression or classification? New methods to evaluate no-reference picture and video quality models

Video and image quality assessment has long been projected as a regression problem, which requires predicting a continuous quality score given an input stimulus. However, recent efforts have shown that accurate quality score regression on real-world user-generated content (UGC) is a very challenging task. To make the problem more tractable, we propose two new methods - binary, and ordinal classification - as alternatives to evaluate and compare no-reference quality models at coarser levels. Moreover, the proposed new tasks convey more practical meaning on perceptually optimized UGC transcoding, or for preprocessing on media processing platforms. We conduct a comprehensive benchmark experiment of popular no-reference quality models on recent in-the-wild picture and video quality datasets, providing reliable baselines for both evaluation methods to support further studies. We hope this work promotes coarse-grained perceptual modeling and its applications to efficient UGC processing.
Applying (3+2+1)D Residual Neural Network with Frame Selection for Hong Kong Sign Language Recognition

As reported by Hong Kong Government in 2017, there are more than 1.5 million residents suffering from hearing impairment in Hong Kong. Most of them rely on Hong Kong Sign Language for daily communication while there are only 63 registered sign language interpreters in Hong Kong. To address this specific social issue and also facilitate the effective communication between the hearing impaired and other people, this paper introduces a word-level Hong Kong Sign Language(HKSL) dataset which currently includes 45 isolated words and at least 30 sign videos per word performed by different signers(more than 1500 videos in total now and still enlarging). Based on this dataset, this paper systemically compares the performances of various deep learning approaches, including (1) 2D histogram of oriented gradients(HOG) feature/pose estimation/feature extraction with long-short term memory(LSTM) layer; (2) 3D Residual Neural Network(ResNet) (3) (2+1)D Residual Neural Network, in HKSL recognition. Meanwhile, to further improve the accuracy of sign language recognition, this paper proposes a novel method called (3+2+1)D ResNet Model with Frame Selection which adopts blurriness detection with Laplacian kernel to construct high-quality video clips and also combines both (2+1)D and 3D ResNet for recognizing the sign language. At the end, the experimental results show that the proposed method outperforms other deep learning approaches and attains an impressive accuracy of 94.6% in our dataset.
Efficient Privacy-Preserving Similarity Range Query With Quadsector Tree in eHealthcare

As a consequence of advance in the Internet of Things (IoT) and big data technology, smart eHealthcare has emerged and greatly enabled patients to enjoy high-quality healthcare services in disease prediction, clinical decision making and healthcare surveillance. Meanwhile, in order to support the dramatic increase of healthcare data, healthcare centers often outsource the on-premises data to a powerful cloud and deploy the cloud server to manage the data. However, since the healthcare data usually contain some sensitive information and also the cloud server is not fully trusted, healthcare centers need to encrypt the data before outsourcing them to the cloud. Unfortunately, data encryption inevitably hinders some advanced applications of the data like the similarity range query in cloud. Although many studies on similarity range query over encrypted data have been reported, most of them still have some limitations in security, efficiency and practicality. Aiming at this challenge, in this article, we propose a new efficient privacy-preserving similarity range query (EPSim) scheme. Specifically, we first present a modified asymmetric scalar-product-preserving encryption (ASPE) scheme and prove it is selectively secure. Then, we introduce a Quadsector tree to represent the data, and employ a filtration condition to design an efficient algorithm for efficient similarity range queries over the Quadsector tree. Finally, we propose our EPSim scheme by integrating the modified ASPE scheme and Quadsector tree. Detailed security analysis indicates that our proposed EPSim scheme is really secure. In addition, extensive performance evaluations are conducted, and the results also demonstrate it is efficient and practical.
ATVIO: Attention Guided Visual-Inertial Odometry

Visual-inertial odometry (VIO) aims to predict trajectory by ego- motion estimation. In recent years, end-to-end VIO has made great progress. However, how to handle visual and inertial measurements and make full use of the complementarity of cameras and inertial sensors remains a challenge. In the paper, we propose a novel attention guided deep framework for visual-inertial odometry (ATVIO) to improve the performance of VIO. Specifically, we extraordinarily concentrate on the effective utilization of the Inertial Measurement Unit (IMU) information. Therefore, we carefully design a one-dimension inertial feature encoder for IMU data processing. The network can extract inertial features quickly and effectively. Meanwhile, we should prevent the inconsistency problem when fusing inertial and visual features. Hence, we explore a novel cross-domain channel attention block to combine the extracted features in a more adaptive manner. Extensive experiments demonstrate that our method achieves competitive performance against state-of-the-art VIO methods.
Immersive Virtual Reality Training : Three Cases from the Danish Industry

Effective industrial training has always been vital. Recently, the need for robust, safe, repeatable and cost-effective digital applications drove many industries to explore immersive technologies and especially Immersive Virtual Reality (IVR) as a possible solution. In this paper, we depict three such cases, from the Danish industry and showcase the motivation of such adoption, technological and design characteristics, alongside perception of its applicability.
Ethics of digital contact tracing wearables

The success of digital COVID-19 contact tracing requires a strategy that successfully addresses the digital divide-inequitable access to technology such as smartphones. Lack of access both undermines the degree of social benefit achieved by the use of tracing apps, and exacerbates existing social and health inequities because those who lack access are likely to already be disadvantaged. Recently, Singapore has introduced portable tracing wearables (with the same functionality as a contact tracing app) to address the equity gap and promote public health. We argue that governments have an ethical obligation to ensure fair access to the protective benefits of contract tracing during the pandemic and that wearables are an effective way of addressing some important equity issues. The most contentious issues about contact tracing apps have been the potential infringements of privacy and individual liberty, especially where the use of apps or other technology (such as wearables or QR codes) is required for access to certain spaces. Here we argue that wearables, as opposed to apps alone, will make a digital contact tracing mandate more practical and explain some conditions under which such a mandate would be justified. We focus on Singapore as a case study that has recently deployed contact tracing wearables nationally, but also reference debate about wearables in Australia and New Zealand. Our analysis will be relevant to counties trialling similar portable tracing wearables.
ResMax: Detecting Voice Spoofing Attacks with Residual Network and Max Feature Map

The “2019 Automatic Speaker Verification Spoofing And Countermeasures Challenge” (ASVspoof) competition aimed to facilitate the design of highly accurate voice spoofing attack detection systems. the competition did not emphasize model complexity and latency requirements; such constraints are strict and integral in real-world deployment. Hence, most of the top performing solutions from the competition all used an ensemble approach, and combined multiple complex deep learning models to maximize detection accuracy - this kind of approach would sit uneasily with real-world deployment constraints. To design a lightweight system, we combined the notions of skip connection (from ResNet) and max feature map (from Light CNN), and evaluated the accuracy of the system using the ASVspoof 2019 dataset. With an optimized constant Q transform (CQT) feature, our single model achieved a replay attack detection equal error rate (EER) of 0.37% on the evaluation set, surpassing the top ensemble system from the competition that achieved an EER of 0.39%.
Remote Sensing Image Retrieval using Hybrid Visual Geometry Group Network with Relevance Feedback

The advancement in the field of remote sensing (RS) has offered a vast number of RS images with higher resolution. Nowadays, remote sensing image retrieval (RSIR) has become a challenging task for researchers due to the complicated contents and specific characteristics of RS images. Content-Based Image Retrieval (CBIR) methods create powerful tools for mining large RS image databases. Content-based RSIR aims to acquire the images with similar visual content based on a query given from a large-scale RS image library. Most of the previous works used pre-trained convolutional neural network (CNN) to form a scene illustration for the classification of RS scenes. In this work, an effective RSIR using hybrid VGGNet (Visual Geometry Group Network) CNN with red deer algorithm (RDA) is presented for the appropriate retrieval of RS images based on the query image. The proposed hybrid VGGNet CNN model integrates dimensionality reduction (DR), feature extraction (FE), loss function optimization, matching process and relevance feedback (RF) mechanism. The technique used to reduce the dimensionality is Principal Component Analysis (PCA). Next, feature extraction is processed using the combination of hybrid VGGNet CNN model, attention module and convolutional feature encoding module. The loss function is optimized using RDA, and the matching process is done by recursive density estimation (RDE). Finally, based on the feedback obtained from the user, the RF mechanism discards the images that are non-relevant and only relevant images are retrieved. The tool used for implementation is PYTHON platform. Hence, the extensive tests on University of California Merced (UCM) and Wuhan University Remote Sensing images with 19 class (WHU-RS19) databases reveal that the proposed model performs better than several state-of-the-art techniques.
Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration

Constructing effective image priors is critical to solving ill-posed inverse problems, such as image restoration. Recent works proposed to exploit image non-local similarity for inverse problems by grouping similar patches, and demonstrated state-of-the-art results in many applications. However, comparing to classic local methods based on filtering or sparsity, most of the non-local algorithms are time-consuming, mainly due to the highly inefficient and redundant block matching step, where the distance between each pair of overlapping patches needs to be computed. In this work, we propose a novel Self-Convolution operator to exploit image non-local similarity in a self-supervised way. The proposed Self-Convolution can generalize the commonly-used block matching step, and produce the equivalent results with much cheaper computation. Based on Self-Convolution, we propose an effective multi-modality image restoration scheme, which is much more efficient than conventional block matching for non-local modeling. Experimental results also demonstrate that Self-Convolution can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching. The codes will be released on GitHub.