Reviewerly/exHarmony huggingface dataset:
Integrating Blockchain Technology into Accounting Curricula: A Template for Accounting Educators

This paper provides a guidance note to accounting educators seeking to incorporate applications of blockchain technology (BT) into accounting curricula. This paper is based on review of recent blockchain related publications in the accounting domain. It explains the areas of blockchain relevant to the accounting profession and provides a list of potential topics for inclusion in the accounting curriculum. Potential academic and industry resources that can be used to develop BT materials for integration into the accounting curriculum are also outlined. Being motivated by the need for the accounting profession to update their blockchain-related awareness and skills to meet the expectations of the accounting industry, this paper will help accounting educators as a guidance note in curriculum design and developing their course materials. This paper represents a reference for accounting educators tasked with incorporating emerging technologies into the accounting curricula to prepare work-ready graduates for the rapidly changing accounting profession.
A sampling and learning framework to prove motion planning infeasibility

We present a learning-based approach to prove infeasibility of kinematic motion planning problems. Sampling-based motion planners are effective in high-dimensional spaces but are only probabilistically complete. Consequently, these planners cannot provide a definite answer if no plan exists, which is important for high-level scenarios, such as task-motion planning. We apply data generated during multi-directional sampling-based planning (such as PRM) to a machine learning approach to construct an infeasibility proof. An infeasibility proof is a closed manifold in the obstacle region of the configuration space that separates the start and goal into disconnected components of the free configuration space. We train the manifold using common machine learning techniques and then triangulate the manifold into a polytope to prove containment in the obstacle region. Under assumptions about the hyper-parameters and robustness of configuration space optimization, the output is either an infeasibility proof or a motion plan in the limit. We demonstrate proof construction for up to 4-DOF configuration spaces. A large part of the algorithm is parallelizable, which offers potential to address higher dimensional configuration spaces.
Traceability Management System Using Blockchain Technology and Cost Estimation in the Metrology Field

Metrological traceability is essential to ensure the reliability of calibration tests. Calibration certificates usually include information on only one upper-level reference standard. As metrological traceability is multi-layered, generally there is no method available for end users to instantly confirm the traceability from the reference standard to a primary standard. This study focuses on the Ethereum blockchain, which has both tamper resistance and high availability, as a digital data management method. To improve the transparency and reliability of calibration tests, a smart contract that traces back to the primary standard is proposed. Consequently, it is confirmed that end users can instantly obtain traceability information. In addition, the execution of smart contracts requires transaction fees. Here, the calculation of the transaction fees is organized, and the traceability management system is discussed from a cost-effective perspective in the field of metrology.
A Picture Is Worth a Thousand Words: Audit Efficiency and Risk Management Through Data Visualization

The chapter aims to discuss impact of data visualization on auditors' analytical procedures and propose practical approaches to address audit efficiency through data visualization techniques. To ensure that efficient presentation of data improves audit efficiency and effectiveness, it is essential that auditors are aware of and effectively apply data visualization techniques in auditing. This chapter presents more profound data visualizations applications in audit procedures that can assist auditors in finding data discrepancies for improving audit risk management through insight development capacity. Further, this chapter considers numerous data visualization analysis tools that audit professionals can use in their specific audit procedures. Finally, this chapter provides avenues for future research in audit data analytics and recommends future research that will allow auditors to realize how technology-driven data visualization tools interact with the audit's standard features.
ShrewdAttack: Low Cost High Accuracy Model Extraction

Machine learning as a service (MLaaS) plays an essential role in the current ecosystem. Enterprises do not need to train models by themselves separately. Instead, they can use well-trained models provided by MLaaS to support business activities. However, such an ecosystem could be threatened by model extraction attacks-an attacker steals the functionality of a trained model provided by MLaaS and builds a substitute model locally. In this paper, we proposed a model extraction method with low query costs and high accuracy. In particular, we use pre-trained models and task-relevant data to decrease the size of query data. We use instance selection to reduce query samples. In addition, we divided query data into two categories, namely low-confidence data and high-confidence data, to reduce the budget and improve accuracy. We then conducted attacks on two models provided by Microsoft Azure as our experiments. The results show that our scheme achieves high accuracy at low cost, with the substitution models achieving 96.10% and 95.24% substitution while querying only 7.32% and 5.30% of their training data on the two models, respectively. This new attack approach creates additional security challenges for models deployed on cloud platforms. It raises the need for novel mitigation strategies to secure the models. In future work, generative adversarial networks and model inversion attacks can be used to generate more diverse data to be applied to the attacks.
Hybrid Bag-of-Visual-Words and FeatureWiz Selection for Content-Based Visual Information Retrieval

Recently, content-based image retrieval (CBIR) based on bag-of-visual-words (BoVW) model has been one of the most promising and increasingly active research areas. In this paper, we propose a new CBIR framework based on the visual words fusion of multiple feature descriptors to achieve an improved retrieval performance, where interest points are separately extracted from an image using features from accelerated segment test (FAST) and speeded-up robust features (SURF). The extracted keypoints are then fused together in a single keypoint feature vector and the improved RootSIFT algorithm is applied to describe the region surrounding each keypoint. Afterward, the FeatureWiz algorithm is employed to reduce features and select the best features for the BoVW learning model. To create the codebook, K-means clustering is applied to quantize visual features into a smaller set of visual words. Finally, the feature vectors extracted from the BoVW model are fed into a support vector machines (SVMs) classifier for image retrieval. An inverted index technique based on cosine distance metric is applied to sort the retrieved images to the similarity of the query image. Experiments on three benchmark datasets (Corel-1000, Caltech-10 and Oxford Flower-17) show that the presented CBIR technique can deliver comparable results to other state-of-the-art techniques, by achieving average accuracies of 92.94%, 98.40% and 84.94% on these datasets, respectively.
A quantum‐like approach for text generation from knowledge graphs

Abstract Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation, such as knowledge graphs. Since all nodes are connected directly, node global representation encoding enables direct communication between two distant nodes while disregarding graph topology. Node local representation encoding, which captures the graph structure, considers the connections between nearby nodes but misses out onlong‐range relations. A quantum‐like approach to learning better‐contextualised node embeddings is proposed using a fusion model that combines both encoding strategies. Our methods significantly improve on two graph‐to‐text datasets compared to state‐of‐the‐art models in various experiments.
Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI

Explainable AI (XAI) systems are sociotechnical in nature; thus, they are subject to the sociotechnical gap--divide between the technical affordances and the social needs. However, charting this gap is challenging. In the context of XAI, we argue that charting the gap improves our problem understanding, which can reflexively provide actionable insights to improve explainability. Utilizing two case studies in distinct domains, we empirically derive a framework that facilitates systematic charting of the sociotechnical gap by connecting AI guidelines in the context of XAI and elucidating how to use them to address the gap. We apply the framework to a third case in a new domain, showcasing its affordances. Finally, we discuss conceptual implications of the framework, share practical considerations in its operationalization, and offer guidance on transferring it to new contexts. By making conceptual and practical contributions to understanding the sociotechnical gap in XAI, the framework expands the XAI design space.
Measures for evaluating IT2FSs constructed from data intervals

Interval type-2 fuzzy sets (IT2FSs) are increasingly used in fuzzy systems for modeling linguistic words. Various approaches exist to construct such IT2FSs. Although the performance of a fuzzy system is significantly influenced by the quality of the utilized IT2FSs however, currently there are no objective criteria for evaluating IT2FSs. This paper introduces a novel objective measure that enables assessing IT2FSs and helps to select the most appropriate ones. The introduced measure is the aggregation of two sets of sub-measures devised to appraise the sufficiency of support and suitability of membership grades. These measures can be effective on the IT2FSs that are built using a set of data intervals collected from a group of subjects. An application with well-defined experiments is devised and implemented to assess the validity of the proposed measure. Experiments are conducted using IT2FSs constructed by the three well-known approaches, i.e., Interval Approach (IA), Enhanced Interval Approach (EIA), and Hao–Mendel Approach (HMA). It is shown that the application performance with the IT2FSs constructed by the three approaches is in accordance with the calculated measures.
AVQS-NN: Adaptive Virtualization for Quantum Services for application support using Neural Networks

Reliable application support is provided by quantum networks utilizing internal and superposition service network topologies. To provide continuous accessibility, the superposition procedure incorporates service replicas and network virtualization. This study presents a quantum requirement model to be prepared for the new virtualization technology and slicing, notwithstanding the uncertainty of that future. Combinatorial learning paradigms are used in this approach to combat the unpredictability that arises from network allocation and service distribution. This method named Adaptive Virtualization for Quantum Services using Neural Network (AVQS-NN) relies on customer satisfaction based on virtualization’s rising demand and rapid service response. Linear neural networks are used in this technique for both virtualization and verification of quantum states. The quantum state is verified to ensure that virtualization enhancements are balanced between network availability and service needs. States are established and toggled between several responses and processing speeds using the suggested technique. This helps provide fast responses, enhanced processing, and reduced delay. Furthermore, with fewer failures, resource utilization is enhanced.
Coherence crossover dynamics in the strong coupling regime

The transient dynamics of quantum coherence of Gaussian states is investigated. The state is coupled to an external environment which can be described by a Fano–Anderson type Hamiltonian. Solving the quantum Langevin equation, we obtain the Greens functions which are used to compute the time evolved first and second moments of the quadrature operators. From the quadrature operator moments, we construct the covariance matrix which is used to measure the coherence in the system. The coherence is estimated using the relative entropy of coherence measure. We consider three different environment spectral densities in our analysis viz, the Ohmic, the sub-Ohmic, and the super-Ohmic densities. In our work, we study the dynamics of the coherent state, squeezed state, and displaced squeezed state. For all these states we observe that when the coupling with the system and the environment is weak, the coherence monotonically decreases and eventually vanishes in the long time limit. Thus all the states exhibit Markovian evolution in the weak coupling limit. In the strong coupling limit, the dynamics for the initial period is Markovian and after a certain period, it becomes non-Markovian where we observe an environmental backaction on the system. To confirm the non-Markovianity of the evolution we use a detection method based on Bures distance formula. Thus in the strong coupling limit, we observe a dynamical crossover from Markovian nature to non-Markovian behavior. This crossover is very abrupt under some environmental conditions and for some parameters of the quantum state. Using a quantum master equation approach we verify the crossover from the dynamics of the dissipation and fluctuation coefficients and the results endorse those obtained from coherence dynamics.
Impact of Big Data and Cloud Computing on Data Analysis

Big data deals with large volume of multidimensional data such as the data generated by Google, Yahoo, LinkedIn, eBay. Big data analytics defines the techniques by which this huge amount of data can be processed and analyzed in a rapid and cost-effective manner. A traditional database management system (DBMS) fails to handle such big data. Therefore, Google develop its own MapReduce technique that can efficiently works on Google File System. Due to the BigTable system embedded into Google MapReduce framework, it becomes easy for searching from millions of data and returning the result in milliseconds. The characteristics of big data lie on three pillars velocity, variety, and volume (stored in data warehouses).
AMED: Aggregated multimedia exploratory and discovery search software

<h2>Abstract</h2> The proliferation of multimedia content is transforming users' information needs from simple lookup-based information retrieval to more complex exploratory and discovery searches. The exploratory search paradigm requires nonlinear access to diverse multimedia documents, which jeopardizes the traditional precision-oriented Search Engine Results Pages (SERP) equipped with media-specific linear lookup lists called verticals. The existing vertical aggregation approaches are instantiated on a specific dataset and are not readily accessible on the web for comparison and reproducibility purposes. Therefore, this article aims to publicize the novel state-of-the-art discovery software aggregating multimedia content to aid the discovery process in exploratory searches. AMED software performs deep semantic analysis, clustering, and summarization on SERP verticals to provide multimedia documents-based traversal. These multimedia documents are further connected to a nonlinear graph based on similarity measures and presented on interactive Search User Interfaces (SUIs) that keep the users captivated within the search space. The empirical evaluation of the proposed data model achieves 99% accuracy score, outperforming the existing aggregation techniques. The further between-subjects (N = 44) usability analysis between the designed SUIs and the Google (baseline) search engine reveals 29.6% better user engagement, 43% more search satisfaction, and 32% more knowledge acquisition with 63.9% reduced clicking efforts.
Desensitization method of meteorological data based on differential privacy protection

Artificial intelligence (AI) profoundly affects the research of meteorology. As an AI method, deep learning greatly improves the accuracy of weather forecasting. Deep learning training requires a lot of data, while the collection of meteorological data faces some problems, such as the long collection cycle, the high cost, and the desensitization requirement. We propose a deep learning model called MDPGAN (Meteorology Differential Privacy Generative Adversarial Network), which introduces a differential privacy framework to reduce the risk of identifying real data by querying synthetic data. The MDPGAN model can generate synthetic weather data with similar statistical characteristics to real weather data. The data generated by the MDPGAN model meets the requirements of data augmentation and data desensitization at the same time. In this paper, the meteorological data set of Kennedy Airport published by NOAA (National Oceanic and Atmospheric Administration) was used for the experiments of the MDPGAN model. The reliability and validity of generated meteorological data were analyzed and tested. The comparison between the generated data and the real data shows that they have similar statistical characteristics, and the synthetic data has achieved good results in the time series prediction of temperature changes. The MDPGAN model provides a convenient tool for the meteorology researches based on deep learning, which can automatically generate a large amount of safe and reliable data, especially suitable for the meteorology researches with small sample data.
A quantum algorithm for Toeplitz matrix-vector multiplication

Toeplitz matrix-vector multiplication is widely used in various fields, including optimal control, systolic finite field multipliers, multidimensional convolution, etc. In this paper, we first present a non-asymptotic quantum algorithm for Toeplitz matrix-vector multiplication with time complexity <?CDATA ${\mathscr{O}}(\kappa {\rm{polylog}}n)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi mathvariant="script">O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>κ</mml:mi> <mml:mi mathvariant="normal">polylog</mml:mi> <mml:mi>n</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> , where κ and 2 n are the condition number and the dimension of the circulant matrix extended from the Toeplitz matrix, respectively. For the case with an unknown generating function, we also give a corresponding non-asymptotic quantum version that eliminates the dependency on the L 1 -norm ρ of the displacement of the structured matrices. Due to the good use of the special properties of Toeplitz matrices, the proposed quantum algorithms are sufficiently accurate and efficient compared to the existing quantum algorithms under certain circumstances.
Design of efficient storage and retrieval of medical records in blockchain based on InterPlanetary File System and modified bloom tree

Abstract In the healthcare sector, medical records contain sensitive information about patients, so guaranteeing the confidentiality and integrity of it is essential. To improve the security of it, blockchain technology is being utilized. The blockchain is a type of distributed ledger and it keeps data securely while also generating trust without the need of third party. It has data storage constraint and Merkle tree preserves data integrity but it is inefficient when searching transactions within it. Hence this paper describes InterPlanetary File System (IPFS) based storage and modified bloom tree data structure which is a hybridization of bloom filter and Merkle tree for efficient searching. To protect data privacy, initially it encrypts medical records using ciphertext policy‐attribute based encryption and then the data stored on IPFS returns a hash value. To diminish the false positive rate (FPR), the hash returned by IPFS is stored in two parts of the bloom filter. The first part stores the data by using “ k ” non‐cryptographic hash function and second part stores the transformed data with the same hash function. The bloom tree is created using Merkle proof for verification of medical record in blockchain. The experiments show that the proposed method reduces the FPR rate and searching complexity is O(log2).
Just-in-time code duplicates extraction

Refactoring is a critical task in software maintenance, and is usually performed to enforce better design and coding practices, while coping with design defects. The Extract Method refactoring is widely used for merging duplicate code fragments into a single new method. Several studies attempted to recommend Extract Method refactoring opportunities using different techniques, including program slicing, program dependency graph analysis, change history analysis, structural similarity, and feature extraction. However, irrespective of the method, most of the existing approaches interfere with the developer's workflow: they require the developer to stop coding and analyze the suggested opportunities, and also consider all refactoring suggestions in the entire project without focusing on the development context. To increase the adoption of the Extract Method refactoring, in this paper, we aim to investigate the effectiveness of machine learning and deep learning algorithms for its recommendation while maintaining the workflow of the developer. The proposed approach relies on mining prior applied Extract Method refactorings and extracting their features to train a deep learning classifier that detects them in the user's code. We implemented our approach as a plugin for IntelliJ IDEA called AntiCopyPaster. To develop our approach, we trained and evaluated various popular models on a dataset of 18,942 code fragments from 13 Open Source Apache projects. The results show that the best model is the Convolutional Neural Network (CNN), which recommends appropriate Extract Method refactorings with an F-measure of 0.82. We also conducted a qualitative study with 72 developers to evaluate the usefulness of the developed plugin. The results show that developers tend to appreciate the idea of the approach and are satisfied with various aspects of the plugin's operation.
A novel PRFB decomposition for non-stationary time-series and image analysis

This work presents a novel perfect reconstruction filterbank decomposition (PRFBD) method for nonlinear and non-stationary time-series and image data representation and analysis. The Fourier decomposition method (FDM), an adaptive approach based on Fourier representation (FR), is shown to be a special case of the proposed PRFBD. In addition, adaptive Fourier–Gauss decomposition (FGD) based on FR and Gaussian filters, and adaptive Fourier–Butterworth decomposition (FBD) based on Butterworth filters are developed as the other special cases of the proposed PRFBD method. The proposed theory of PRFBD can decompose any signal (time-series, image, or other data) into a set of desired number of Fourier intrinsic band functions (FIBFs) that follow the amplitude-modulation and frequency-modulation (AM-FM) representations. A generic filterbank representation, where perfect reconstruction can be ensured for any given set of lowpass or highpass filters, is also presented. We performed an extensive analysis on both simulated and real-life data (COVID-19 pandemic, Earthquake and Gravitational waves) to demonstrate the efficacy of the proposed method. The resolution results in the time-frequency representation demonstrate that the proposed method is more promising than the state-of-the-art approaches.