Reviewerly/exHarmony huggingface dataset:
First break picking with deep learning – evaluation of network architectures

ABSTRACT In recent years, various convolutional neural network architectures have been proposed for first break picking. In this paper, we compare the standard auto‐encoder and U‐net architectures as well as versions enhanced with ResNet style skip connections. The U‐net appears to have become the standard network for segmentation, judging from the number of published articles. Still, there is some variety in neural network architectural choices. In this paper, we assess the impact of neural network depth, width and input data size, as well as some small modifications for deep networks offered by the ResNet. In general, results improve as the networks get deeper, but with diminishing returns. The more complex the data, the more benefit the deeper networks bring. We use complete shot gathers, albeit rescaled for efficiency, to train the neural networks. For shot gathers with a simple piecewise linear moveout, this approach yields results with good accuracy when gathers are resampled to 128 × 128 samples. For shot gathers with more complex first break moveout, using our approach it is advised to stay close to the original dimension of each gather for best accuracy, at the expense of increased training times. A good trade‐off between network depth, image size and training times is to use a nine‐stage U‐net with 256 sample images. Despite the advantages in other applications, the basic U‐net outperforms a U‐net with ResNet features. We show that changing the input data dimensions for trained networks does not work, despite the fact the fully convolutional networks are independent of image size. The U‐net based first break picking is not sensitive to picking errors, as in many cases the neural network predictions are better than the training data where the training data have random mispicks. This suggests a practical application; namely, to train or re‐train a pre‐trained network on a single data set after conventional first break picking with the objective of improving conventionally picked first breaks.
Audit Data Analysis and Application Based on Correlation Analysis Algorithm

Traditional audit data analysis algorithms have many shortcomings, such as the lack of means to mine the hidden audit clues behind the data, the difficulty of finding increasingly hidden cheating techniques caused by the electronic and networked environment, and the inability to solve the quality defects of the audited data. Correlation analysis algorithm in data mining technology is an effective means to obtain knowledge from massive data, which can complete, muffle, clean, and reduce defective data and then can analyze massive data and obtain audit trails under the guidance of expert experience or analysts. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounds the research status and significance of audit data analysis and application; elaborates the development background, current status, and future challenges of correlation analysis algorithm; introduces the methods and principles of data model and its conversion and audit model construction; conducts audit data collection and cleaning; implements audit data preprocessing and its algorithm description; performs audit data analysis based on correlation analysis algorithm; analyzes the hidden node activation value and audit rule extraction in correlation analysis algorithm; proposes the application of audit data based on correlation analysis algorithm; discusses the relationship between audit data quality and audit risk; and finally compares different data mining algorithms in audit data analysis. The findings demonstrate that by analyzing association rules, the correlation analysis algorithm can determine the significance of a huge quantity of audit data and characterise the degree to which linked events would occur concurrently or sequentially in a probabilistic manner. The correlation analysis algorithm first inputs the collected audit data through preprocessing module to filter out useless data and then organizes the obtained data into a format that can be recognized by data mining algorithm and executes the correlation analysis algorithm on the sorted data; finally, the obtained hidden data is divided into normal data and suspicious data by comparing it with the pattern in the rule base. The algorithm can conduct in-depth analysis and research on the company's accounting vouchers, account books, and a large number of financial accounting data and other data of various natures in the company's accounting vouchers; reveal its original characteristics and internal connections; and turn it into an audit. People need more direct and useful information. The study results of this paper provide a reference for further researches on audit data analysis and application based on correlation analysis algorithm.
An Efficient Algorithm for Sparse Quantum State Preparation

Generating quantum circuits that prepare specific states is an essential part of quantum compilation. Algorithms that solve this problem for general states generate circuits at grow exponentially in the number of qubits. However, in contrast to general states, many practically relevant states are sparse in the standard basis. In this paper we show how sparsity can be used for efficient state preparation. We present a polynomial-time algorithm that generates polynomial-size quantum circuits (linear in the number of nonzero coefficients times number of qubits) that prepare given states, making computer-aided design of sparse state preparation scalable.
Cross-Domain Graph Convolutions for Adversarial Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) has attracted increasing attention in recent years, which adapts classifiers to an unlabeled target domain by exploiting a labeled source domain. To reduce the discrepancy between source and target domains, adversarial learning methods are typically selected to seek domain-invariant representations by confusing the domain discriminator. However, classifiers may not be well adapted to such a domain-invariant representation space, as the sample- and class-level data structures could be distorted during adversarial learning. In this article, we propose a novel transferable feature learning approach on graphs (TFLG) for unsupervised adversarial domain adaptation (DA), which jointly incorporates sample- and class-level structure information across two domains. TFLG first constructs graphs for minibatch samples and identifies the classwise correspondence across domains. A novel cross-domain graph convolutional operation is designed to jointly align the sample- and class-level structures in two domains. Moreover, a memory bank is designed to further exploit the class-level information. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach compared to the state-of-the-art UDA methods.
Design And Development of Human Anatomy Learning Platform for Medical Students Based On Augmented Intelligence Technology

Augmented Intelligence technology was introduced for the task of helping improve human work in various fields, one of which is education. Several problems in the learning process, which are currently completely virtual, raise new problems, especially related to practicums which require teaching modules as guidelines for practicum implementation but still maintain the impression of interactive learning. For this reason, Augmented Reality technology is applied as a solution to build a practical human anatomy module, then called AIVE Platform embedded in smartphones to provide informative and immersive learning that can be run indoors or outdoors so that it is not limited by space and time. This platform can run on Android and iOS which is built on the AR Foundation framework to work across platforms. This module has followed the rules of the anatomical atlas that include labels on each part, there is also a login system to store student usage history, as well as the choice of learning mode. This module has been licensed to operate from a teaching doctor in anatomy to be used as a teaching module. The PIECES framework used to analyze the importance and satisfaction level of the platform gives score 4.085 out of 5 on and 4.081 out of 5 respectively.
DP-MVS: Detail Preserving Multi-View Surface Reconstruction of Large-Scale Scenes

This paper presents an accurate and robust dense 3D reconstruction system for detail preserving surface modeling of large-scale scenes from multi-view images, which we named DP-MVS. Our system performs high-quality large-scale dense reconstruction, which preserves geometric details for thin structures, especially for linear objects. Our framework begins with a sparse reconstruction carried out by an incremental Structure-from-Motion. Based on the reconstructed sparse map, a novel detail preserving PatchMatch approach is applied for depth estimation of each image view. The estimated depth maps of multiple views are then fused to a dense point cloud in a memory-efficient way, followed by a detail-aware surface meshing method to extract the final surface mesh of the captured scene. Experiments on ETH3D benchmark show that the proposed method outperforms other state-of-the-art methods on F1-score, with the running time more than 4 times faster. More experiments on large-scale photo collections demonstrate the effectiveness of the proposed framework for large-scale scene reconstruction in terms of accuracy, completeness, memory saving, and time efficiency.
SmartCoin: A novel incentive mechanism for vehicles in intelligent transportation system based on consortium blockchain

An intelligent transportation system (ITS) improves traffic safety and efficiency by enabling vehicles to generate and broadcast messages. However, it is not easy to trust the received message's credibility in an untrusted environment or untrusted network. Additionally, there is no reward mechanism for the honest message generator. Hence, to improve safety and network efficiency and encourage more vehicles to participate in network improvisation, we proposed a novel incentive mechanism for vehicles called SmartCoin based on a consortium blockchain. The proposed scheme aims to achieve social welfare, improved transportation, less traffic congestion, reduced road accidents, and a transportation network free from fraud messages. In the proposed scheme, vehicles give the rating to the message source vehicle based on the validity of the message. Some SmartCoins are credited into the vehicle's account at the blockchain according to the rating of the message. The SmartCoins could be redeemed either at the fuel station, electric charge station for electric vehicles and vehicle service station. Blockchain technology provides decentralization, immutability, transparency to the ITS. The secure storage for the SmartCoin, traffic messages and user information is achieved using blockchain. The secure hash algorithm (SHA256) and elliptic curve digital signature algorithm (ECDSA-192 bit) are used to provide secure communication, message integrity and authentication for vehicles and information. The result analysis reveals that the proposed scheme is effective, comprehensive, and feasible in collecting and storing trust values in the ITS network. The performance evaluation showed enhancement in performance as 90% less consensus delay and 70-80% less storage and communication cost compared to state-of-the-art schemes.
Accelerating noise-tolerant zeroing neural network with fixed-time convergence to solve the time-varying Sylvester equation

Recently, Xiao et al. presented a new noise-tolerant zeroing neural network (NNTZNN) model that can achieve fixed-time convergence even in the presence of some noise to solve the time-varying Sylvester equation. In this brief paper, we extend it to more general case through introducing a power parameter k while retaining its fixed-time convergence and noise-tolerant performance. The new proposed model is here named as the accelerating noise-tolerant zeroing neural network (ANTZNN) model since its fixed convergence time can be shorter than that of the NNTZNN model in certain model parameter ranges. These parameter ranges and the convergence of the ANTZNN model are theoretically analyzed in detail. Numerical experiments are performed to confirm the theoretical results, including the numerical comparisons with the known NNTZNN model under different parameter settings. Furthermore, the ANTZNN model is also applied to the control of robot manipulator, thus showing the applicability of the proposed ANTZNN model.
A Security Framework for Distributed Ledgers

In the past few years blockchains have been a major focus for security research, resulting in significant progress in the design, formalization, and analysis of blockchain protocols. However, the more general class of distributed ledgers, which includes not just blockchains but also prominent non-blockchain protocols, such as Corda and OmniLedger, cannot be covered by the state-of-the-art in the security literature yet. These distributed ledgers often break with traditional blockchain paradigms, such as block structures to store data, system-wide consensus, or global consistency. In this paper, we close this gap by proposing the first framework for defining and analyzing the security of general distributed ledgers, with an ideal distributed ledger functionality, called Fledger, at the core of our contribution. This functionality covers not only classical blockchains but also non-blockchain distributed ledgers in a unified way. To illustrate Fledger, we first show that the prominent ideal block-chain functionalities Gledger and GPL realize (suitable instantiations of) Fledger, which captures their security properties. This implies that their respective implementations, including Bitcoin, Ouroboros Genesis, and Ouroboros Crypsinous, realize Fledger as well. Secondly, we demonstrate that Fledger is capable of precisely modeling also non-blockchain distributed ledgers by performing the first formal security analysis of such a distributed ledger, namely the prominent Corda protocol. Due to the wide spread use of Corda in industry, in particular the financial sector, this analysis is of independent interest. These results also illustrate that Fledger not just generalizes the modular treatment of blockchains to distributed ledgers, but moreover helps to unify existing results.
NetQASM—a low-level instruction set architecture for hybrid quantum–classical programs in a quantum internet

We introduce NetQASM, a low-level instruction set architecture for quantum internet applications. NetQASM is a universal, platform-independent and extendable instruction set with support for local quantum gates, powerful classical logic and quantum networking operations for remote entanglement generation. Furthermore, NetQASM allows for close integration of classical logic and communication at the application layer with quantum operations at the physical layer. This enables quantum network applications to be programmed in high-level platform-independent software, which is not possible using any other QASM variants. We implement NetQASM in a series of tools to write, parse, encode and run NetQASM code, which are available online. Our tools include a higher-level SDK in Python, which allows an easy way of programming applications for a quantum internet. Our SDK can be used at home by making use of our existing quantum simulators, NetSquid and SimulaQron, and will also provide a public interface to hardware released on a future iteration of Quantum Network Explorer.
Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case

Predictive maintenance is an ever-growing topic of interest, spanning different fields and approaches. In the automotive domain, thanks to on-board sensors and the possibility to transmit collected data to the cloud, car manufacturers can deploy predictive maintenance solutions to prevent components malfunctioning and eventually recall to the service the vehicle before the customer experiences the failure. In this paper we present PREPIPE, a data-driven pipeline for predictive maintenance. Given the raw time series of signals recorded by the on-board engine control unit of diesel engines, we exploit PREPIPE to predict the clogging status of the oxygen sensor, a key component of the exhaust system to control combustion efficiency and pollutant emissions. In the design of PREPIPE we deeply investigate: (i) how to choose the best subset of signals to best capture the sensor status, (ii) how much data needs to be collected to make the most accurate prediction, (iii) how to transform the original time series into features suitable for state-of-art classifiers, (iv) how to select the most important features, (v) how to include historical features to predict the clogging status of the sensor. We thoroughly assess PREPIPE performance and compare it with state-of-art deep learning architectures. Our results show that PREPIPE correctly identifies critical situations before the sensor reaches critical conditions. Furthermore, PREPIPE supports domain experts in optimizing the design of data-driven predictive maintenance pipelines with performance comparable to deep learning methodologies while keeping a degree of interpretability.
Coherence, path predictability, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>I</mml:mi></mml:math> concurrence: A triality

It is well known that fringe contrast is not a good quantifier of the wave nature of a quanton in multipath interference. A new interference visibility, based on the Hilbert-Schmidt coherence is introduced. It is demonstrated that this visibility is a good quantifier of wave nature, and can be experimentally measured. A generalized path predictability is introduced, which reduces to the predictability of Greenberger and Yasin, for the case of two paths. In a multipath, which-way interference experiment, the new visibility, the predictability and the I-concurrence (quantifying the entanglement between the quanton and the path-detector), are shown to follow a tight triality relation. It quantifies the essential role that entanglement plays in multipath quantum complementarity, for the first time.
An interlaboratory study to evaluate the forensic analysis and interpretation of glass evidence

The results of an interlaboratory study by ten different laboratories using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and standard test method (ASTM E2927-16e1) for the analysis and comparison of glass evidence is reported. The aims were to evaluate the performance of the new CFGS2 calibration standard for the quantitative analysis of simulated casework samples, evaluate the comparison criterion as recommended by the ASTM E2927 method (association or non-association) and the use of a likelihood ratio (LR) as a quantitative determination of the strength of evidence found. Each laboratory calculated a LR to report the significance of glass source comparisons for a set of glass samples of known origin. Two different types of background databases were used for the calculation of the LR to evaluate the effect of the size and composition of the databases on the calculation of the LR. As expected, glass that originated from the same windowpane was found to be indistinguishable using the ASTM E2927 match criteria and result in a high LR value (strong support for an association) and glass that originated from different vehicles are distinguished (strong support for an exclusion). Glass samples that originated from different vehicles but that were the same make, model and year (or comparisons between the inner and outer pane of the same windshield) were chemically similar and reflected in a low LR. Good agreement among the laboratories was reported with < 5 % relative standard deviations (RSDs) among participants with few exceptions.
ContrasGAN: Unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning

Human Activity Recognition (HAR) makes it possible to drive applications directly from embedded and wearable sensors. Machine learning, and especially deep learning, has made significant progress in learning sensor features from raw sensing signals with high recognition accuracy. However, most techniques need to be trained on a large labelled dataset, which is often difficult to acquire. In this paper, we present ContrasGAN, an unsupervised domain adaptation technique that addresses this labelling challenge by transferring an activity model from one labelled domain to other unlabelled domains. ContrasGAN uses bi-directional generative adversarial networks for heterogeneous feature transfer and contrastive learning to capture distinctive features between classes. We evaluate ContrasGAN on three commonly-used HAR datasets under conditions of cross-body, cross-user, and cross-sensor transfer learning. Experimental results show a superior performance of ContrasGAN on all these tasks over a number of state-of-the-art techniques, with relatively low computational cost.
Designing VRPT experience for empathy toward out-groups using critical incidents and cultural explanations

First person perspective taking presented in head-mounted displays make it a perfect interface to experience empathy toward other people. Since some intercultural misunderstandings stem from ethnocentrism, it is worth considering the possibilities given by the VR experiences to explain behaviors toward out-groups and induce empathetic actions. In this paper we present the design process of ethnoVR, a 7-minutes 360-degree film, which allows taking the perspective of two students - a Chinese and a Pole - who face a problem in efficient communication. The scenario was created with the usage of critical incident technique, adopting user-centered design paradigm.
TikTok’s Spiral of Antisemitism

The growing presence of antisemitism on social media platforms has become more prominent in recent years. Yet, while most of the scholarly attention has been focused on leading platforms like Twitter, Facebook, or Instagram, the extremist immigration to other platforms like TikTok went unnoticed. TikTok is the fastest-growing application today, attracting a huge audience of 1.2 billion active users, mostly children and teenagers. This report is based on two studies, conducted in 2020 and 2021, applying a systematic content analysis of TikTok videos, comments, and even usernames. Data were collected twice, in two four-month periods, February–May 2020 and February–May 2021, to allow for comparisons of changes and trends over time. Our findings highlighted the alarming presence of extreme antisemitic messages in video clips, songs, comments, texts, pictures, and symbols presented in TikTok’s content. TikTok’s algorithm is even more disconcerting since it leads to a spiral of hate: it pushes users who unintentionally view disturbing content to view more. Considering TikTok’s young demographic, these findings are more than alarming; TikTok even fails to apply its own Terms of Service, which do not allow content “deliberately designed to provoke or antagonize people, or are intended to harass, harm, hurt, scare, distress, embarrass or upset people or include threats of physical violence”.