Reviewerly/exHarmony huggingface dataset:
Ship Detection Based on YOLOv2 for SAR Imagery

Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities, and its application for oil and ship detection has been the focus of many previous research studies. Many object detection methods ranging from traditional to deep learning approaches have been proposed. However, majority of them are computationally intensive and have accuracy problems. The huge volume of the remote sensing data also brings a challenge for real time object detection. To mitigate this problem a high performance computing (HPC) method has been proposed to accelerate SAR imagery analysis, utilizing the GPU based computing methods. In this paper, we propose an enhanced GPU based deep learning method to detect ship from the SAR images. The You Only Look Once version 2 (YOLOv2) deep learning framework is proposed to model the architecture and training the model. YOLOv2 is a state-of-the-art real-time object detection system, which outperforms Faster Region-Based Convolutional Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) methods. Additionally, in order to reduce computational time with relatively competitive detection accuracy, we develop a new architecture with less number of layers called YOLOv2-reduced. In the experiment, we use two types of datasets: A SAR ship detection dataset (SSDD) dataset and a Diversified SAR Ship Detection Dataset (DSSDD). These two datasets were used for training and testing purposes. YOLOv2 test results showed an increase in accuracy of ship detection as well as a noticeable reduction in computational time compared to Faster R-CNN. From the experimental results, the proposed YOLOv2 architecture achieves an accuracy of 90.05% and 89.13% on the SSDD and DSSDD datasets respectively. The proposed YOLOv2-reduced architecture has a similarly competent detection performance as YOLOv2, but with less computational time on a NVIDIA TITAN X GPU. The experimental results shows that the deep learning can make a big leap forward in improving the performance of SAR image ship detection.
Past, Present, and Future of Face Recognition: A Review

Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera–subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans

Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and interventional medical image analysis. Current solutions for anatomy detection are typically based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations, including the use of suboptimal feature engineering techniques and most importantly the use of computationally suboptimal search-schemes for anatomy detection. To address these issues, we propose a method that follows a new paradigm by reformulating the detection problem as a behavior learning task for an artificial agent. We couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis. In other words, an artificial agent is trained not only to distinguish the target anatomical object from the rest of the body but also how to find the object by learning and following an optimal navigation path to the target object in the imaged volumetric space. We evaluated our approach on 1487 3D-CT volumes from 532 patients, totaling over 500,000 image slices and show that it significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective, while also achieving a 20-30 percent higher detection accuracy. Most importantly, we improve the detection-speed of the reference methods by 2-3 orders of magnitude, achieving unmatched real-time performance on large 3D-CT scans.
BERT-ATTACK: Adversarial Attack Against BERT Using BERT

Adversarial attacks for discrete data (such as texts) have been proved significantly more challenging than continuous data (such as images) since it is difficult to generate adversarial samples with gradient-based methods.Current successful attack methods for texts usually adopt heuristic replacement strategies on the character or word level, which remains challenging to find the optimal solution in the massive space of possible combinations of replacements while preserving semantic consistency and language fluency.In this paper, we propose BERT-Attack, a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT.We turn BERT against its fine-tuned models and other deep neural models in downstream tasks so that we can successfully mislead the target models to predict incorrectly.Our method outperforms state-of-theart attack strategies in both success rate and perturb percentage, while the generated adversarial samples are fluent and semantically preserved.Also, the cost of calculation is low, thus possible for large-scale generations.The code is available at https://github.com/LinyangLee/BERT-Attack.
Facebook FAIR’s WMT19 News Translation Task Submission

This paper describes Facebook FAIR's submission to the WMT19 shared news translation task.We participate in four language directions, English ↔ German and English ↔ Russian in both directions.Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit.This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data.We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking.Our system improves on our previous system's performance by 4.5 BLEU points and achieves the best casesensitive BLEU score for the translation direction English→Russian.
The Energy Consumption of Blockchain Technology: Beyond Myth

Abstract When talking about blockchain technology in academia, business, and society, frequently generalizations are still heared about its – supposedly inherent – enormous energy consumption. This perception inevitably raises concerns about the further adoption of blockchain technology, a fact that inhibits rapid uptake of what is widely considered to be a groundbreaking and disruptive innovation. However, blockchain technology is far from homogeneous, meaning that blanket statements about its energy consumption should be reviewed with care. The article is meant to bring clarity to the topic in a holistic fashion, looking beyond claims regarding the energy consumption of Bitcoin, which have, so far, dominated the discussion.
Mineral commodity summaries 2020

Data sheets contain information on the domestic industry structure, Government programs, tariffs, and 5-year salient statistics for more than 90 individual minerals and materials.Mineral Industry Surveys-These periodic statistical and economic reports are designed to provide timely statistical data on production, shipments, stocks, and consumption of significant mineral commodities and are available at https://www.usgs.gov/centers/nmic/mineral-industry-surveys.The surveys are issued monthly, quarterly, or at other regular intervals.Materials Flow Studies-These publications describe the flow of minerals and materials from extraction to ultimate disposition to help better understand the economy, manage the use of natural resources, and protect the environment and are available at https://www.usgs.gov/centers/nmic/materials-flow.Recycling Reports-These studies illustrate the recycling of metal commodities and identify recycling trends and are available at https://www.usgs.gov/centers/nmic/recycling-statistics-and-information.
XNect

We present a real-time approach for multi-person 3D motion capture at over 30 fps using a single RGB camera. It operates successfully in generic scenes which may contain occlusions by objects and by other people. Our method operates in subsequent stages. The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals. We contribute a new architecture for this CNN, called SelecSLS Net , that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy. In the second stage, a fullyconnected neural network turns the possibly partial (on account of occlusion) 2D pose and 3D pose features for each subject into a complete 3D pose estimate per individual. The third stage applies space-time skeletal model fitting to the predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose, and enforce temporal coherence. Our method returns the full skeletal pose in joint angles for each subject. This is a further key distinction from previous work that do not produce joint angle results of a coherent skeleton in real time for multi-person scenes. The proposed system runs on consumer hardware at a previously unseen speed of more than 30 fps given 512x320 images as input while achieving state-of-the-art accuracy, which we will demonstrate on a range of challenging real-world scenes.
S2ORC: The Semantic Scholar Open Research Corpus

We introduce S2ORC, 1 a large corpus of 81.1M English-language academic papers spanning many academic disciplines.The corpus consists of rich metadata, paper abstracts, resolved bibliographic references, as well as structured full text for 8.1M open access papers.Full text is annotated with automaticallydetected inline mentions of citations, figures, and tables, each linked to their corresponding paper objects.In S2ORC, we aggregate papers from hundreds of academic publishers and digital archives into a unified source, and create the largest publicly-available collection of machine-readable academic text to date.We hope this resource will facilitate research and development of tools and tasks for text mining over academic text.
Blockchain: case studies in food supply chain visibility

Purpose This paper aims to investigate how blockchain has moved beyond cryptocurrencies and is being deployed to enhance visibility and trust in supply chains, their limitations and potential impact. Design/methodology/approach Qualitative analysis are undertaken via case studies drawn from food companies using semi-structured interviews. Findings Blockchain is demonstrated as an enabler of visibility in supply chains. Applications at scale are most likely for products where the end consumer is prepared to pay the premium currently required to fund the technology, e.g. baby food. Challenges remain in four areas: trust of the technology, human error and fraud at the boundaries, governance, consumer data access and willingness to pay. Research limitations/implications The paper shows that blockchain can be utilised as part of a system generating visibility and trust in supply chains. Research directs academic attention to issues that remain to be addressed. The challenges pertaining to the technology itself we believe to be generalisable; those specific to the food industry may not hold elsewhere. Practical implications From live case studies, we provide empirical evidence that blockchain provides visibility of exchanges and reliable data in fully digitised supply chains. This provides provenance and guards against counterfeit goods. However, firms will need to work to gain consumer buy-in for the technology following repeated past claims of trustworthiness. Originality/value This paper provides primary evidence from blockchain use cases “in the wild”. The exploratory case studies examine application of blockchain for supply chain visibility.
A Systematic Review of Emoji: Current Research and Future Perspectives

A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development, usage, function and application of emoji. In this review article, we provide a systematic review of the extant body of work on emoji, reviewing how they have developed, how they are used differently, what functions they have and what research has been conducted on them in different domains. Furthermore, we summarize directions for future research on this topic.
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas.The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder.On the challenging Spider dataset this framework boosts the exact match accuracy to 57.2%, surpassing its best counterparts by 8.7% absolute improvement.Further augmented with BERT, it achieves the new state-of-the-art performance of 65.6% on the Spider leaderboard.In addition, we observe qualitative improvements in the model's understanding of schema linking and alignment.Our implementation will be open-sourced at
Is Metaverse in education a blessing or a curse: a combined content and bibliometric analysis

Abstract The Metaverse has been the centre of attraction for educationists for quite some time. This field got renewed interest with the announcement of social media giant Facebook as it rebranding and positioning it as Meta. While several studies conducted literature reviews to summarize the findings related to the Metaverse in general, no study to the best of our knowledge focused on systematically summarizing the finding related to the Metaverse in education. To cover this gap, this study conducts a systematic literature review of the Metaverse in education. It then applies both content and bibliometric analysis to reveal the research trends, focus, and limitations of this research topic. The obtained findings reveal the research gap in lifelogging applications in educational Metaverse. The findings also show that the design of Metaverse in education has evolved over generations, where generation Z is more targeted with artificial intelligence technologies compared to generation X or Y. In terms of learning scenarios, there have been very few studies focusing on mobile learning, hybrid learning, and micro learning. Additionally, no study focused on using the Metaverse in education for students with disabilities. The findings of this study provide a roadmap of future research directions to be taken into consideration and investigated to enhance the adoption of the Metaverse in education worldwide, as well as to enhance the learning and teaching experiences in the Metaverse.
COVID-19 Contact Tracing and Data Protection Can Go Together

We discuss the implementation of app-based contact tracing to control the coronavirus disease (COVID-19) pandemic and discuss its data protection and user acceptability aspects.
Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation

In order to evaluate the prevalence of security and privacy practices on a representative sample of the Web, researchers rely on website popularity rankings such as the Alexa list. While the validity and representativeness of these rankings are rarely questioned, our findings show the contrary: we show for four main rankings how their inherent properties (similarity, stability, representativeness, responsiveness and benignness) affect their composition and therefore potentially skew the conclusions made in studies. Moreover, we find that it is trivial for an adversary to manipulate the composition of these lists. We are the first to empirically validate that the ranks of domains in each of the lists are easily altered, in the case of Alexa through as little as a single HTTP request. This allows adversaries to manipulate rankings on a large scale and insert malicious domains into whitelists or bend the outcome of research studies to their will. To overcome the limitations of such rankings, we propose improvements to reduce the fluctuations in list composition and guarantee better defenses against manipulation. To allow the research community to work with reliable and reproducible rankings, we provide Tranco, an improved ranking that we offer through an online service available at https://tranco-list.eu.
Virtual reality in tourism: a state-of-the-art review

Purpose Emerging technologies, such as virtual reality (VR), have been influencing both the tourism supply side and tourists alike. The purpose of this study is to analyse VR research in tourism and to provide a comprehensive state-of-the-art review. As the technological connotation of the term VR has been changing and encompasses various VR systems with different capabilities, this paper aims to provide a systematic and structured overview. The overall objective of this paper is to contribute to a thorough understanding of VR research in tourism. Design/methodology/approach This paper comprehensively reviews and analyses existing literature on VR in tourism, published from 1994 to February 2018. Using a wide variety of sources, these papers were examined so as to give a state-of-the-art literature review and to deepen one’s understanding of the diverse applications of VR in a tourism context. This paper also presents a novel classification of different VR systems according to the level of immersion and depicts their respective technological capabilities. Findings The advent of new VR hardware necessitates a distinction for different VR systems applied in the tourism sector. Research conducted during the past three years has been focussing on the application of head-mounted displays, which reflects the temporal development of VR technology. Regardless of the VR system, most studies examine VR as a marketing tool for promotion and communication purposes during the pre-travel phase, focussing on behavioural aspects. Advances in technology will yield new opportunities and application possibilities for the tourism industry. Originality/value The key contribution of this paper lies in its structural approach, which differentiates between non-, semi- and fully immersive VR systems in tourism, as well as the proposition of respective definitions. The concluding part of the paper proposes practical implications for tourism businesses together with directions for future studies.
Structural Deep Clustering Network

Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, achieves state-of-the-art performance and has attracted considerable attention. Current deep clustering methods usually boost the clustering results by means of the powerful representation ability of deep learning, e.g., autoencoder, suggesting that learning an effective representation for clustering is a crucial requirement. The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning. Motivated by the great success of Graph Convolutional Network (GCN) in encoding the graph structure, we propose a Structural Deep Clustering Network (SDCN) to integrate the structural information into deep clustering. Specifically, we design a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep neural architectures and guide the update of the whole model. In this way, the multiple structures of data, from low-order to high-order, are naturally combined with the multiple representations learned by autoencoder. Furthermore, we theoretically analyze the delivery operator, i.e., with the delivery operator, GCN improves the autoencoder-specific representation as a high-order graph regularization constraint and autoencoder helps alleviate the over-smoothing problem in GCN. Through comprehensive experiments, we demonstrate that our propose model can consistently perform better over the state-of-the-art techniques.
Towards quantum machine learning with tensor networks

Machine learning is a promising application of quantum computing, but challenges remain as near-term devices will have a limited number of physical qubits and high error rates. Motivated by the usefulness of tensor networks for machine learning in the classical context, we propose quantum computing approaches to both discriminative and generative learning, with circuits based on tree and matrix product state tensor networks that could have benefits for near-term devices. The result is a unified framework where classical and quantum computing can benefit from the same theoretical and algorithmic developments, and the same model can be trained classically then transferred to the quantum setting for additional optimization. Tensor network circuits can also provide qubit-efficient schemes where, depending on the architecture, the number of physical qubits required scales only logarithmically with, or independently of the input or output data sizes. We demonstrate our proposals with numerical experiments, training a discriminative model to perform handwriting recognition using a optimization procedure that could be carried out on quantum hardware, and testing the noise resilience of the trained model.