Reviewerly/exHarmony huggingface dataset:
Navigating the Future: Blockchain’s Impact on Accounting and Auditing Practices

This study seeks to meticulously analyze the scholarly discussion on the integration of blockchain technology into accounting and auditing. Based on a total of 67 articles from the Web of Science (WoS) database, this study adopts a bibliometrics and content analysis approach which uses both numerical and visualization techniques to examine the extant literature. It spans the timeframe between 2016 and 2022. Bibliometrix R-package (Biblioshiny, version 4 is employed to analyze the descriptive analysis, which includes publication trends, the most trustworthy sources of scientific publications, prominent scientific authors, prominent documents, and country collaborations. VOSviewer software Version 1.6.20, is used for a network visualization of keywords and bibliographic coupling. Leveraging the content analysis, this research reveals three fundamental themes: first, the use of blockchain technology to strengthen financial reporting systems; second, blockchain technology and the future of auditing; and third, the valuation of cryptocurrencies. Research gaps in the current literature include a lack of comprehensive studies on blockchain’s regulatory and governance aspects in accounting, insufficient exploration of risks and challenges in adopting new technologies in auditing, and a limited understanding of tax consequences, disclosure requirements, and regulatory frameworks for cryptocurrencies, necessitating future research endeavors. Thus, this study extends existing theoretical insights by exploring blockchain’s role in financial reporting, its transformative impact on auditing, and the possible adaptation or development of new valuation methods for cryptocurrencies. It further identifies and discusses future research directions, contributing to potential novel frameworks for addressing regulatory, governance, and socio-economic dimensions of blockchain integration into accounting and auditing practices.
Intelligent Visual Representation for Java Code Data in the Field of Software Engineering Based on Remote Sensing Techniques

In the field of software engineering, large and complex code bases may lead to some burden of understanding their structure and meaning for developers. To reduce the burden on developers, we consider a code base visualization method to visually express the meaning of code bases. Inspired by remote sensing imagery, we employ graphical representations to illustrate the semantic connections within Java code bases, aiming to help developers understand its meaning and logic. This approach is segmented into three distinct levels of analysis. First, at the project-level, we visualize Java projects by portraying each file as an element within a code forest, offering a broad overview of the project’s structure. This macro-view perspective aids in swiftly grasping the project’s layout and hierarchy. Second, at the file-level, we concentrate on individual files, using visualization techniques to highlight their unique attributes and complexities. This perspective enables a deeper understanding of each file’s structure and its role within the larger project. Finally, at the component-level, our focus shifts to the detailed analysis of Java methods and classes. We examine these components for complexity and other specific characteristics, providing insights that are crucial for the optimization of code and the enhancement of software quality. By integrating remote sensing technology, our method offers software engineers deeper insights into code quality, significantly enhancing the software development lifecycle and its outcomes.
Fusion of Deep Sort and Yolov5 for Effective Vehicle Detection and Tracking Scheme in Real-Time Traffic Management Sustainable System

In recent years, advancements in sustainable intelligent transportation have emphasized the significance of vehicle detection and tracking for real-time traffic flow management on the highways. However, the performance of existing methods based on deep learning is still a big challenge due to the different sizes of vehicles, occlusions, and other real-time traffic scenarios. To address the vehicle detection and tracking issues, an intelligent and effective scheme is proposed which detects vehicles by You Only Look Once (YOLOv5) with a speed of 140 FPS, and then, the Deep Simple Online and Real-time Tracking (Deep SORT) is integrated into the detection result to track and predict the position of the vehicles. In the first phase, YOLOv5 extracts the bounding box of the target vehicles, and in second phase, it is fed with the output of YOLOv5 to perform the tracking. Additionally, the Kalman filter and the Hungarian algorithm are employed to anticipate and track the final trajectory of the vehicles. To evaluate the effectiveness and performance of the proposed algorithm, simulations were carried out on the BDD100K and PASCAL datasets. The proposed algorithm surpasses the performance of existing deep learning-based methods, yielding superior results. Finally, the multi-vehicle detection and tracking process illustrated that the precision, recall, and mAP are 91.25%, 93.52%, and 92.18% in videos, respectively.
Enhancing Low-Resource Sanskrit-Hindi Translation through Deep Learning with Ayurvedic Text

The Machine Translation (MT) community is interested in Neural Machine Translation (NMT) because it can persevere continues data over a range of input and output phrase lengths. The NMT system employs a cutting-edge attention mechanism that enables concentrating on a particular input vector of the input sentence rather than obtaining an entire vector for every input sentence. While the Neural approach enhances translation superiority by addressing issues with enduring dependencies and making contextual analysis more accessible, it also requires a sufficient parallel corpus for training, which is challenging in dialects with limited resources. The primary goal of this research is to tackle the distinctive difficulties associated with translating Ayurvedic texts using neural machine translation (NMT). Ayurvedic texts pose unique challenges due to their specialized vocabulary and domain-specific terminology, necessitating a customized approach to achieve precise and meaningful translations. To create a reliable neural machine translation system for the Sanskrit-Hindi language pair, this study utilized a manually created parallel corpus containing Ayurvedic texts and general domain texts. With the help of this corpus, two NMT models were constructed: an attention-based transformer model (NMT2) and an attention-based encoder-decoder recurrent neural network (NMT1). These models integrated deep neural network techniques to optimize the translation process and overcome the constraints of limited training data and complex grammatical structures of Sanskrit. For Sanskrit-to-Hindi, the transformer-based approach received a higher average BLEU score of 48.1 percent and a uni-gram BLEU Score of 56.86 percent.
PauliComposer: compute tensor products of Pauli matrices efficiently

Abstract We introduce a simple algorithm that efficiently computes tensor products of Pauli matrices. This is done by tailoring the calculations to this specific case, which allows to avoid unnecessary calculations. The strength of this strategy is benchmarked against state-of-the-art techniques, showing a remarkable acceleration. As a side product, we provide an optimized method for one key calculus in quantum simulations: the Pauli basis decomposition of Hamiltonians.
RobustMQ: benchmarking robustness of quantized models

Abstract Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities when exposed to various types of noise in real-world applications. Despite the importance of evaluating the impact of quantization on robustness, existing research on this topic is limited and often disregards established principles of robustness evaluation, resulting in incomplete and inconclusive findings. To address this gap, we thoroughly evaluated the robustness of quantized models against various types of noise (adversarial attacks, natural corruption, and systematic noise) on ImageNet. The comprehensive evaluation results empirically provide valuable insights into the robustness of quantized models in various scenarios. For example: 1) quantized models exhibit higher adversarial robustness than their floating-point counterparts, but are more vulnerable to natural corruption and systematic noise; 2) in general, increasing the quantization bit-width results in a decrease in adversarial robustness, an increase in natural robustness, and an increase in systematic robustness; 3) among corruption methods, impulse noise and glass blur are the most harmful to quantized models, while brightness has the least impact; 4) among different types of systematic noise, the nearest neighbor interpolation has the highest impact, while bilinear interpolation, cubic interpolation, and area interpolation are the three least harmful. Our research contributes to advancing the robust quantization of models and their deployment in real-world scenarios.
A Novel Deep Federated Learning-Based Model to Enhance Privacy in Critical Infrastructure Systems

Deep learning (DL) can provide critical infrastructure operators with valuable insights and predictive capabilities to help them make more informed decisions, improving system's robustness. However, training DL models requires large amounts of data, which can be costly to store in a centralized manner. Storing large amounts of sensitive critical infrastructure data in the cloud can pose significant security risks. Federated learning (FL) allows several clients to share learning data and train ML models. Unlike centralized models, FL does not require the sharing of client data. A novel framework is presented to train a VGG16 based CNN global model without sharing the data and only updating the local models among clients using federated averaging. For experimentation, MNIST dataset is used. The framework achieves high accuracy and keep data private using FL in critical infrastructures. The benefits and challenges of FL along with security vulnerabilities and attacks have been discussed along with the defenses that can be used to mitigate these attacks.
(Re)Designing Security and War? Reflections on Transgressive Creativity and International Relations

Abstract In the discipline of IR, a growing number of academics work with artists, designers, architects, and filmmakers to explore a range of global political, economic, and security challenges. At the same time, there has been a concern—made powerfully by Dan Öberg—that there is a danger of what he terms “transgressive creativity” in the way that new approaches and methods are being used to respond to security challenges, especially in a military context. In this essay, I explore how the problem of this “transgressive creativity” is a concern shared by two groups working on the problems of security, war, technology, economy, and politics: critical designers and military designers (or the group that is becoming known as the Archipelago of Design). While the objectives of both communities are different, they both share a view that a sense of openness to collaboration is essential to go beyond traditional institutional approaches in order to make sense of complex and uncertain futures in a time of technological acceleration and geopolitical change. The essay concludes that we should be vigilant to the problems of transgressive creativity that Öberg alerts us to, but we also need to broaden the research agenda to understand how creative techniques are being used by a variety of actors and organizations to address the problems of international politics; academics in IR also need to see whether the “openness” to collaboration has broader disciplinary and methodological implications for researchers.
Patient experience of a virtual reality calm room in a psychiatric inpatient care setting in Sweden: a qualitative study with inpatients

Calm rooms have been developed and implemented in psychiatric inpatient care settings to offer patients a dedicated space for relaxation in a convenient and safe environment. Recent technology developments have enabled virtual reality (VR) equivalents of calm rooms that can be feasibly deployed in psychiatric care settings. While research has shown VR environments to be efficacious in inducing relaxation, little is known how these virtual calm rooms are perceived by patients. The aim of this study was to elucidate patient experiences of using a VR calm room in a psychiatric inpatient setting.Qualitative interview study. Semi-structured interviews were analysed using qualitive inductive content analysis, which focuses on the interpretation of texts for making replicable and valid inferences.Swedish hospital psychiatric inpatient care setting with a wireless, three degrees-of-freedom VR head-mounted display running a calm room application simulating nature environment.20 adult patients (12 women) with bipolar disorder (n=18) or unipolar depression (n=2).Participants experienced the use of the VR calm room as having a positive impact on them, inducing awareness, calmness and well-being. They were thankful to be offered a non-pharmacological alternative for anxiety relief. Participants also expressed that they had some concerns about how they would react emotionally before using the VR device. However, after use, they highlighted that their overall experience was positive. They also expressed that they could see potential for further development of VR technology in psychiatric care.VR technology has the potential to solve pressing logistic issues in offering calm rooms in psychiatric inpatient care. VR calm rooms appear to be appreciated by psychiatric inpatients, who value their accessibility, convenience and variety of modalities offered. Participants perceived an increase in their well-being after use.
Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets

Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.
A full-document analysis of the semantic relation between European Public Assessment Reports and EMA guidelines using a BERT language model

In the European Union, the Committee for Medicinal Products for Human Use of the European Medicines Agency (EMA) develop guidelines to guide drug development, supporting development of efficacious and safe medicines. A European Public Assessment Report (EPAR) is published for every medicine application that has been granted or refused marketing authorisation within the EU. In this work, we study the use of text embeddings and similarity metrics to investigate the semantic similarity between EPARs and EMA guidelines. All 1024 EPARs for initial marketing authorisations from 2008 to 2022 was compared to the 669 current EMA scientific guidelines. Documents were converted to plain text and split into overlapping chunks, generating 265,757 EPAR and 27,649 guideline text chunks. Using a Sentence BERT language model, the chunks were transformed into embeddings and fed into an in-house piecewise matching algorithm to estimate the full-document semantic distance. In an analysis of the document distance scores and product characteristics using a linear regression model, EPARs of anti-virals for systemic use (ATC code J05) and antihemorrhagic medicines (B02) present with statistically significant lower overall semantic distance to guidelines compared to other therapeutic areas, also when adjusting for product age and EPAR length. In conclusion, we believe our approach provides meaningful insight into the interplay between EMA scientific guidelines and the assessment made during regulatory review, and could potentially be used to answer more specific questions such as which therapeutic areas could benefit from additional regulatory guidance.
Reflections on the Application and Validation of Technology in Language Testing

This paper highlights as issues of concern the rapid changes in technology and the tendency to report on partial validation efforts where the work is not identified as forming part of a larger validation project. With close human supervision emerging technologies can have a significant and positive impact on language testing. While technology seems to be constantly changing and improving, there is still a lot to learn in the approaches these papers take in identifying aspects of the constructs of interest amenable to operationalization and measurement through technology. In terms of validation, the paper suggests a number of important things to consider. These include presenting assessment arguments in a form that meets the needs of the stakeholder groups most clearly affected by the claims being made; working towards an interactive communication-based argument approach, ideally involving validators who are independent of the developer; and ensuring that any “partial” validation is explicitly linked to a broader argument. The paper ends by predicting that technology will see the practice of language testing changing radically from where it is today and while fairness, justice and validation will always be primary concerns, the challenges associated with establishing evidence of these will only increase.
Scale‐preserving shape reconstruction from monocular endoscope image sequences by supervised depth learning

Reconstructing 3D shapes from images are becoming popular, but such methods usually estimate relative depth maps with ambiguous scales. A method for reconstructing a scale-preserving 3D shape from monocular endoscope image sequences through training an absolute depth prediction network is proposed. First, a dataset of synchronized sequences of RGB images and depth maps is created using an endoscope simulator. Then, a supervised depth prediction network is trained that estimates a depth map from a RGB image minimizing the loss compared to the ground-truth depth map. The predicted depth map sequence is aligned to reconstruct a 3D shape. Finally, the proposed method is applied to a real endoscope image sequence.
Motion vector‐domain video steganalysis exploiting skipped macroblocks

Abstract Video steganography has the potential to be used to convey illegal information, and video steganalysis is a vital tool to detect the presence of this illicit act. Currently, all the motion vector (MV)‐based video steganalysis algorithms extract feature sets directly from the MVs, but ignoring the embedding operation may perturb the statistical distribution of other video encoding elements, such as the skipped macroblocks (no direct MVs). This paper proposes a novel 11‐dimensional feature set to detect MV‐based video steganography based on the above observation. The proposed feature is extracted based on the skipped macroblocks by recompression calibration. Specifically, the feature consists of two components. The first is the probability distribution of motion vector prediction (MVP) difference, and the second is the probability distribution of partition state transfer. Extensive experiments on different conditions demonstrate that the proposed feature set achieves good detection accuracy, especially in lower embedding capacities. In addition, the loss of detection performance caused by recompression calibration using mismatched quantization parameters (QP) is within the acceptable range, so the proposed method can be used in practical scenarios.
Breaking the Size Barrier: Universal Circuits Meet Lookup Tables

A Universal Circuit (UC) is a Boolean circuit of size $$\varTheta (n \log n)$$ that can simulate any Boolean function up to a certain size n. Valiant (STOC'76) provided the first two UC constructions of asymptotic sizes $$\sim 5 n\log n$$ and $$\sim 4.75 n\log n$$ , and today's most efficient construction of Liu et al. (CRYPTO'21) has size $$\sim 3n\log n$$ . Evaluating a public UC with a secure Multi-Party Computation (MPC) protocol allows efficient Private Function Evaluation (PFE), where a private function is evaluated on private data. Previously, most UC constructions have only been developed for circuits consisting of 2-input gates. In this work, we generalize UCs to simulate circuits consisting of ( $$\rho $$ $$\rightarrow $$ $$\omega $$ )-Lookup Tables (LUTs) that map $$\rho $$ input bits to $$\omega $$ output bits. Our LUT-based UC (LUC) construction has an asymptotic size of $$1.5\rho \omega n \log \omega n$$ and improves the size of the UC over the best previous UC construction of Liu et al. (CRYPTO'21) by factors 1.12 $$\times $$ – $$2.18\times $$ for common functions. Our results show that the greatest size improvement is achieved for $$\rho =3$$ inputs, and it decreases for $$\rho >3$$ . Furthermore, we introduce Varying Universal Circuits (VUCs), which reduce circuit size at the expense of leaking the number of inputs $$\rho $$ and outputs $$\omega $$ of each LUT. Our benchmarks demonstrate that VUCs can improve over the size of the LUC construction by a factor of up to $$1.45\times $$ .
VSS from Distributed ZK Proofs and Applications

Non-Interactive Verifiable Secret Sharing (NI-VSS) is a technique for distributing a secret among a group of individuals in a verifiable manner, such that shareholders can verify the validity of their received share and only a specific number of them can access the secret. VSS is a fundamental tool in cryptography and distributed computing. In this paper, we present an extremely efficient NI-VSS scheme using Zero-Knowledge (ZK) proofs on secret shared data. While prior VSS schemes have implicitly used ZK proofs on secret shared data, we specifically use their formal definition recently provided by Boneh et al. in CRYPTO 2019. The proposed NI-VSS scheme uses a quantum random oracle and a quantum computationally hiding commitment scheme in a black-box manner, which ensures its ease of use, especially in post-quantum threshold protocols. Implementation results further solidify its practicality and superiority over current constructions. With the new VSS scheme, for parameter sets $$(n, t)=(128, 63)$$ and (2048, 1023), a dealer can share a secret in less than 0.02 and 2.0 s, respectively, and shareholders can verify their shares in less than 0.4 and 5.0 ms. Compared to the well-established Pedersen VSS scheme, for the same parameter sets, at the cost of $$2.5\times $$ higher communication, the new scheme is respectively $$22.5\times $$ and $$3.25\times $$ faster in the sharing phase, and notably needs $$271\times $$ and $$479\times $$ less time in the verification. Leveraging the new NI-VSS scheme, we revisit several classic and PQ-secure threshold protocols and improve their efficiency. Our revisions led to more efficient versions of both the Pedersen DKG protocol and the GJKR threshold signature scheme. We show similar efficiency enhancements and improved resilience to malicious parties in isogeny-based DKG and threshold signature schemes. We think, due to its remarkable efficiency and ease of use, the new NI-VSS scheme can be a valuable tool for a wide range of threshold protocols.