Reviewerly/exHarmony huggingface dataset:
Robust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds

Detection of non-overlapping ellipses from 2-dimensional (2D) edge points is an essential step towards solving typical photogrammetry problems pertaining to feature detection, calibration, and registration of optical instruments. For instance, circular and spherical black and white calibration and registration targets are represented as ellipses in images. Furthermore, the intersection of a cut plane with cylindrical point clouds generates 2D points following elliptic patterns. To this end, this study proposes a collection of new methods for the automatic and robust detection of non-overlapping ellipses from 2D points. These methods will first be applied to detect circular and spherical targets in images and, second, to detect cylinders in 3D point clouds. The method utilizes the Euclidian ellipticity and a new systematic and generalizable threshold to decide if a set of connected points follow an elliptic pattern. When connected points include outliers, the newly proposed robust Monte Carlo-based ellipse fitting method will be deployed. This method includes three new developments: (i) selecting initial subsamples using a bucketing strategy based on the polar angle of the points; (ii) detecting inlier points by reducing the robust ellipse fitting to a robust circle fitting problem; and (iii) choosing the best inlier set amongst all subsamples using adaptive, systematic, and generalizable selection criteria. A new process is presented to extract cylinders from a point cloud by detecting non-overlapping ellipses from the points projected onto an intersecting cut plane. The proposed methods were compared to established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments. The experiments include (i) comparisons of robust ellipse fitting; (ii) sensitivity analysis of the ellipse validation criteria; (iii) comparison of non-overlapping ellipse detection; and (iv) detection of pipes from terrestrial laser scanner point clouds. It was found that the proposed robust ellipse detection was superior to four reliable robust methods, including the popular least median of squares, in both simulated and real-world datasets. The proposed process for detecting non-overlapping ellipses achieved F-measure of 99.3% on real images, compared to 42.4%, 65.6%, and 59.2%, obtained using the methods of Fornaciari, Patraucean, and Panagiotakis, respectively. The proposed cylinder extraction method identified all detectable mechanical pipes in two real-world point clouds collected in laboratory and industrial construction site conditions. The results of this investigation show promise for the application of the proposed methods for automatic extraction of circular targets from images and pipes from point clouds.
Nonlinear Squeezing for Measurement-Based Non-Gaussian Operations in Time Domain

Quantum non-Gaussian gate is a missing piece to the realization of continuous-variable universal quantum operations in an optical system. In a measurement-based implementation of the cubic phase gate, a lowest-order non-Gaussian gate, non-Gaussian ancillary states that have a property we call nonlinear squeezing are required. This property, however, has never been experimentally verified. In this paper, we generate a superposition between a vacuum state and a single-photon state whose nonlinear squeezing is maximized by the optimization of the superposition coefficients. The nonlinear squeezing is observed via real-time quadrature measurements, meaning that the generated states are compatible with real-time feedforward and are suitable as ancillary states for the cubic phase gate in the time domain. Moreover, by increasing the number of photons, it is expected that nonlinear squeezing can be further improved. The idea presented here can be readily extended to higher-order phase gates [P. Marek et al., Phys. Rev. A 97, 022329 (2018)]. As such, this work presents an important step to extend continuous-variable quantum information processing from Gaussian regime to non-Gaussian regime.
Annotating the Pandemic: Named Entity Recognition and Normalisation in COVID-19 Literature

The COVID-19 pandemic has been accompanied by such an explosive increase in media coverage and scientific publications that researchers find it difficult to keep up.We are presenting a publicly available pipeline to perform named entity recognition and normalisation in parallel to help find relevant publications and to aid in downstream NLP tasks such as text summarisation.In our approach, we are using a dictionary-based system for its high recall in conjunction with two models based on BioBERT for their accuracy.Their outputs are combined according to different strategies depending on the entity type.In addition, we are using a manually crafted dictionary to increase performance for new concepts related to COVID-19.We have previously evaluated our work on the CRAFT corpus, and make the output of our pipeline available on two visualisation platforms.
A Novel Chaotic Block Image Encryption Algorithm Based on Deep Convolutional Generative Adversarial Networks

This paper proposes a novel chaotic block image encryption algorithm based on deep convolutional generative adversarial networks (DCGANs), quaternions, an improved Feistel network, and an overall scrambling and diffusion mechanism. First, a new hyperchaotic system is introduced and combined with DCGANs to generate a random sequence with better randomness and complexity as a key stream. This sequence is then combined with a quaternion and an improved Feistel network encryption of a colour plaintext image by utilizing the key block matrix to ultimately achieve overall scrambling and diffusion of the cipher image. Finally, the security of this algorithm is quantitatively and qualitatively analysed. The simulation results show that the proposed hyperchaotic system has a large key space and good random characteristics and that the new algorithm yields adequate security and can resist brute-force attacks and chosen-plaintext attacks. Therefore, this approach provides a new way to achieve secure transmission and protection of image information.
Fast Searching Density Peak Clustering Algorithm Based on Shared Nearest Neighbor and Adaptive Clustering Center

The clustering analysis algorithm is used to reveal the internal relationships among the data without prior knowledge and to further gather some data with common attributes into a group. In order to solve the problem that the existing algorithms always need prior knowledge, we proposed a fast searching density peak clustering algorithm based on the shared nearest neighbor and adaptive clustering center (DPC-SNNACC) algorithm. It can automatically ascertain the number of knee points in the decision graph according to the characteristics of different datasets, and further determine the number of clustering centers without human intervention. First, an improved calculation method of local density based on the symmetric distance matrix was proposed. Then, the position of knee point was obtained by calculating the change in the difference between decision values. Finally, the experimental and comparative evaluation of several datasets from diverse domains established the viability of the DPC-SNNACC algorithm.
A systematic review on sequence-to-sequence learning with neural network and its models

We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
Diagnosing 12 prostate needle cores within an hour of biopsy via open-top light-sheet microscopy

Processing and diagnosing a set of 12 prostate biopsies using conventional histology methods typically take at least one day. A rapid and accurate process performed while the patient is still on-site could significantly improve the patient's quality of life.We develop and assess the feasibility of a one-hour-to-diagnosis (1Hr2Dx) method for processing and providing a preliminary diagnosis of a set of 12 prostate biopsies.We developed a fluorescence staining, optical clearing, and 3D open-top light-sheet microscopy workflow to enable 12 prostate needle core biopsies to be processed and diagnosed within an hour of receipt. We analyzed 44 biopsies by the 1Hr2Dx method, which does not consume tissue. The biopsies were then processed for routine, slide-based 2D histology. Three pathologists independently evaluated the 3D 1Hr2Dx and 2D slide-based datasets in a blinded, randomized fashion. Turnaround times were recorded, and the accuracy of our method was compared with gold-standard slide-based histology.The average turnaround time for tissue processing, imaging, and diagnosis was 44.5 min. The sensitivity and specificity of 1Hr2Dx in diagnosing cancer were both >90 % .The 1Hr2Dx method has the potential to improve patient care by providing an accurate preliminary diagnosis within an hour of biopsy.
Alignment-Free Offline Calibration of Commercial Optical See-Through Head-Mounted Displays With Simplified Procedures

Despite the growing availability of self-contained augmented reality head-mounted displays (AR HMDs) based on optical see-through (OST) technology, their potential applications across highly challenging medical and industrial settings are still hampered by the complexity of the display calibration required to ensure the locational coherence between the real and virtual elements.The calibration of commercial OST displays remains an open challenge due to the inaccessibility of the user's perspective and the limited hardware information available to the end-user.State-of-the-art calibrations usually comprise both offline and online stages.The offline calibration at a generic viewpoint provides a starting point for the subsequent refinements and it is crucial.Current offline calibration methods either heavily rely on the useralignment or require complicated hardware calibrations, making the overall procedure subjective and/or tedious.To address this problem, in this work we propose two fully alignment-free calibration methods with less complicated hardware calibration procedures compared with state-of-the-art solutions.The first method employs an eye-replacement camera to compute the rendering camera's projection matrix based on photogrammetry techniques.The second method controls the rendered object position in a tracked 3D space to compensate for the parallax-related misalignment for a generic viewpoint.Both methods have been tested on Microsoft HoloLens 1. Quantitative results show that the average overlay misalignment is fewer than 4 pixels (around 1.5 mm or 9 arcmin) when the target stays within arm's reach.The achieved misalignment is much lower than the HoloLens default interpupillary distance (IPD)-based correction, and equivalent but with lower variance than the Single Point Active Alignment Method (SPAAM)-based calibration.The two proposed methods offer strengths in complementary aspects and can be chosen according to the user's needs.We also provide several update schemes for the two methods that can be integrated for an on-line viewpoint-dependent refinement of the calibration parameters.Both methods have been integrated into a Unity3D-based framework and can be directly applied to Unity-assisted devices.
Unsupervised Anomaly Detection Using Style Distillation

Autoencoders (AEs) have been widely used for unsupervised anomaly detection. They learn from normal samples such that they produce high reconstruction errors for anomalous samples. However, AEs can exhibit the over-detection issue because they imperfectly reconstruct not only anomalous samples but also normal ones. To address this issue, we introduce an outlier-exposed style distillation network (OE-SDN) that mimics the mild distortions caused by an AE, which are termed as style translation. We use the difference between the outputs of the OE-SDN and AE as an alternative anomaly score. Experiments on anomaly classification and segmentation tasks show that the performance of our method is superior to existing methods.
Evolutionary model discovery of causal factors behind the socio-agricultural behavior of the Ancestral Pueblo

Agent-based modeling of artificial societies allows for the validation and analysis of human-interpretable, causal explanations of human behavior that generate society-scale phenomena. However, parameter calibration is insufficient to conduct data-driven explorations that are adequate in evaluating the importance of causal factors that constitute agent rules that match real-world individual-scale generative behaviors. We introduce evolutionary model discovery, a framework that combines genetic programming and random forest regression to evaluate the importance of a set of causal factors hypothesized to affect the individual’s decision-making process. With evolutionary model discovery, we investigated the farm plot seeking behavior of the Ancestral Pueblo of the Long House Valley simulated in the Artificial Anasazi model. We evaluated the importance of causal factors unconsidered in the original model, which we hypothesized to have affected the decision-making process. Our findings, concur with other archaeological studies on the Ancestral Pueblo communities during the Pueblo II period, which indicate the existence of cross-village polities, hierarchical organization, and dependence on the viability of the agricultural niche. Contrary to the original Artificial Anasazi model, where closeness was the sole factor driving farm plot selection, selection of higher quality land, distancing from failed farm plots, and desire for social presence are found to be more important. Finally, models updated with farm selection strategies designed by incorporating these insights showed significant improvements in accuracy and robustness over the original Artificial Anasazi model.
Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -Nearest Neighbors ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>K</mi> </math> -NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </math> , and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively.
Attention-Based Neural Networks for Chroma Intra Prediction in Video Coding

Neural networks can be successfully used to improve several modules of advanced video coding schemes. In particular, compression of colour components was shown to greatly benefit from usage of machine learning models, thanks to the design of appropriate attention-based architectures that allow the prediction to exploit specific samples in the reference region. However, such architectures tend to be complex and computationally intense, and may be difficult to deploy in a practical video coding pipeline. This work focuses on reducing the complexity of such methodologies, to design a set of simplified and cost-effective attention-based architectures for chroma intra-prediction. A novel size-agnostic multi-model approach is proposed to reduce the complexity of the inference process. The resulting simplified architecture is still capable of outperforming state-of-the-art methods. Moreover, a collection of simplifications is presented in this paper, to further reduce the complexity overhead of the proposed prediction architecture. Thanks to these simplifications, a reduction in the number of parameters of around 90% is achieved with respect to the original attention-based methodologies. Simplifications include a framework for reducing the overhead of the convolutional operations, a simplified cross-component processing model integrated into the original architecture, and a methodology to perform integer-precision approximations with the aim to obtain fast and hardware-aware implementations. The proposed schemes are integrated into the Versatile Video Coding (VVC) prediction pipeline, retaining compression efficiency of state-of-the-art chroma intra-prediction methods based on neural networks, while offering different directions for significantly reducing coding complexity.
Connecting Robust Shuffle Privacy and Pan-Privacy

In the shuffle model of differential privacy, data-holding users send randomized messages to a secure shuffler, the shuffler permutes the messages, and the resulting collection of messages must be differentially private with regard to user data. In the pan-private model, an algorithm processes a stream of data while maintaining an internal state that is differentially private with regard to the stream data. We give evidence connecting these two apparently different models.Our results focus on robustly shuffle private protocols, whose privacy guarantees are not greatly affected by malicious users. First, we give robustly shuffle private protocols and upper bounds for counting distinct elements and uniformity testing. Second, we use pan-private lower bounds to prove robustly shuffle private lower bounds for both problems. Focusing on the dependence on the domain size k, we find that robust shuffle privacy and pan-privacy have additive error for counting distinct elements. For uniformity testing, we give a robustly shuffle private protocol with sample complexity Õ(k2/3) and show that an Ω(k2/3) dependence is necessary in a specific parameter regime. Finally, we show that this connection is useful in both directions: we give a pan-private adaptation of recent work on shuffle private histograms and use it to recover further separations between pan-privacy and interactive local privacy.
Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction

Dependency trees have been shown to be effective in capturing long-range relations between target entities.Nevertheless, how to selectively emphasize target-relevant information and remove irrelevant content from the tree is still an open problem.Existing approaches employing predefined rules to eliminate noise may not always yield optimal results due to the complexity and variability of natural language.In this paper, we present a novel architecture named Dynamically Pruned Graph Convolutional Network (DP-GCN), which learns to prune the dependency tree with rethinking in an end-to-end scheme.In each layer of DP-GCN, we employ a selection module to concentrate on nodes expressing the target relation by a set of binary gates and then augment the pruned tree with a pruned semantic graph to ensure the connectivity.After that, we introduce a rethinking mechanism to guide and refine the pruning operation by feeding back the high-level learned features repeatedly.Extensive experimental results demonstrate that our model achieves impressive performance compared to strong competitors.
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification

Relation classification aims to extract semantic relations between entity pairs from the sentences.However, most existing methods can only identify seen relation classes that occurred during training.To recognize unseen relations at test time, we explore the problem of zero-shot relation classification.Previous work regards the problem as reading comprehension or textual entailment, which have to rely on artificial descriptive information to improve the understandability of relation types.Thus, rich semantic knowledge of the relation labels is ignored.In this paper, we propose a novel logic-guided semantic representation learning model for zero-shot relation classification.Our approach builds connections between seen and unseen relations via implicit and explicit semantic representations with knowledge graph embeddings and logic rules.Extensive experimental results demonstrate that our method can generalize to unseen relation types and achieve promising improvements.
Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning

Scientific keyphrase identification and classification is the task of detecting and classifying keyphrases from scholarly text with their types from a set of predefined classes.This task has a wide range of benefits, but it is still challenging in performance due to the lack of large amounts of labeled data required for training deep neural models.In order to overcome this challenge, we explore pre-trained language models BERT and SciBERT with intermediate task transfer learning, using 42 data-rich related intermediate-target task combinations.We reveal that intermediate task transfer learning on SciBERT induces a better starting point for target task fine-tuning compared with BERT and achieves competitive performance in scientific keyphrase identification and classification compared to both previous works and strong baselines.Interestingly, we observe that BERT with intermediate task transfer learning fails to improve the performance of scientific keyphrase identification and classification potentially due to significant catastrophic forgetting.This result highlights that scientific knowledge achieved during the pre-training of language models on large scientific collections plays an important role in the target tasks.We also observe that sequence tagging related intermediate tasks, especially syntactic structure learning tasks such as POS Tagging, tend to work best for scientific keyphrase identification and classification.