Reviewerly/exHarmony huggingface dataset:
Multispectral and Radar Data for the Setting of Gold Mineralization in the South Eastern Desert, Egypt

Satellite-based multi-sensor data coupled with field and microscopic investigations are used to unravel the setting and controls of gold mineralization in the Wadi Beitan–Wadi Rahaba area in the South Eastern Desert of Egypt. The satellite-based multispectral and Synthetic Aperture Radar (SAR) data promoted a vibrant litho-tectonic understanding and abetted in assessing the regional structural control of the scattered gold occurrences in the study area. The herein detailed approach includes band rationing, principal component and independent component analyses, directional filtering, and automated and semi-automated lineament extraction techniques to Landsat 8- Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Phased Array L-band Synthetic Aperture Radar (PALSAR), and Sentinel-1B data. Results of optical and SAR data processed as grayscale raster images of band ratios, Relative Absorption Band Depth (RBD), and (mafic–carbonate–hydrous) mineralogical indices are used to extract the representative pixels (regions of interest). The extracted pixels are then converted to vector shape files and are finally imported into the ArcMap environment. Similarly, manually and automatically extracted lineaments are merged with the band ratios and mineralogical indices vector layers. The data fusion approach used herein reveals no particular spatial association between gold occurrences and certain lithological units, but shows a preferential distribution of gold–quartz veins in zones of chlorite–epidote alteration overlapping with high-density intersections of lineaments. Structural features including en-echelon arrays of quartz veins and intense recrystallization and sub-grain development textures are consistent with vein formation and gold deposition syn-kinematic with the host shear zones. The mineralized, central-shear quartz veins, and the associated strong stretching lineation affirm vein formation amid stress build-up and stress relaxation of an enduring oblique convergence (assigned as Najd-related sinistral transpression; ~640–610 Ma). As the main outcome of this research, we present a priority map with zones defined as high potential targets for undiscovered gold resources.
Why the Gold Standard Approach by Mammography Demands Extension by Multiomics? Application of Liquid Biopsy miRNA Profiles to Breast Cancer Disease Management

In the global context, the epidemic of breast cancer (BC) is evident for the early 21st century. Evidence shows that national mammography screening programs have sufficiently reduced BC related mortality. Therefore, the great utility of the mammography-based screening is not an issue. However, both false positive and false negative BC diagnosis, excessive biopsies, and irradiation linked to mammography application, as well as sub-optimal mammography-based screening, such as in the case of high-dense breast tissue in young females, altogether increase awareness among the experts regarding the limitations of mammography-based screening. Severe concerns regarding the mammography as the "golden standard" approach demanding complementary tools to cover the evident deficits led the authors to present innovative strategies, which would sufficiently improve the quality of the BC management and services to the patient. Contextually, this article provides insights into mammography deficits and current clinical data demonstrating the great potential of non-invasive diagnostic tools utilizing circulating miRNA profiles as an adjunct to conventional mammography for the population screening and personalization of BC management.
Exploiting Centrality Information with Graph Convolutions for Network Representation Learning

Network embedding has been proven effective to learn low-dimensional vector representations for network vertices, and recently received a tremendous amount of research attention. However, most of existing methods for network embedding merely focus on preserving the first and second order proximities between nodes, and the important properties of node centrality are neglected. Various centrality measures such as Degree, Closeness, Betweenness, Eigenvector and PageRank centralities have been designed to measure the importance of individual nodes. In this paper, we focus on a novel yet unsolved problem that aims to learn low-dimensional continuous nodes representations that not only preserve the network structure, but also keep the centrality information. We propose a generalizable model, namely GraphCSC, that utilizes both linkage information and centrality information to learn low-dimensional vector representations for network vertices. The learned embeddings by GraphCSC are able to preserve different centrality information of nodes. In addition, we further propose GraphCSC-M, a more comprehensive model that can preserve different centrality information simultaneously through learning multiple centrality-specific embeddings, and a novel attentive multi-view learning approach is developed to compress multiple embeddings of one node into a compact vector representation. Extensive experiments have been conducted to demonstrate that our model is able to preserve different centrality information of nodes, and achieves better performance on several benchmark tasks compared with recent state-of-the-art network embedding methods.
Face Recognition based Surveillance System Using FaceNet and MTCNN on Jetson TX2

Surveillance systems, in spite of the recent advances, still poses many challenges, especially in the field of patrolling or tracking of subjects through CCTV footage or any other portable drone mechanisms. Real time monitoring of public places for possible suspects are still made through manual observations in many places. The manual labour involved and the human errors that can occur makes the system less efficient. Many research attempts were made for designing foolproof systems and still going on, understanding the importance of the problem. This paper presents the implementation of an intelligent multicamera Face Recognition based surveillance system using FaceNet and MTCNN algorithm on Jetson TX2. The proposed portable system tracks the subject or the suspect with the camera ID/location together with the timestamp and logs his presence in the database, using multiple camera installation.
Distributed logistics platform based on Blockchain and IoT

This paper presents an approach to integrating Blockchain and IoT technologies into modern supply chains. We propose the concept of a new logistics platform that is built as a distributed network of nodes and offers an alternative approach dealing with the complexity of modern supply chains by breaking them into smaller, functionally independent parts. The modular structure of the platform allows users to add their own nodes or extend the functionality of the existing ones. Nodes are communicating with the use of IoT technology, which serves as a bridge between the virtual and real worlds, making this platform truly digital. Blockchain technology is not only used for writing down agreements and for making transactions, but also as a trustworthy public listing of services and information. It connects the nodes into a public and secure system that provides reliable services of a supply chain. In this work, we describe the individual nodes and their implementation and present preliminary results of experiments using a laboratory model of a logistic chain.
Slack Me If You Can! Using Enterprise Social Networking Tools in Virtual Agile Teams

Virtual teams rely on enterprise social networking tools such as Slack to collaborate efficiently. While such tools contribute to making the communication more synchronous and support distributed agile development, there are several challenges such as how to interact with each other and how to balance the communication with other types of communication mechanisms such as meetings, e-mail, and phone. In this paper, we describe and discuss how a distributed global project used Slack. Some of the challenges we identified were related to language problems, using too much direct messaging when communicating, and unbalanced activity (33% of the users accounted for 86% of the messages). The positive aspects of using the tool were increased transparency, team awareness, and informal communication. Further, Slack facilitates problem-focused communication which is essential for agile teams. Our study stresses the importance of reflecting on how virtual teams use communication tools, and we suggest that teams decide on guidelines on how to use the tools to improve their coordination.
The face of art

Facial Landmark detection in natural images is a very active research domain. Impressive progress has been made in recent years, with the rise of neural-network based methods and large-scale datasets. However, it is still a challenging and largely unexplored problem in the artistic portraits domain. Compared to natural face images, artistic portraits are much more diverse. They contain a much wider style variation in both geometry and texture and are more complex to analyze. Moreover, datasets that are necessary to train neural networks are unavailable. We propose a method for artistic augmentation of natural face images that enables training deep neural networks for landmark detection in artistic portraits. We utilize conventional facial landmarks datasets, and transform their content from natural images into "artistic face" images. In addition, we use a feature-based landmark correction step, to reduce the dependency between the different facial features, which is necessary due to position and shape variations of facial landmarks in artworks. To evaluate our landmark detection framework, we created an "Artistic-Faces" dataset, containing 160 artworks of various art genres, artists and styles, with a large variation in both geometry and texture. Using our method, we can detect facial features in artistic portraits and analyze their geometric style. This allows the definition of signatures for artistic styles of artworks and artists, that encode both the geometry and the texture style. It also allows us to present a geometric-aware style transfer method for portraits.
The development of Bitcoin futures: Exploring the interactions between cryptocurrency derivatives

We utilise a high-frequency analysis to investigate the period surrounding the establishment of two new futures contracts based on the performance of Bitcoin. Our analysis shows that there have been significant pricing effects sourced from both fraudulent and regulatory unease within the industry. While analysing breakpoints in efficiency, we verify the view that Bitcoin futures dominate price discovery relative to spot markets. However, we add to this research by finding that CBOE futures are found to be the leading source of informational flow when compared directly to their CME equivalent.
Communication Complexity of Byzantine Agreement, Revisited

As Byzantine Agreement (BA) protocols find application in large-scale decentralized cryptocurrencies, an increasingly important problem is to design BA protocols with improved communication complexity. A few existing works have shown how to achieve subquadratic BA under an adaptive adversary. Intriguingly, they all make a common relaxation about the adaptivity of the attacker, that is, if an honest node sends a message and then gets corrupted in some round, the adversary cannot erase the message that was already sent - henceforth we say that such an adversary cannot perform "after-the-fact removal". By contrast, many (super-)quadratic BA protocols in the literature can tolerate after-the-fact removal. In this paper, we first prove that disallowing after-the-fact removal is necessary for achieving subquadratic-communication BA.
Minimal Linear Codes in Odd Characteristic

In this paper, we generalize constructions in two recent works of Ding, Heng, and Zhou to any field F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , q odd, providing infinite families of minimal codes for which the Ashikhmin-Barg bound does not hold.
Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation

Semantic amodal segmentation is a recently proposed extension to instance-aware segmentation that includes the prediction of the invisible region of each object instance. We present the first all-in-one end-to-end trainable model for semantic amodal segmentation that predicts the amodal instance masks as well as their visible and invisible part in a single forward pass. In a detailed analysis, we provide experiments to show which architecture choices are beneficial for an all-in-one amodal segmentation model. On the COCO amodal dataset, our model outperforms the current baseline for amodal segmentation by a large margin. To further evaluate our model, we provide two new datasets with ground truth for semantic amodal segmentation, D2S amodal and COCOA cls. For both datasets, our model provides a strong baseline performance. Using special data augmentation techniques, we show that amodal segmentation on D2S amodal is possible with reasonable performance, even without providing amodal training data.
Modeling and Analysis of Leaky Deception Using Signaling Games With Evidence

Deception plays critical roles in economics and technology, especially in emerging interactions in cyberspace. Holistic models of deception are needed in order to analyze interactions and to design mechanisms that improve them. Game theory provides such models. In particular, existing work models deception using signaling games. But signaling games inherently model deception that is undetectable. In this paper, we extend signaling games by including a detector that gives off probabilistic warnings when the sender acts deceptively. Then, we derive pooling and partially separating equilibria of the game. We find that: 1) high quality detectors eliminate some pure-strategy equilibria; 2) detectors with high true-positive rates encourage more honest signaling than detectors with low false-positive rates; 3) receivers obtain optimal outcomes for equal-error-rate detectors; and 4) surprisingly, deceptive senders sometimes benefit from highly accurate deception detectors. We illustrate these results with an application to defensive deception for network security. Our results provide a quantitative and rigorous analysis of the fundamental aspects of detectable deception.
Label Sanitization Against Label Flipping Poisoning Attacks

Many machine learning systems rely on data collected in the wild from untrusted sources, exposing the learning algorithms to data poisoning. Attackers can inject malicious data in the training dataset to subvert the learning process, compromising the performance of the algorithm producing errors in a targeted or an indiscriminate way. Label flipping attacks are a special case of data poisoning, where the attacker can control the labels assigned to a fraction of the training points. Even if the capabilities of the attacker are constrained, these attacks have been shown to be effective to significantly degrade the performance of the system. In this paper we propose an efficient algorithm to perform optimal label flipping poisoning attacks and a mechanism to detect and relabel suspicious data points, mitigating the effect of such poisoning attacks.
Sem-GAN: Semantically-Consistent Image-to-Image Translation

Unpaired image-to-image translation is the problem of mapping an image in the source domain to one in the target domain, without requiring corresponding image pairs. To ensure the translated images are realistically plausible, recent works, such as Cycle-GAN, demands this mapping to be invertible. While, this requirement demonstrates promising results when the domains are unimodal, its performance is unpredictable in a multi-modal scenario such as in an image segmentation task. This is because, invertibility does not necessarily enforce semantic correctness. To this end, we present a semantically-consistent GAN framework, dubbed Sem-GAN, in which the semantics are defined by the class identities of image segments in the source domain as produced by a semantic segmentation algorithm. Our proposed framework includes consistency constraints on the translation task that, together with the GAN loss and the cycle-constraints, enforces that the images when translated will inherit the appearances of the target domain, while (approximately) maintaining their identities from the source domain. We present experiments on several image-to-image translation tasks and demonstrate that Sem-GAN improves the quality of the translated images significantly, sometimes by more than 20% on the FCN score. Further, we show that semantic segmentation models, trained with synthetic images translated via Sem-GAN, leads to significantly better segmentation results than other variants.
Quantum Annealing amid Local Ruggedness and Global Frustration

Quantum annealers are designed to utilize quantum tunneling to find good solutions to hard optimization problems. When constructing a family of synthetic inputs to test the potential of a quantum annealing platform, one should therefore ensure that the inputs a) have locally rugged energy landscapes so that solvers can benefit from quantum tunneling, and b) are hard optimization problems with global frustration so that they are computationally meaningful.
MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning

Multi-task learning is commonly used in autonomous driving for solving various visual perception tasks. It offers significant benefits in terms of both performance and computational complexity. Current work on multi-task learning networks focus on processing a single input image and there is no known implementation of multi-task learning handling a sequence of images. In this work, we propose a multi-stream multi-task network to take advantage of using feature representations from preceding frames in a video sequence for joint learning of segmentation, depth, and motion. The weights of the current and previous encoder are shared so that features computed in the previous frame can be leveraged without additional computation. In addition, we propose to use the geometric mean of task losses as a better alternative to the weighted average of task losses. The proposed loss function facilitates better handling of the difference in convergence rates of different tasks. Experimental results on KITTI, Cityscapes and SYNTHIA datasets demonstrate that the proposed strategies outperform various existing multi-task learning solutions.
Fuzzy comprehensive evaluation of virtual reality mine safety training system

Effective safety training is an indispensable means to ensure mine safety. Virtual Reality (VR) technology is a promising method to improve training efficiency. At present, research on VR mine safety training is limited; it focuses mainly on the implementation of a VR training system rather than the evaluation of the techniques and methods employed, which is far more important. The evaluation of a VR safety training system mainly includes the selection of indicators, calculation of weights, and a comprehensive evaluation of the system components. This paper presents a comprehensive evaluation of the VR mine safety training system with the analytic hierarchy process and the fuzzy logic technology. Firstly, by analyzing the components of the VR system, the weights of the system components and the main elements of each part are determined; the most important part is the software, which has a weight of 49.62%. Secondly, the fuzzy logic of each element will give a comprehensive evaluation result of the system. An existing VR training system is evaluated, and the result appears to be moderate. The main disadvantage of the system is the lack of realism, the absence of user data record, and the limited degree of freedom of the VR scene. Through this method, the VR training system can be scientifically and efficiently evaluated, and the system shortcomings can be derived from the evaluation results, so that the system can be improved in future, thereby ensuring the effectiveness of the system itself, and laying a solid foundation for safety training.
CNN-based Method for Lung Cancer Detection in Whole Slide Histopathology Images

Early diagnosis of lung cancer is critical for improvement of patient survival. Histopathological assessment of tissue is standard procedure needed for early diagnosis. Tissue analysis is usually performed by pathologist review, but this procedure is time-consuming and error-prone. Automated detection of cancer regions would significantly speed up the whole process and help the pathologist. In this paper we propose fully automatic method for lung cancer detection in whole slide images of lung tissue samples. Classification is performed on image patch level using convolutional neural network (CNN). Two CNN architectures (VGG and ResNet) are trained and their performance are compared. Obtained results show that CNN based approach has potential to help pathologists in lung cancer diagnosis.
Bi-directional navigation intent communication using spatial augmented reality and eye-tracking glasses for improved safety in human–robot interaction

Safety, legibility and efficiency are essential for autonomous mobile robots that interact with humans. A key factor in this respect is bi-directional communication of navigation intent, which we focus on in this article with a particular view on industrial logistic applications. In the direction robot-to-human, we study how a robot can communicate its navigation intent using Spatial Augmented Reality (SAR) such that humans can intuitively understand the robot’s intention and feel safe in the vicinity of robots. We conducted experiments with an autonomous forklift that projects various patterns on the shared floor space to convey its navigation intentions. We analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift and carried out stimulated recall interviews (SRI) in order to identify desirable features for projection of robot intentions. In the direction human-to-robot, we argue that robots in human co-habited environments need human-aware task and motion planning to support safety and efficiency, ideally responding to people’s motion intentions as soon as they can be inferred from human cues. Eye gaze can convey information about intentions beyond what can be inferred from the trajectory and head pose of a person. Hence, we propose eye-tracking glasses as safety equipment in industrial environments shared by humans and robots. In this work, we investigate the possibility of human-to-robot implicit intention transference solely from eye gaze data and evaluate how the observed eye gaze patterns of the participants relate to their navigation decisions. We again analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift for clues that could reveal direction intent. Our analysis shows that people primarily gazed on that side of the robot they ultimately decided to pass by. We discuss implications of these results and relate to a control approach that uses human gaze for early obstacle avoidance.