Reviewerly/exHarmony huggingface dataset:
Toward Efficient Object Detection in Aerial Images Using Extreme Scale Metric Learning

In aerial image object detection, how to efficiently detect different size objects in input images of different scales and obtain a unified multi-scale representation of the object is an important issue. Existing methods rarely consider the connection between multi-scale training and multi-scale inference, and do not well optimize the constraint of input object samples in the multi-scale training process, which limits the performance of multi-scale representation. In this study, an efficient object detection algorithm for aerial images is proposed to alleviate this problem. Firstly, we propose to use metric learning to obtain the scale representation boundary of each object class, reduce the support of indistinguishable objects at extreme scales in the training process, and enhance the effect of multi-scale representation. Secondly, indistinguishable small objects are merged into small object regions, and these regions are trained to recommend the detector to detect small objects on the following high-resolution scale. Thus, a reasonable association between multi-scale training and inference is established, and the efficiency of multi-scale inference is considerably improved. The proposed algorithm has been tested on three popular aerial image datasets, including VisDrone, DOTA and UAVDT. Experimental results show that it can improve the detection accuracy and reduce the number of processing pixels.
A study of person re-identification design characteristics for aerial data

As the technological advances of the last decade have led to increased performance and availability of video cameras, along with the rise of deep learning-based image recognition, the task of person re-identification has almost exclusively been studied on datasets with ground-based, static camera settings. Yet re-identification applications on aerial-based data captured by Unmanned Aerial Vehicles (UAVs) can be particularly valuable for monitoring public events, border protection, and law enforcement. For a long time no publicly available UAV-based re-identification datasets of sufficient size for modern machine learning techniques existed, which prevented research in this area. Recently, however, two new large-scale UAV-based datasets have been released. We examine re-identification performances of common neural networks on the newly released PRAI-1581 and P-DESTRE aerial-based datasets for UAV-related error sources and data augmentation strategies to increase robustness against them. Our findings of common error sources for these UAV-based datasets include occlusions, camera angles, bad poses, and low resolutions. Furthermore, data augmentation techniques such as rotating images during training prove to be a promising aid for training on the UAV-based data with varying camera angles. By carefully selecting robust networks in addition to choosing adequate training parameters and data augmentation strategies we are able to surpass the original re-identification accuracies published by the authors of the PRAI-1581 and the P-DESTRE dataset respectively.
GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval

A major challenge of research on non-English machine reading for question answering (QA) is the lack of annotated datasets. In this paper, we present GermanQuAD, a dataset of 13,722 extractive question/answer pairs. To improve the reproducibility of the dataset creation approach and foster QA research on other languages, we summarize lessons learned and evaluate reformulation of question/answer pairs as a way to speed up the annotation process. An extractive QA model trained on GermanQuAD significantly outperforms multilingual models and also shows that machine-translated training data cannot fully substitute hand-annotated training data in the target language. Finally, we demonstrate the wide range of applications of GermanQuAD by adapting it to GermanDPR, a training dataset for dense passage retrieval (DPR), and train and evaluate one of the first non-English DPR models.
Path Planning and Control Strategy Design for Mobile Robot Based on Hybrid Swarm Optimization Algorithm

This paper presents an improvement of the actual output trajectory tracking performance of a mobile robot based on convolutional neural network controller with off-line and on-line tuning Back-Propagation algorithms.The goals of this strategy are to find the optimal path to direct its movement and to design Convolutional Neural Network Trajectory Tracking (CNNTT) controller in order to control the nonlinear kinematics mobile robot system.Therefore, a hybrid swarm optimization algorithm uses for solving the two most important problems of path planning; the first is that the path must avoid collision with obstacles, and the second it must reduce the length of the path to a minimum.This paper will discuss the finding of the shortest path with the optimum cost function by using three optimizations' algorithms; Chaotic Particle Swarm Optimization (CPSO) algorithm, A-star algorithm, and a hybrid swarm optimization algorithm (ACPSO).The task of the proposed feedback (CNNTT) controller is to obtain precisely and quickly the robust left and right wheels velocity which are used to control the position and orientation of the mobile robot system.These algorithms are simulated by MATLAB in a fixed obstacles environment to show the effectiveness of the hybrid swarm optimization algorithm in terms of the minimum number of an evaluation function and the shortest path length as well as the results of the proposed method showing that the (CNNTT) controller is accurate in terms of the mobile robot follows the desired paths quickly through fast obtaining the (CNNTT) controller's parameters and a smooth linear wheels velocity actions are generated for mobile robot system with minimum number of cost-function evolutions that minimized the tracking error x-position around 4 cm and y-position around 2.5 cm and zero approximately orientation error as well as no oscillation in the responses.Finally, we confirm the effectiveness of the numerical simulation results of the proposed control strategy through comparison other types of controller simulation results.
Visualization Analysis of Artificial Intelligence Technology in Higher Education Based on SSCI and SCI Journals from 2009 to 2019

The application of artificial intelligence technology in higher education has study value and study space. The study mainly used core data collection of the Web of Science database based on SSCI and SCI journals and based on “artificial intelligence” and “higher education” these two subjects retrieval for 2009 to 2019. In addition, It used keyword co-occurrence function and cluster analysis function of CiteSpace Knowledge Graph software for visualization analysis and detailed analysis of study hotspots. The study elaborated on the application of artificial intelligence technology in higher education, and gained study hotspots such as machine learning, augmented reality, and learning management systems, and briefly discussed the impact of artificial intelligence on higher education.
Parity Quantum Optimization: Compiler

We introduce parity quantum optimization with the aim of solving optimization problems consisting of arbitrary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-body interactions and side conditions using planar quantum chip architectures. The method introduces a decomposition of the problem graph with arbitrary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-body terms using generalized closed cycles of a hypergraph. Side conditions of the optimization problem in form of hard constraints can be included as open cycles containing the terms involved in the side conditions. The generalized parity mapping thus circumvents the need to translate optimization problems to a quadratic unconstrained binary optimization problem (QUBO) and allows for the direct encoding of higher-order constrained binary optimization problems (HCBO) on a square lattice and full parallelizability of gates.
Learning spatiotemporal features of DSA using 3D CNN and BiConvGRU for ischemic moyamoya disease detection

Background Moyamoya disease (MMD) is a serious intracranial cerebrovascular disease. Cerebral hemorrhage caused by MMD will bring life risk to patients. Therefore, MMD detection is of great significance in the prevention of cerebral hemorrhage. In order to improve the accuracy of digital subtraction angiography (DSA) in the diagnosis of ischemic MMD, in this paper, a deep network architecture combined with 3D convolutional neural network (3D CNN) and bidirectional convolutional gated recurrent unit (BiConvGRU) is proposed to learn the spatiotemporal features for ischemic MMD detection.Methods Firstly, 2D convolutional neural network (2D CNN) is utilized to extract spatial features for each frame of DSA. Secondly, the long-term spatiotemporal features of DSA sequence are extracted by BiConvGRU. Thirdly, the short-term spatiotemporal features of DSA are further extracted by 3D convolutional neural network (3D CNN). In addition, different features are extracted when gray images and optical flow images pass through the network, and multiple features are extracted by features fusion. Finally, the fused features are utilized to classify.Results The proposed method was quantitatively evaluated on a data sets of 630 cases. The experimental results showed a detection accuracy of 0.9788, sensitivity and specificity were 0.9780 and 0.9796, respectively, and area under curve (AUC) was 0.9856. Compared with other methods, we can get the highest accuracy and AUC.Conclusions The experimental results show that the proposed method is stable and reliable for ischemic MMD detection, which provides an option for doctors to accurately diagnose ischemic MMD.
Virtual World as an Interactive Safety Training Platform

Virtual training platform allows interactive and engaging learning through practice without exposing trainees to hazards. In the recent pandemic (COVID-19) situation, online training is gaining importance as it allows learning with social distancing. This research study develops two online training modes—slide-based and virtual world—and assesses them on factors such as knowledge retention, engagement, and attention. Fire safety and emergency evacuation procedures were selected for online training development, focusing on a university community. A Lean Startup methodology was employed to develop training content for virtual and slide-based safety training (SBST). A virtual university building was developed with 15 learning objectives on fire safety. An empirical evaluation of the training modes was conducted with 143 participants. The results validated that a Virtual Safety World (VSW) can provide the same knowledge as SBST but can do so in a more engaging manner. Retention of concepts after a month was higher in VSW participants. The participants’ attention levels, measured by employing qEEG, showed that participants exhibited better-sustained attention while in VSW than in SBST mode. In addition, initial studies of the virtual training platform, designed to be adaptive to the user, are performed using deep learning and qEEG.
Deposit classification scheme for the Critical Minerals Mapping Initiative Global Geochemical Database

A challenge for the global economy is to meet the growing demand for commodities used in today's advanced technologies.Critical minerals are commodities (for example, elements, compounds, minerals) deemed vital to the economic and national security of individual countries that are vulnerable to supply disruption.The national geological agencies of Australia, Canada, and the United States recently joined forces to advance understanding and foster development of critical mineral resources in their respective countries through the Critical Minerals Mapping Initiative (CMMI).An initial goal of the CMMI is to fill the knowledge gap on the abundance of critical minerals in ores.To do this, the CMMI compiled modern multielement geochemical data generated by each agency on ore samples collected from historical and active mines and prospects from around the world.To identify relationships between critical minerals, deposit types, deposit environments, and mineral systems, a unified deposit classification scheme was needed.This report describes the scheme developed by the CMMI to classify the initial release of geochemical data.In 2021, the resulting database-along with basic query, statistical analysis, and display tools-will be served to the public through a web-based portal managed by Geoscience Australia.The database will enable users to trace critical minerals through mineral systems and identify individual deposits or deposit types that are potential sources of critical minerals.
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers

The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains.Recently, large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQL parsing, improving zero-shot generalization to unseen databases.In this work, we examine the challenges that still prevent these techniques from practical deployment.First, we present KaggleDBQA, a new cross-domain evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions.Second, we re-examine the choice of evaluation tasks for text-to-SQL parsers as applied in real-life settings.Finally, we augment our in-domain evaluation task with database documentation, a naturally occurring source of implicit domain knowledge.We show that KaggleDBQA presents a challenge to state-ofthe-art zero-shot parsers but a more realistic evaluation setting and creative use of associated database documentation boosts their accuracy by over 13.2%, doubling their performance.
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger’s Adversarial Attacks

The Universal Trigger (UniTrigger) is a recently-proposed powerful adversarial textual attack method.Utilizing a learning-based mechanism, UniTrigger generates a fixed phrase that, when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class.To defend against this attack that can cause significant harm, in this paper, we borrow the "honeypot" concept from the cybersecurity community and propose DARCY, a honeypot-based defense framework against UniTrigger.DARCY greedily searches and injects multiple trapdoors into an NN model to "bait and catch" potential attacks.Through comprehensive experiments across four public datasets, we show that DARCY detects UniTrigger's adversarial attacks with up to 99% TPR and less than 2% FPR in most cases, while maintaining the prediction accuracy (in F1) for clean inputs within a 1% margin.We also demonstrate that DARCY with multiple trapdoors is also robust to a diverse set of attack scenarios with attackers' varying levels of knowledge and skills.
Improving BERT with Syntax-aware Local Attention

Pre-trained Transformer-based neural language models, such as BERT, have achieved remarkable results on varieties of NLP tasks.Recent works have shown that attention-based models can benefit from more focused attention over local regions.Most of them restrict the attention scope within a linear span, or confine to certain tasks such as machine translation and question answering.In this paper, we propose a syntax-aware local attention, where the attention scopes are restrained based on the distances in the syntactic structure.The proposed syntax-aware local attention can be integrated with pretrained language models, such as BERT, to render the model to focus on syntactically relevant words.We conduct experiments 1 on various single-sentence benchmarks, including sentence classification and sequence labeling tasks.Experimental results show consistent gains over BERT on all benchmark datasets.The extensive studies verify that our model achieves better performance owing to more focused attention over syntactically relevant words.
Clinical Manifestations of Ultrasonic Virtual Reality in the Diagnosis and Treatment of Cardiovascular Diseases

On a global scale, cardiovascular disease has become one of the most serious diseases that endangers human health and causes death and seriously threatens human life and health. If we can make accurate, timely, and effective judgments on cardiovascular-related parameters and take corresponding effective measures, the incidence of cardiovascular diseases can be reduced to a large extent. Based on this, this paper proposes the clinical application research of ultrasound virtual reality technology in the diagnosis and treatment of cardiovascular diseases. This article uses literature methods, experimental research methods, mathematical statistical analysis methods, and other research methods and in-depth study of virtual reality technology, cardiovascular disease, and other theoretical knowledge and briefly introduces ultrasound image denoising algorithms, such as bilateral filtering and PM model. And on this basis, it establishes clinical trials of ultrasound virtual reality technology in the diagnosis and treatment of cardiovascular diseases. This article mainly analyzes the application of virtual reality technology, technology comparison, and the experimental results carried out in this article. From the survey results, the total prevalence of hypertension was 25.1%, and the prevalence of males and females was 25.9% and 24.4%, respectively; the diagnostic accuracy rate of the experimental group reached 85.39%, while the diagnostic accuracy rate of the control group was 76.8%. It shows that the use of ultrasound virtual reality technology for disease diagnosis can effectively improve the accuracy of cardiovascular disease diagnosis and reduce the proportion of misdiagnosis and missed detection.
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages

Model-agnostic meta-learning (MAML) has been recently put forth as a strategy to learn resource-poor languages in a sample-efficient fashion.Nevertheless, the properties of these languages are often not well represented by those available during training.Hence, we argue that the i.i.d.assumption ingrained in MAML makes it ill-suited for cross-lingual NLP.In fact, under a decision-theoretic framework, MAML can be interpreted as minimising the expected risk across training languages (with a uniform prior), which is known as Bayes criterion.To increase its robustness to outlier languages, we create two variants of MAML based on alternative criteria: Minimax MAML reduces the maximum risk across languages, while Neyman-Pearson MAML constrains the risk in each language to a maximum threshold.Both criteria constitute fully differentiable two-player games.In light of this, we propose a new adaptive optimiser solving for a local approximation to their Nash equilibrium.We evaluate both model variants on two popular NLP tasks, part-of-speech tagging and question answering.We report gains for their average and minimum performance across low-resource languages in zeroand few-shot settings, compared to joint multisource transfer and vanilla MAML.The code for our experiments is available at https:// github.
Proceeding with caution - a survey on central bank digital currency

The hypothetical benefits and risks of central bank digital currencies are being widely discussed. This BIS paper adds to these discussions by taking stock of how progress and plans in this area are developing, based on a global survey of central banks. Responses show that central banks are proceeding with caution and most are only at a conceptual stage with their work. However, a handful have moved to considering practical issues and a couple of central banks with idiosyncratic circumstances might issue a digital currency in the short or medium term.
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting Agent

The objective of most Reinforcement Learning painting agents is to minimize the loss between a target image and the paint canvas. Human painter artistry emphasizes important features of the target image rather than simply reproducing it. Using adversarial or L2 losses in the RL painting models, although its final output is generally a work of finesse, produces a stroke sequence that is vastly different from that which a human would produce since the model does not have knowledge about the abstract features in the target image. In order to increase the human-like planning of the model without the use of expensive human data, we introduce a new loss function for use with the model's reward function: Content Masked Loss. In the context of robot painting, Content Masked Loss employs an object detection model to extract features which are used to assign higher weight to regions of the canvas that a human would find important for recognizing content. The results, based on 332 human evaluators, show that the digital paintings produced by our Content Masked model show detectable subject matter earlier in the stroke sequence than existing methods without compromising on the quality of the final painting. Our code is available at https://github.com/pschaldenbrand/ContentMaskedLoss.
Understanding Neural Networks and Individual Neuron Importance via Information-Ordered Cumulative Ablation

In this work, we investigate the use of three information-theoretic quantities-entropy, mutual information with the class variable, and a class selectivity measure based on Kullback-Leibler (KL) divergence-to understand and study the behavior of already trained fully connected feedforward neural networks (NNs). We analyze the connection between these information-theoretic quantities and classification performance on the test set by cumulatively ablating neurons in networks trained on MNIST, FashionMNIST, and CIFAR-10. Our results parallel those recently published by Morcos et al., indicating that class selectivity is not a good indicator for classification performance. However, looking at individual layers separately, both mutual information and class selectivity are positively correlated with classification performance, at least for networks with ReLU activation functions. We provide explanations for this phenomenon and conclude that it is ill-advised to compare the proposed information-theoretic quantities across layers. Furthermore, we show that cumulative ablation of neurons with ascending or descending information-theoretic quantities can be used to formulate hypotheses regarding the joint behavior of multiple neurons, such as redundancy and synergy, with comparably low computational cost. We also draw connections to the information bottleneck theory for NNs.
Assessment of a Location-Based Mobile Augmented-Reality Game by Adult Users with the ARCS Model

In mobile augmented reality (MAR) games, learning by doing is important to supplement the theoretical knowledge with practical exercise in order to maximize the learning outcome. However, in many fields, the users are not able to apply their knowledge in practical ways, despite having achieved a good understanding of the theoretical fundamentals and this is even more important to adult learners. The aim of this research is to examine young, middle-aged and elderly adults’ opinions about the location-based MAR game Ingress, by applying John Keller’s “ARCS learning motivation model” (Attention, Relevance, Confidence and Satisfaction). The users’ responses to closed questions related to Ingress were collected from 45 adult players aged 20–60 from Greece and were subsequently analyzed by means of pre- and post-quantitative measures of the four ARCS factors. The results show that: (a) game training improves all the factors of ARCS, primarily attention and satisfaction; (b) the responses of young people (20–35) agree more with those of elderly adults (&gt;52) than with those of the intermediate age group of 36–51. Our findings, therefore, highlight the potential and the applicability of the ARCS model in MAR games.