Reviewerly/exHarmony huggingface dataset:
Integrating aeromagnetic data and Landsat-8 imagery for detection of post-accretionary shear zones controlling hydrothermal alterations: The Allaqi-Heiani Suture zone, South Eastern Desert, Egypt

The Arabian-Nubian Shield (ANS) underwent a regional carbonation event with CO2 fluxes derived from the mantle and circulated along post-accretionary shear zones, thus affecting the redistribution of gold mineralization. Formerly, the analyses of remotely-sensed data have suggested regions of gold mineralization linked to the post-accretionary structured framework, but the results were insufficient for detailed prospecting in the ANS. In this research, aeromagnetic data and Landsat-8 imagery were integrated for delineating new high potential zones of gold mineralization in the Allaqi-Heiani Suture (AHS) zone, the ANS, South Eastern Desert (SED) of Egypt. Aeromagnetic data were enhanced using the Center for exploration targeting (CET) grid enhancement technique to detect the main structures that control hydrothermal alterations in the study area. Principal Component Analysis (PCA) technique and ratios of spectral bands were applied to Landsat-8 data for mapping zones of hydrothermal alteration associated with gold mineralization. The traced structural elements from RTP, CET and Landsat-8 maps were statistically plotted and interpreted using rose diagrams. These diagrams indicated that the NW and NE trends are the most dominant in the RTP map and that the intersection zones obtained from the CET analysis are aligned along the NW-oriented trend. While the NNW, WNW and NE trends were the most dominant ones in Landsat maps. The integration of the results indicates several new high potential zones of gold and Cu/Ni mineralization types, which are mostly concentrated at the western part of the AHS zone. The hydrothermal alteration zones and associated gold mineralization are strongly linked to NW, NE, E-W and WNW trending lineaments. The approach used in this study can be applied to other parts of the ANS and other regions with similar geological conditions around the world.
Impact of augmented reality and spatial cognition on assembly in construction

Applications of augmented reality have the potential to help radically improve construction productivity, reduce rework and improve communication of design intent. A hand-held augmented reality application that has been developed to assist in assembly and inspection of pipe spools using 3D scanning, scan-vs-BIM, flexible workflow, and a touch sensitive user interface is described here. Representing a class of augmented reality applications, it is used to examine the impact of augmented reality and its relation to human spatial cognitive abilities. Experiments are conducted with 21 professional pipe fitters and 40 engineering students, whose spatial cognitive abilities are measured, and who are then asked to assemble a complex pipe spool using conventional or augmented reality assisted means. Statistical analysis of the experimental results for these groups of subjects validates several observations. Augmented reality can help save substantial time in pipe spool assembly over the conventional methods for both untrained engineers and for well trained professional pipe fitters. Those whose cognitive abilities are measured as low experienced more benefit than the other subjects from the augmented reality application. More applications of augmented reality need to be studied to understand its potential impact on construction work.
Enabling Enterprise Blockchain Interoperability with Trusted Data Transfer (Industry Track)

The adoption of permissioned blockchain networks in enterprise settings has seen an increase in growth over the past few years. While encouraging, this is leading to the emergence of new data, asset and process silos limiting the potential value these networks bring to the broader ecosystem. Mechanisms for enabling network interoperability help preserve the benefits of independent sovereign networks, while allowing for the transfer or sharing of data, assets and processes across network boundaries. However, a naive approach to interoperability based on traditional point-to-point integration is insufficient for preserving the underlying trust decentralized networks provide. In this paper, we lay the foundation for an approach to interoperability based on a communication protocol that derives trust from the underlying network consensus protocol. We present an architecture and a set of building blocks that can be adapted for use in a range of network implementations and demonstrate a proof-of-concept for trusted data-sharing between two independent trade finance and supply-chain networks, each running on Hyperledger Fabric. We show how existing blockchain deployments can be adapted for interoperation and discuss the security and extensibility of our architecture and mechanisms.
Online Model Distillation for Efficient Video Inference

High-quality computer vision models typically address the problem of understanding the general distribution of real-world images. However, most cameras observe only a very small fraction of this distribution. This offers the possibility of achieving more efficient inference by specializing compact, low-cost models to the specific distribution of frames observed by a single camera. In this paper, we employ the technique of model distillation (supervising a low-cost student model using the output of a high-cost teacher) to specialize accurate, low-cost semantic segmentation models to a target video stream. Rather than learn a specialized student model on offline data from the video stream, we train the student in an online fashion on the live video, intermittently running the teacher to provide a target for learning. Online model distillation yields semantic segmentation models that closely approximate their Mask R-CNN teacher with 7 to 17× lower inference runtime cost (11 to 26× in FLOPs), even when the target video's distribution is non-stationary. Our method requires no offline pretraining on the target video stream, achieves higher accuracy and lower cost than solutions based on flow or video object segmentation, and can exhibit better temporal stability than the original teacher. We also provide a new video dataset for evaluating the efficiency of inference over long running video streams.
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation

In this paper, we propose the differentiable mask-matching network (DMM-Net) for solving the video object segmentation problem where the initial object masks are provided. Relying on the Mask R-CNN backbone, we extract mask proposals per frame and formulate the matching between object templates and proposals as a linear assignment problem where thA heading inside a blocke cost matrix is predicted by a deep convolutional neural network. We propose a differentiable matching layer which unrolls a projected gradient descent algorithm in which the projection step exploits the Dykstra's algorithm. We prove that under mild conditions, the matching is guaranteed to converge to the optimal one. In practice, it achieves similar performance compared to the Hungarian algorithm during inference. Meanwhile, we can back-propagate through it to learn the cost matrix. After matching, a U-Net style architecture is exploited to refine the matched mask per time step. On DAVIS 2017 dataset, DMM-Net achieves the best performance without online learning on the first frames and the 2nd best with it. Without any fine-tuning, DMM-Net performs comparably to state-of-the-art methods on SegTrack v2 dataset. At last, our differentiable matching layer is very simple to implement; we attach the PyTorch code in the supplementary material which is less than 50 lines long.
State-of-the-Art Review on Mixed Reality Applications in the AECO Industry

The ability to combine digital information with the real world enables mixed reality (MR) technology to provide a better display of information, resulting in its increasing popularity in various fields. The architecture, engineering, construction, and operation (AECO) industry is no exception. However, existing reviews on the use of MR technology can hardly keep up with the rapid development of MR applications. Therefore, a state-of-the-art review focusing on MR technology applications in the AECO industry is needed to reflect the current status of MR implementation in the AECO industry. This review is based on articles retrieved from well-acknowledged academic journals within the domain of the AECO industry. In this paper, 87 journal papers on MR applications are identified and classified into four categories: (1) applications in architecture and engineering, (2) applications in construction, (3) applications in operation, and (4) applications in multiple stages. Five basic components of MR, including spatial registration, display, user interaction, data storage, and multiuser collaboration, in each of the aforementioned 87 journal papers are identified and discussed. After reviewing the selected applications and corresponding MR components, this paper summarizes the challenges of MR development and provides insights into future trends of the MR technology in four aspects, namely: (1) accuracy of spatial registration, (2) user interface (UI), (3) data storage and transfer, and (4) multiuser collaboration.
RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes

Multi-domain image-to-image translation has gained increasing attention recently. Previous methods take an image and some target attributes as inputs and generate an output image with the desired attributes. However, such methods have two limitations. First, these methods assume binary-valued attributes and thus cannot yield satisfactory results for fine-grained control. Second, these methods require specifying the entire set of target attributes, even if most of the attributes would not be changed. To address these limitations, we propose RelGAN, a new method for multi-domain image-to-image translation. The key idea is to use relative attributes, which describes the desired change on selected attributes. Our method is capable of modifying images by changing particular attributes of interest in a continuous manner while preserving the other attributes. Experimental results demonstrate both the quantitative and qualitative effectiveness of our method on the tasks of facial attribute transfer and interpolation.
A privacy-preserving data aggregation scheme for dynamic groups in fog computing

Fog computing has garnered significant attention in recent years, since it can bridge the cloud and terminal devices and provide low latency, location awareness, and geo-distribution at the edge of the network. Data aggregation is a prime candidate for fog computing applications. However, most previous works about data aggregation do not focus on the fog computing. In addition, existing secure data aggregation schemes in fog computing usually do not support dynamic groups and arbitrary aggregation functions. In this paper, we construct concrete data encryption, data aggregation and data decryption algorithms, and further propose a privacy-preserving and collusion-resistant data aggregation scheme for dynamic groups in fog computing. Specifically, in the proposed protocol, the cloud server can periodically collect raw data and compute arbitrary aggregation functions on them. Even if some malicious terminal devices collude with the fog device or the cloud server, the honest terminal devices’ privacy cannot be breached. The fog device can filter out false data and aggregate all terminal devices’ ciphertexts to save the bandwidth. Besides, dynamic join and exit of terminal devices is achieved. Detailed security analysis shows that our scheme holds k-source anonymity. Our scheme is also demonstrated to be efficient via extensive experiments.
Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol

Nowadays, many people are facing the problem of arthritis. Regular monitoring and consultation of joint health from a specialist can help patients with this chronicle disease. The ratio of orthopedic doctors to patients with arthritis is low, worldwide. Use of smart devices can support the healthcare industry a lot. Motivated by these facts, here we propose an architecture to track the hand movements of the patient. For regular monitoring of patients with arthritis, fog and cloud gateways for real-time response generation are used. Thread protocol and Bayesian network classifier have been included in the proposed architecture to achieve reliable communication and anomaly detection, respectively. A dataset of 431 patients with arthritis is taken in real time and simulated on OMNet++ simulator. Observations show that the packet delivery ratio is improved by 15-20%, the response time is reduced by 20-30%, and the packet delivery rate is improved by 25-35%, in comparison to not using the fog and thread protocol.
Outlier detection

Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these statistics can be significantly biased because of the presence of outliers when calculating these statistics. This makes their use inaccurate. To overcome this problem, we propose a two-stage thresholding method (2T). Most obvious outliers are first removed by using a more conservative threshold, and the same process is then repeated for the processed scores. Experiments show that this two-stage approach significantly improves the results of all the three existing thresholding techniques.
Automatic Identification of Breast Ultrasound Image Based on Supervised Block-Based Region Segmentation Algorithm and Features Combination Migration Deep Learning Model

Breast cancer is a high-incidence type of cancer for women. Early diagnosis plays a crucial role in the successful treatment of the disease and the effective reduction of deaths. In this paper, deep learning technology combined with ultrasound imaging diagnosis was used to identify and determine whether the tumors were benign or malignant. First, the tumor regions were segmented from the breast ultrasound (BUS) images using the supervised block-based region segmentation algorithm. Then, a VGG-19 network pretrained on the ImageNet dataset was applied to the segmented BUS images to predict whether the breast tumor was benign or malignant. The benchmark data for bio-validation were obtained from 141 patients with 199 breast tumors, including 69 cases of malignancy and 130 cases of benign tumors. The experiment showed that the accuracy of the supervised block-based region segmentation algorithm was almost the same as that of manual segmentation; therefore, it can replace manual work. The diagnostic effect of the combination feature model established based on the depth feature of the B-mode ultrasonic imaging and strain elastography was better than that of the model established based on these two images alone. The correct recognition rate was 92.95%, and the AUC was 0.98 for the combination feature model.
Fashion++: Minimal Edits for Outfit Improvement

Given an outfit, what small changes would most improve its fashionability? This question presents an intriguing new computer vision challenge. We introduce Fashion++, an approach that proposes minimal adjustments to a full-body clothing outfit that will have maximal impact on its fashionability. Our model consists of a deep image generation neural network that learns to synthesize clothing conditioned on learned per-garment encodings. The latent encodings are explicitly factorized according to shape and texture, thereby allowing direct edits for both fit/presentation and color/patterns/material, respectively. We show how to bootstrap Web photos to automatically train a fashionability model, and develop an activation maximization-style approach to transform the input image into its more fashionable self. The edits suggested range from swapping in a new garment to tweaking its color, how it is worn (e.g., rolling up sleeves), or its fit (e.g., making pants baggier). Experiments demonstrate that Fashion++ provides successful edits, both according to automated metrics and human opinion.
Applications and research trends of digital human models in the manufacturing industry

Virtual reality (VR) has been widely used in various manufacturing industries, and VR-based virtual manufacturing has received significant attention in the current intelligent manufacturing era. Digital human models (DHMs) are essential for virtual manufacturing applications. Additionally, researching new applications of DHMs has developed into an important academic research field. This paper aims to identify the applications and research trends of DHMs in the manufacturing industry and to provide a reference for the continued development of virtual manufacturing and DHMs. We selected a total of 49 related articles from a large number of articles published between 2014 and 2019. The applications of DHMs in the manufacturing industry are analyzed from different perspectives and various relevant technical limitations are discussed. The results indicate that the applications of DHMs differ significantly between different types of fields. The automotive industry is the main application field for DHMs, and assembly/maintenance simulations and evaluations are the main application types. Additionally, there are still some limitations in the establishment of virtual environments, motion control, and DHM evaluation that should be addressed. Finally, research trends in the application of DHMs are illustrated and discussed, including the planning and assessment of human-robot collaboration systems, the combination of DHMs and augmented reality, and improved motion planning for DHMs. In summary, the application of DHMs can improve the realism and effectiveness of virtual manufacturing, and DHMs will be more widely and deeply studied and applied in various manufacturing industries in the near future.
Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding

Attributed network embedding is the task to learn a lower dimensional vector representation of the nodes of an attributed network, which can be used further for downstream network mining tasks. Nodes in a network exhibit community structure and most of the network embedding algorithms work well when the nodes, along with their attributes, adhere to the community structure of the network. But real life networks come with community outlier nodes, which deviate significantly in terms of their link structure or attribute similarities from the other nodes of the community they belong to. These outlier nodes, if not processed carefully, can even affect the embeddings of the other nodes in the network. Thus, a node embedding framework for dealing with both the link structure and attributes in the presence of outliers in an unsupervised setting is practically important. In this work, we propose a deep unsupervised autoencoders based solution which minimizes the effect of outlier nodes while generating the network embedding. We use both stochastic gradient descent and closed form updates for faster optimization of the network parameters. We further explore the role of adversarial learning for this task, and propose a second unsupervised deep model which learns by discriminating the structure and the attribute based embeddings of the network and minimizes the effect of outliers in a coupled way. Our experiments show the merit of these deep models to detect outliers and also the superiority of the generated network embeddings for different downstream mining tasks. To the best of our knowledge, these are the first unsupervised non linear approaches that reduce the effect of the outlier nodes while generating Network Embedding.
An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

Abstract Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.
Towards Segmenting Anything That Moves

Detecting and segmenting individual objects, regardless of their category, is crucial for many applications such as action detection or robotic interaction. While this problem has been well-studied under the classic formulation of spatio-temporal grouping, state-of-the-art approaches do not make use of learning-based methods. To bridge this gap, we propose a simple learning-based approach for spatio-temporal grouping. Our approach leverages motion cues from optical flow as a bottom-up signal for separating objects from each other. Motion cues are then combined with appearance cues that provide a generic objectness prior for capturing the full extent of objects. We show that our approach outperforms all prior work on the benchmark FBMS dataset. One potential worry with learning-based methods is that they might overfit to the particular type of objects that they have been trained on. To address this concern, we propose two new benchmarks for generic, moving object detection, and show that our model matches top-down methods on common categories, while significantly out-performing both top-down and bottom-up methods on never-before-seen categories.