Reviewerly/exHarmony huggingface dataset:
The Impact of Artificial Intelligence on Accounting

Innovation technology is changing many aspects of our lives. We live in a time of great transformation that predicts that in the coming years, every aspect of human life will be influenced by artificial intelligence. The phenomenon of robotics is a vast one, with popularity in all fields of activity, as the business world tends towards globalization, and the Internet is progressing at a rapid pace and the flow of information becomes particularly flexible, which makes us witness rapid digitization. of the whole society. Accounting professionals need to turn innovative solutions such as artificial intelligence and automation into opportunities, not threats … new technologies will make life easier. Blockchain is an innovative form of application of information technology in the Internet age, seen as a distributed "registry" defined by decentralization, immutability and transparency. The application of the blockchain in accounting is a topical issue, as it is a common accounting record and it is expected that its application could bring significant changes in the practical work of accountants. Therefore, the main objective of this paper is to analyze the existing literature on the importance of cognitive technologies and the possible impact on accounting, from a bibliometric perspective. The bibliometric analysis is carried out both chronologically and geographically with the support of the Web of Science (WoS) and Scopus database. VOSviewer software was used to process the data, with the aim of providing a comprehensive picture of how cognitive technologies are of interest to academic researchers and accounting practitioners.
The effect of perceived risk, technology trust, and technology awareness on the consumer's behavioural intention to adopt online pharmacy

The online pharmacy business in Saudi Arabia is facing a great challenge in adoption. Therefore, this paper aims to examine the factors influencing the adoption of online pharmacy in Saudi Arabia. This study was carried out under the Unified theory of acceptance and usage of technology-2 (UTAUT-2) to examine the perception of customers on adopting online pharmacy. SPSS and structural equation modelling-Smart-PLS are used for data analysis. The main findings showed that technology trust and technological awareness has a significant impact on the behavioural intention of the consumer to adopt online pharmacy. On the other hand, it is found that perceived risk has an insignificant influence on the consumer's behavioural intention. It is recommended to further investigate the effect of other factors influencing online pharmacy adoption in Saudi Arabia such as performance expectancy, effort expectancy, social influence and hedonic motivation.
Imperfect Quantum Photonic Neural Networks

Quantum photonic neural networks are variational photonic circuits that can be trained to implement high-fidelity quantum operations. However, work-to-date has assumed idealized components, including a perfect $\pi$ Kerr nonlinearity. Here, we investigate the limitations of realistic quantum photonic neural networks that suffer from fabrication imperfections leading to photon loss and imperfect routing, and weak nonlinearities, showing that they can learn to overcome most of these errors. Using the example of a Bell-state analyzer, we demonstrate that there is an optimal network size, which balances imperfections versus the ability to compensate for lacking nonlinearities. With a sub-optimal $\pi/10$ effective Kerr nonlinearity, we show that a network fabricated with current state-of-the-art processes can achieve an unconditional fidelity of 0.891, that increases to 0.999999 if it is possible to precondition success on the detection of a photon in each logical photonic qubit. Our results provide a guide to the construction of viable, brain-inspired quantum photonic devices for emerging quantum technologies.
Analyzing drainage basin orientation and its relationship to active fold growth (Handun anticline, Zagros, Iran)

Combinations of tectonic geomorphological criteria are frequently used to detect vertical and lateral growth of fold structures in tectonically active settings due to their low cost and relative ease of application. The purpose of this study is to analyze the morphometric properties of drainage basins developed into the flanks of a growing anticline to explore the active tectonic growth patterns of the fold. We target the Handun anticline in the Zagros Simply Folded Belt due to it being an actively growing fold structure with a high variability of drainage basin morphologies across different parts of the fold. 57 drainage basins were characterized in terms of their orientation using a newly defined drainage basin orientation (DBO) index, in combination with a set of standard tectonic geomorphological metrics (drainage basin area [Ba], slope [S], asymmetry factor [AF], hypsometric integral [HI], basin shape [Bs], crescentness index [CI], sinuosity of the main drainage [Smd], drainage density [Dd], drainage density of 1st-order streams [Dd1], drainage frequency [Df] and sinuosity of anticline divide [Sad]). Indices were synthesized for spatial comparison between western, central and eastern zones of the growing fold. Results show that the DBO is strongly correlated with its CI, Smd, and Ba. In terms of spatial distribution, the central zone is characterized by higher values of DBO, Ba, S, CI, Smd, and Sad. These contrast with the western and eastern zones (fold plunge regions), which are characterized by higher values of Dd, Dd1, and Df. Of note is the southern limb of the anticline, which is characterized by larger drainage basins, and higher values of AF, CI, and Smd. This suggests higher lateral erosion on the southern fold flank. In contrast, drainage basins with steeper S, and greater DBO and elongation are found on the northern limb, suggesting dominance of vertical erosion. This contrasting south-north erosion pattern suggests that the northern fold flank is actively steepening, and presenting a more youthful topography, with lower and spatially focused erosion that is increasing with time. Overall, high values of the newly proposed DBO metric relate to the presence of a curved and/or an asymmetric forked drainage pattern, and also to the trend of faults and fractures across the fold. These structures are typically oriented oblique to the fold axis, with further modification into a semi-annular drainage pattern developed around a salt diapir.
Boosting adversarial attacks with future momentum and future transformation

The transferability of adversarial examples under the black-box attack setting has attracted extensive attention from the community. Advanced optimization algorithms are one of the most successful ways to improve the transferability among all methods proposed recently. However, existing advanced optimization algorithms either can only slightly enhance the transferability of adversarial examples or take a large amount of computation time. We propose the future momentum and future transformation (FMFT) method to balance the transferability and computation overhead. The FMFT method incorporates two parts, future momentum (FM) and future transformation (FT). FM is inspired by the looking ahead property and updates adversarial examples with the future N-th step momentum during each iteration. FT, on the other hand, makes use of the input transformations during the progress of future momentum calculation to obtain a more robust gradient and reduce computation overhead. Additionally, we propose a new input transformation called random block scaling, which divides the image into various blocks and then scales the blocks differently. Empirical evaluations on the standard ImageNet dataset demonstrate the superiority of our FMFT.
LAD: Layer-Wise Adaptive Distillation for BERT Model Compression

Recent advances with large-scale pre-trained language models (e.g., BERT) have brought significant potential to natural language processing. However, the large model size hinders their use in IoT and edge devices. Several studies have utilized task-specific knowledge distillation to compress the pre-trained language models. However, to reduce the number of layers in a large model, a sound strategy for distilling knowledge to a student model with fewer layers than the teacher model is lacking. In this work, we present Layer-wise Adaptive Distillation (LAD), a task-specific distillation framework that can be used to reduce the model size of BERT. We design an iterative aggregation mechanism with multiple gate blocks in LAD to adaptively distill layer-wise internal knowledge from the teacher model to the student model. The proposed method enables an effective knowledge transfer process for a student model, without skipping any teacher layers. The experimental results show that both the six-layer and four-layer LAD student models outperform previous task-specific distillation approaches during GLUE tasks.
Outlier Vehicle Trajectory Detection Using Deep Autoencoders in Santiago, Chile

In the last decade, a large amount of data from vehicle location sensors has been generated due to the massification of GPS systems to track them. This is because these sensors usually include multiple variables such as position, speed, angular position of the vehicle, etc., and, furthermore, they are also usually recorded in very short time intervals. On the other hand, routes are often generated so that they do not correspond to reality, due to artifacts such as buildings, bridges, or sensor failures and where, due to the large amount of data, visual analysis of human expert is unable to detect genuinely anomalous routes. The presence of such abnormalities can lead to faulty sensors being detected which may allow sensor replacement to reliably track the vehicle. However, given the reliability of the available sensors, there are very few examples of such anomalies, which can make it difficult to apply supervised learning techniques. In this work we propose the use of unsupervised deep neural network models based on stacked autoencoders to detect anomalous routes in vehicles within Santiago de Chile. The results show that the proposed model is capable of effectively detecting anomalous paths in real data considering validation given by an expert user, reaching a performance of 82.1% on average. As future work, we propose to incorporate the use of Long Short-Term Memory (LSTM) and attention-based networks in order to improve the detection of anomalous trajectories.
Are SHAP Values Biased Towards High-Entropy Features?

In this paper, we examine the bias towards high-entropy features exhibited by SHAP values on tree-based structures such as classification and regression trees, random forests or gradient boosted trees. Previous work has shown that many feature importance measures for tree-based models assign higher values to high-entropy features, i.e. with high cardinality or balanced categories, and that this bias also applies to SHAP values. However, it is unclear if this bias is a major problem in practice or merely a statistical artifact with little impact on real data analyses. In this paper, we show that the severity of the bias strongly depends on the signal to noise ratio (SNR) in the dataset and on adequate hyperparameter tuning. In high-SNR settings, the bias is still present but is unlikely to affect feature rankings and thus can be safely ignored in many real data applications. On the other hand, in low-SNR settings, a feature without ground-truth effect but with high entropy could be ranked higher than a feature with ground-truth effect but low entropy. Here, we show that careful hyperparameter tuning can remove the bias.
Quantum State Tomography in Nonequilibrium Environments

We generalize an approach to studying the quantum state tomography (QST) of open systems in terms of the dynamical map in Kraus representation within the framework of dynamic generation of informationally complete positive operator-valued measures. As applications, we use the generalized approach to theoretically study the QST of qubit systems in the presence of nonequilibrium environments which exhibit nonstationary and non-Markovian random telegraph noise statistical properties. We derive the time-dependent measurement operators for the quantum state reconstruction of the single qubit and two-qubit systems in terms of the polarization operator basis. It is shown that the behavior of the time-dependent measurement operators is closely associated with the dynamical map of the qubit systems.
Towards Accurate Children’s Arabic Handwriting Recognition via Deep Learning

Automatic handwriting recognition has received considerable attention over the past three decades. Handwriting recognition systems are useful for a wide range of applications. Much research has been conducted to address the problem in Latin languages. However, less research has focused on the Arabic language, especially concerning recognizing children’s Arabic handwriting. This task is essential as the demand for educational applications to practice writing and spelling Arabic letters is increasing. Thus, the development of Arabic handwriting recognition systems and applications for children is important. In this paper, we propose two deep learning-based models for the recognition of children’s Arabic handwriting. The proposed models, a convolutional neural network (CNN) and a pre-trained CNN (VGG-16) were trained using Hijja, a recent dataset of Arabic children’s handwriting collected in Saudi Arabia. We also train and test our proposed models using the Arabic Handwritten Character Dataset (AHCD). We compare the performance of the proposed models with similar models from the literature. The results indicate that our proposed CNN outperforms the pre-trained CNN (VGG-16) and the other compared models from the literature. Moreover, we developed Mutqin, a prototype to help children practice Arabic handwriting. The prototype was evaluated by target users, and the results are reported.
Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition

Chinese characters carry a wealth of morphological and semantic information; therefore, zero-shot Chinese character recognition with the morphology of Chinese characters has drawn significant attention. The previous methods are mainly based on radical-level decomposition or stroke-level decomposition, which usually cannot capture adequately the structural and spatial information of Chinese characters. In this paper, we develop a stroke-based autoencoder (SAE), to model the sophisticated morphology of Chinese characters with a self-supervised method. Following its canonical writing order, we first represent a Chinese character as a series of stroke images with a fixed writing order, and then our SAE model is trained to reconstruct this stroke image sequence. This pre-trained SAE model can predict the stroke image series for unseen characters, as long as their strokes or radicals are in the training set. We have designed two contrasting SAE architectures on different forms of stroke images. One is fine-tuned on existing stroke-based method for zero-shot recognition of handwritten Chinese characters, and the other is applied to enrich the Chinese word embeddings from their morphological features. The experimental results validate that after pre-training, our SAE architecture outperforms other existing methods in zero-shot recognition and enhances the representation of Chinese characters with their abundant morphological and semantic information.
MSmart: Smart Contract Vulnerability Analysis and Improved Strategies Based on Smartcheck

As is well known, smart contracts on the blockchain store plenty of digital assets, and these contracts deployed on the blockchain are difficult to be modified. For this reason, the analysis and detection of smart contract vulnerabilities have received extensive attention. Smartcheck, a typical Java-implemented static analysis tool of smart contracts, is capable of converting Solidity source code into path diagrams based on the lexical and syntactic analysis, and finds smart contract vulnerabilities by path matching. Although Smartcheck can analyze most of the real-world vulnerabilities, some imperceptible vulnerabilities may be ignored, causing huge economic losses. In order to address these issues, we develop a new tool named MSmart to analyze the vulnerabilities of high risk such as timestamp dependence vulnerabilities, integer overflow vulnerabilities, self-destruct vulnerabilities, etc. MSmart converts the smart contract source code into an intermediate representation, and looks for smart contract vulnerabilities based on intermediate representation and XPath rules. We add new intermediate representation rules of Smartcheck to detect more kinds of vulnerabilities and optimize existing rules to suit the complexity of smart contract. We also implemented smart contract batch detection to shorten the time it takes to find vulnerabilities. To analysis the performance of MSmart, we collect 6000 real-world contracts from Etherscan and design some comparative experiments with other tools. The results of experiment show that MSmart is able to analyze related vulnerabilities better, and false positives and false negatives have been reduced due to our improvements.
Informativeness across Interpreting Types: Implications for Language Shifts under Cognitive Load

Previous quantitative studies discussing interpreting types have focused on various features of linguistic forms in outputs. However, none of them has examined their informativeness. Entropy, as a measure of the average information content and the uniformity of the probability distribution of language units, has been applied to quantitative linguistic research on different types of language texts. In the present study, entropy and repeat rate were used to investigate the difference of overall informativeness and concentration of output texts between simultaneous interpreting and consecutive interpreting. We intend to figure out the frequency distribution patterns of word and word category in two types of interpreting texts. Analyses of linear mixed-effects models showed that entropy and repeat rate can distinguish the informativeness of consecutive and simultaneous interpreting outputs, and consecutive interpreting outputs entail a higher word entropy value and a lower word repeat rate than simultaneous interpreting outputs. We propose that consecutive interpreting is a cognitive process which reaches an equilibrium between production economy for interpreters and comprehension sufficiency for listeners, especially in the case where input speeches are more complex. Our findings also shed lights on the selection of interpreting types in application scenarios. The current research is the first of its kind in examining informativeness across interpreting types, demonstrating a dynamic adaptation of language users to extreme cognitive load.
Exploring the Intersection between Software Maintenance and Machine Learning—A Systematic Mapping Study

While some areas of software engineering knowledge present great advances with respect to the automation of processes, tools, and practices, areas such as software maintenance have scarcely been addressed by either industry or academia, thus delegating the solution of technical tasks or human capital to manual or semiautomatic forms. In this context, machine learning (ML) techniques play an important role when it comes to improving maintenance processes and automation practices that can accelerate delegated but highly critical stages when the software launches. The aim of this article is to gain a global understanding of the state of ML-based software maintenance by using the compilation, classification, and analysis of a set of studies related to the topic. The study was conducted by applying a systematic mapping study protocol, which was characterized by the use of a set of stages that strengthen its replicability. The review identified a total of 3776 research articles that were subjected to four filtering stages, ultimately selecting 81 articles that were analyzed thematically. The results reveal an abundance of proposals that use neural networks applied to preventive maintenance and case studies that incorporate ML in subjects of maintenance management and management of the people who carry out these tasks. In the same way, a significant number of studies lack the minimum characteristics of replicability.
Care-full co-curation: critical urban placemaking for more-than-human futures

Can participatory engagements in the form of more-than-human co-creation be a generative form of socially and ecologically-just and critical urban placemaking? In recent years, there has been growing interest across sectors in bringing together diverse needs, desires, and experiences through co-creative processes that foster transformative futures, which involve caring with, and for, specific stakeholders. However, institutionalised and increasingly formulaic approaches to care and participation raise questions of who exactly is being included and excluded, how co-creation is carried out, and to what ends. This article explores three interrelated examples of critical urban placemaking in the arts, interrogating how we might design for liveable urban futures as matters of care. We propose that this is achievable through the critical conceptual lens of 'care-full co-curation' which prioritises the vital co-existence of all beings, and designs with and alongside the needs and lived experiences of human and other-than-human actors and agencies alike.
Obstacle Avoidance Control of Unmanned Aerial Vehicle with Motor Loss-of-Effectiveness Fault Based on Improved Artificial Potential Field

This paper presents an obstacle avoidance control strategy for an underactuated quadrotor unmanned aerial vehicle with motor loss-of-effectiveness fault and disturbance. The control system is divided into two parts: the obstacle avoidance loop and the tracking loop. By introducing the height factor in the artificial potential field function, an improved obstacle avoidance strategy is designed in the obstacle avoidance loop. Compared with the existing literature, the proposed obstacle avoidance strategy can avoid falling into the trap of the local optimum when a UAV encounters obstacles. At the same time, considering the sudden motor loss-of-effectiveness fault of UAV, adaptive technology is used to estimate the fault parameters online to restrain the effects of motor loss-of-effectiveness fault in the tracking loop. The stability of the closed-loop UAV system is guaranteed by stabilizing each of the subsystems through backstepping technology. Simulations are conducted to demonstrate the effectiveness of the designed obstacle avoidance control strategy.