Reviewerly/exHarmony huggingface dataset:
Patch-based 3D U-Net and transfer learning for longitudinal piglet brain segmentation on MRI

In order to study neural plasticity in immature brain following early brain lesion, large animal model are needed. Because of its morphological similarities with the human developmental brain, piglet is a suitable but little used one. Its study from Magnetic Resonance Imaging (MRI) requires the development of automatic algorithms for the segmentation of the different structures and tissues. A crucial preliminary step consists in automatically segmenting the brain.We propose a fully automatic brain segmentation method applied to piglets by combining a 3D patch-based U-Net and a post-processing pipeline for spatial regularization and elimination of false positives. Our approach also integrates a transfer-learning strategy for managing an automated longitudinal monitoring evaluated for four developmental stages (2, 6, 10 and 18 weeks), facing the issue of MRI changes resulting from the rapid brain development. It is compared to a 2D approach and the Brain Extraction Tool (BET) as well as techniques adapted to other animals (rodents, macaques). The influence of training patches size and distribution is studied as well as the benefits of spatial regularization.Results show that our approach is efficient in terms of average Dice score (0.952) and Hausdorff distance (8.51), outperforming the use of a 2D U-Net (Dice: 0.919, Hausdorff distance: 11.06) and BET (Dice: 0.764, Hausdorff distance: 25.91). The transfer-learning strategy achieves a good performance on older piglets (Dice of 0.934 at 6 weeks, 0.956 at 10 weeks and 0.958 at 18 weeks) compared to a standard training strategy with few data (Dice of 0.636 at 6 weeks, 0.907 at 10 weeks, not calculable at 18 weeks because of too few training piglets).In conclusion, we provide a method for longitudinal MRI piglet brain segmentation based on 3D U-Net and transfer learning which can be used for future morphometric studies and applied to other animals.
A Data Augmentation Method for Skeleton-Based Action Recognition with Relative Features

In recent years, skeleton-based human action recognition (HAR) approaches using convolutional neural network (CNN) models have made tremendous progress in computer vision applications. However, using relative features to depict human actions, in addition to preventing overfitting when the CNN model is trained on a few samples, is still a challenge. In this paper, a new motion image is introduced to transform spatial-temporal motion information into image-based representations. For each skeleton sequence, three relative features are extracted to describe human actions. The three relative features are consisted of relative coordinates, immediate displacement, and immediate motion orientation. In particular, the relative coordinates introduced in our paper not only depict the spatial relations of human skeleton joints but also provide long-term temporal information. To address the problem of small sample sizes, a data augmentation strategy consisting of three simple but effective data augmentation methods is proposed to expand the training samples. Because the generated color images are small in size, a shallow CNN model is suitable to extract the deep features of the generated motion images. Two small-scale but challenging skeleton datasets were used to evaluate the method, scoring 96.59% and 97.48% on the Florence 3D Actions dataset and UTkinect-Action 3D dataset, respectively. The results show that the proposed method achieved a competitive performance compared with the state-of-the-art methods. Furthermore, the augmentation strategy proposed in this paper effectively solves the overfitting problem and can be widely adopted in skeleton-based action recognition.
De-Noising of Magnetotelluric Signals by Discrete Wavelet Transform and SVD Decomposition

Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise in MT data based on discrete wavelet transform and singular value decomposition (SVD), with multiscale dispersion entropy and phase space reconstruction carried out for pretreatment. No “over processing” takes place in the proposed method. Compared with wavelet transform and SVD decomposition in synthetic tests, the proposed method removes the profile of noise more completely, including large-scale noise and impulse noise. For high levels or low levels of noise, the proposed method can increase the signal-to-noise ratio of data more obviously. Moreover, application to the field MT data can prove the performance of the proposed method. The proposed method is a feasible method for the elimination of various noise types and can improve MT data with high noise levels, obtaining a recovery in the response. It can improve abrupt points and distortion in MT response curves more effectively than the robust method can.
Experimental classical entanglement in a 16 acoustic qubit-analogue

Abstract The possibility of achieving and controlling scalable classically entangled, i.e., inseparable, multipartite states, would fundamentally challenge the advantages of quantum systems in harnessing the power of complexity in information science. Here, we investigate experimentally the extent of classical entanglement in a $$16$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>16</mml:mn> </mml:mrow> </mml:math> acoustic qubit-analogue platform. The acoustic qubit-analogue, a.k.a., logical phi-bit, results from the spectral partitioning of the nonlinear acoustic field of externally driven coupled waveguides. Each logical phi-bit is a two-level subsystem characterized by two independently measurable phases. The phi-bits are co-located within the same physical space enabling distance independent interactions. We chose a vector state representation of the $$16$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>16</mml:mn> </mml:mrow> </mml:math> -phi-bit system which lies in a $${2}^{16}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mn>16</mml:mn> </mml:msup> </mml:math> -dimensional Hilbert space. The calculation of the entropy of entanglement demonstrates the possibility of achieving inseparability of the vector state and of navigating the corresponding Hilbert space. This work suggests a new direction in harnessing the complexity of classical inseparability in information science.
Privacy-preserving breast cancer recurrence prediction based on homomorphic encryption and secure two party computation

Protecting patients’ privacy is one of the most important tasks when developing medical artificial intelligence models since medical data is the most sensitive personal data. To overcome this privacy protection issue, diverse privacy-preserving methods have been proposed. We proposed a novel method for privacy-preserving Gated Recurrent Unit (GRU) inference model using privacy enhancing technologies including homomorphic encryption and secure two party computation. The proposed privacy-preserving GRU inference model validated on breast cancer recurrence prediction with 13,117 patients’ medical data. Our method gives reliable prediction result (0.893 accuracy) compared to the normal GRU model (0.895 accuracy). Unlike other previous works, the experiment on real breast cancer data yields almost identical results for privacy-preserving and conventional cases. We also implement our algorithm to shows the realistic end-to-end encrypted breast cancer recurrence prediction.
Personal identification with artificial intelligence under COVID-19 crisis: a scoping review

Artificial intelligence is useful for building objective and rapid personal identification systems. It is important to research and develop personal identification methods as social and institutional infrastructure. A critical consideration during the coronavirus disease 2019 pandemic is that there is no contact between the subjects and personal identification systems. The aim of this study was to organize the recent 5-year development of contactless personal identification methods that use artificial intelligence.This study used a scoping review approach to map the progression of contactless personal identification systems using artificial intelligence over the past 5 years. An electronic systematic literature search was conducted using the PubMed, Web of Science, Cochrane Library, CINAHL, and IEEE Xplore databases. Studies published between January 2016 and December 2020 were included in the study.By performing an electronic literature search, 83 articles were extracted. Based on the PRISMA flow diagram, 8 eligible articles were included in this study. These eligible articles were divided based on the analysis targets as follows: (1) face and/or body, (2) eye, and (3) forearm and/or hand. Artificial intelligence, including convolutional neural networks, contributed to the progress of research on contactless personal identification methods.This study clarified that contactless personal identification methods using artificial intelligence have progressed and that they have used information obtained from the face and/or body, eyes, and forearm and/or hand.
The Carceral Automaton: Digital Prisons and Technologies of Detention

Prisons are on the cusp of a technological transformation as twenty-first-century digital connectivity in ‘free’ society permeates prison design and offender management. This article will begin with an overview of the digital technologies in ‘smart’ prisons. Two limbs are emerging: technologies that are embedded into the infrastructure of prisons to benefit authorities through heightened security, and technologies that may benefit prisoners by providing them with positive opportunities to access justice, maintain family relationships and engage in programs aimed at optimising their post-release circumstances and rehabilitation. However, recent case law paints a picture of prison life devoid of human contact during the COVID-19 pandemic, bringing isolation and heightened anxiety. Through the lens of emergent conceptions of digital criminology, this article will analyse Australian case law to examine whether the automated, smart or digital prison offers a utopian vision of safe detention and rehabilitation or a dehumanised and punitive dystopia.
Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model

We employed linear and nonlinear error correction models (ECMs) to predict the log returns of Bitcoin (BTC). The linear ECM is the best model for predicting BTC compared to the neural network and autoregressive models in terms of RMSE, MAE, and MAPE. Using a linear ECM, we are able to understand how BTC is affected by other coins. In addition, we performed Granger-causality tests on fourteen cryptocurrencies.
Advances and Prospects of Vision-Based 3D Shape Measurement Methods

Vision-based three-dimensional (3D) shape measurement techniques have been widely applied over the past decades in numerous applications due to their characteristics of high precision, high efficiency and non-contact. Recently, great advances in computing devices and artificial intelligence have facilitated the development of vision-based measurement technology. This paper mainly focuses on state-of-the-art vision-based methods that can perform 3D shape measurement with high precision and high resolution. Specifically, the basic principles and typical techniques of triangulation-based measurement methods as well as their advantages and limitations are elaborated, and the learning-based techniques used for 3D vision measurement are enumerated. Finally, the advances of, and the prospects for, further improvement of vision-based 3D shape measurement techniques are proposed.
Developing an IoT Identity Management System Using Blockchain

Identity (ID) management systems have evolved based on traditional data modelling and authentication protocols that are facing security, privacy, and trust challenges with the growth of Internet of Things (IoT). Research surveys reveal that blockchain technology offers special features of self-sovereign identity and cryptography that can be leveraged to address the issues of security breach and privacy leaks prevalent in existing ID management systems. Although research studies are recently exploring the suitability of blockchain based support to existing infrastructure, there is a lack of focus on IoT ecosystem in the secured ID management with data provenance of digital assets in businesses. In this paper, we propose a blockchain based ID management system for computing assets in an IoT ecosystem comprising of devices, software, users, and data operations. We design and develop a proof-of-concept prototype using a federated and distributed blockchain platform with smart contracts to support highly trusted data storage and secure authentication of IoT resources and operations within a business case scenario.
A Collaborative Despeckling Method for SAR Images Based on Texture Classification

Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based methods, and non-local filtering methods. However, SAR images usually contain many different types of regions, including homogeneous and heterogeneous regions. Some filters could despeckle effectively in homogeneous regions but could not preserve structures in heterogeneous regions. Some filters preserve structures well but do not suppress speckle effectively. Following this theory, we design a combination of two state-of-the-art despeckling tools that can overcome their respective shortcomings. In order to select the best filter output for each area in the image, the clustering and Gray Level Co-Occurrence Matrices (GLCM) are used for image classification and weighting, respectively. Clustering and GLCM use the co-registered optical images of SAR images because their structure information is consistent, and the optical images are much cleaner than SAR images. The experimental results on synthetic and real-world SAR images show that our proposed method can provide a better objective performance index under a strong noise level. Subjective visual inspection demonstrates that the proposed method has great potential in preserving structural details and suppressing speckle noise.
YOLOv3-SORT: detection and tracking player/ball in soccer sport

Soccer player and ball detection and tracking have emerged as an area of intense interest among many analysts and researchers. This is because it aids coaches in team performance evaluation and decision-making to achieve optimal results. However, existing methodologies have failed to effectively detect and track the ball when it moves at high velocity and also to track players under occlusion conditions. You only look once (YOLOv3) and simple online real-time (SORT)-based soccer ball and player tracking approach is proposed, for accurately classifying the detected objects in soccer video and track them in various challenging situations. The proposed methodology consists of two parts: (i) YOLOv3 can detect and classify the objects (i.e., player, soccer ball, and background) and eliminate the detected objects outside the playfield as background; (ii) tracking is achieved using SORT algorithm which employs a Kalman filtering and bounding box overlap. The proposed model achieves tracking accuracy of 93.7% on multiple object tracking accuracy metrics with a detection speed of 23.7 frames per second (FPS) and a tracking speed of 11.3 FPS.
Intractability of Electronic Structure in a Fixed Basis

Finding the ground-state energy of electrons subject to an external electric field is a fundamental problem in computational chemistry. While the theory of QMA-completeness has been instrumental in understanding the complexity of finding ground states in many-body quantum systems, prior to this work it has been unknown whether or not the special form of the Hamiltonian for the electronic structure of molecules can be exploited to find ground states efficiently or whether the problem remains hard for this special case. We prove that the electronic-structure problem, when restricted to a fixed single-particle basis and a fixed number of electrons, is QMA-complete. In our proof, the local Hamiltonian is encoded in the choice of spatial orbitals used to discretize the electronic-structure Hamiltonian. In contrast, Schuch and Verstraete have proved hardness for the electronic-structure problem with an additional site-specific external magnetic field, but without the restriction to a fixed basis, by encoding a local Hamiltonian on qubits in the site-specific magnetic field. We also show that estimation of the energy of the lowest-energy Slater-determinant state (i.e., the Hartree-Fock state) is nondeterministic polynomial time (NP)-complete for the electronic-structure Hamiltonian in a fixed basis.Received 5 October 2021Revised 18 January 2022Accepted 14 March 2022DOI:https://doi.org/10.1103/PRXQuantum.3.020322Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasElectronic structureTechniquesComputational complexityQuantum Information
Predictors of the Sense of Embodiment of a Female Victim of Sexual Harassment in a Male Sample Through 360-Degree Video-Based Virtual Reality

The sense of embodiment refers to the set of sensations related to having (i.e., ownership), being located in (i.e., location), and controlling (i.e., agency) a virtual body. Recently, 360-degree video-based Virtual Reality (VR) has been used to manipulate the sense of embodiment, generating the body-swap illusion, that is, the illusionary switch from the real body to a virtual one. However, the psychological mechanisms involved in this illusion are still unknown. The present study is a secondary analysis of the study by Ventura et al. (2021) investigating the feasibility of 360-degree video to induce the body swap from a male’s real body to a female virtual body in a sexual harassment virtual environment. In addition, the study explores whether the sense of presence and psychological trait variables related to sexual harassment (i.e., machismo, chivalry, alexithymia, empathic abilities) predict the illusion of owning the body of a female victim of sexual harassment. Forty-four men participated in the study, and the results indicate that the 360-degree video is able to induce the body-swap illusion for location and ownership, but not for agency. Multiple regression analyses showed that the sense of presence was a predictor of the three dimensions of embodiment, but specific psychological traits (i.e., low scores on machismo, high scores on difficulties expressing feelings, and high scores on perspective taking) were also predictor variables of experiencing a greater sense of location and agency in the female virtual body. This study shows that both technological issues and participants’ psychological traits are involved in the experience of the body-swap illusion in a sexual harassment scenario using 360-degree video-based VR.
A survey on visual transfer learning using knowledge graphs

The information perceived via visual observations of real-world phenomena is unstructured and complex. Computer vision (CV) is the field of research that attempts to make use of that information. Recent approaches of CV utilize deep learning (DL) methods as they perform quite well if training and testing domains follow the same underlying data distribution. However, it has been shown that minor variations in the images that occur when these methods are used in the real world can lead to unpredictable and catastrophic errors. Transfer learning is the area of machine learning that tries to prevent these errors. Especially, approaches that augment image data using auxiliary knowledge encoded in language embeddings or knowledge graphs (KGs) have achieved promising results in recent years. This survey focuses on visual transfer learning approaches using KGs, as we believe that KGs are well suited to store and represent any kind of auxiliary knowledge. KGs can represent auxiliary knowledge either in an underlying graph-structured schema or in a vector-based knowledge graph embedding. Intending to enable the reader to solve visual transfer learning problems with the help of specific KG-DL configurations we start with a description of relevant modeling structures of a KG of various expressions, such as directed labeled graphs, hypergraphs, and hyper-relational graphs. We explain the notion of feature extractor, while specifically referring to visual and semantic features. We provide a broad overview of knowledge graph embedding methods and describe several joint training objectives suitable to combine them with high dimensional visual embeddings. The main section introduces four different categories on how a KG can be combined with a DL pipeline: 1) Knowledge Graph as a Reviewer; 2) Knowledge Graph as a Trainee; 3) Knowledge Graph as a Trainer; and 4) Knowledge Graph as a Peer. To help researchers find meaningful evaluation benchmarks, we provide an overview of generic KGs and a set of image processing datasets and benchmarks that include various types of auxiliary knowledge. Last, we summarize related surveys and give an outlook about challenges and open issues for future research.
Unsupervised Learning

Most of this book concerns supervised learning methods such as regression and classification. In the supervised learning setting, we typically have access to a set of p features $$ X_{1}, X_{2}, \ldots, X_{p},$$ measured on n observations, and a response Y also measured on those same n observations. The goal is then to predict Y using $$ X_{1}, X_{2}, \ldots, X_{p}.$$
A novel artificial bee colony optimiser with dynamic population size for multi-level threshold image segmentation

Existing swarm intelligence (SI) models are usually derived from fixed-population biological system. However, this approach inevitably causes unnecessary computational cost. In addition, the population size of these models is usually hard to be pr-determined appropriately. In this contribution, this paper exploits a general varying-population swarm model (VPSM) with life-cycle foraging rules based on the population growth dynamic principle. This model essentially improves individual-level adaptability and population-level emergence to self-adapt towards an optimal population size. Then, a novel VPSM-based artificial bee colony optimiser is instantiated with orthogonal Latin squares approach and crossover-based social learning strategies. A comprehensive experimental analysis is implemented in which the proposed algorithm is benchmarked against classical bio-mimetic algorithms on CEC2014 test suites. Then, this algorithm is applied for multi-level image segmentation. Computation results show the performance superiority of the proposed algorithm.
A projected dynamic system associated with a cybersecurity investment model with budget constraints and fixed demands

In this paper, we present a cybersecurity investment supply chain model with nonlinear budget constraints and fixed demands, which gives a generalized Nash equilibrium model.We define the related variational equilibrium and study the associated projected dynamic system (PDS), establishing the relationship between the solutions to the variational inequality and the stationary points of the PDS.Then, we propose a computational procedure to find such stationary points and, finally, some numerical examples illustrate the obtained results.