Reviewerly/exHarmony huggingface dataset:
Factors that Influence the Adoption of Computer Assisted Audit Techniques (CAATs) by External Auditors in Yemen

External auditors face new challenges in keeping pace with technological development in the accounting profession, which is how to audits outputs of advanced electronic accounting systems. As a result, several electronic systems and applications have emerged to assist the external auditors in their tasks, which called Computer -Assisted Audit Techniques (CAATs). The main objective of this study to examine the factors influencing of adoption of CAATs in the external audit process in Yemen, using Structural Equation Modeling (SEM). The Unified Theory of Acceptance and Use of Technology (UTAUT) was utilized as a theoretical basis in this study. A quantitative approach was carried out by a questionnaire completed by 312 participants. The results mainly showed that the behavioral intention to adopt CAATs is significantly and positively influenced by the performance expectancy, social influence and facilitating conditions, but it was not influenced by the effort expectancy. Besides, this is the first study in Yemen about the adoption of CAATs.
PrivEdge: From Local to Distributed Private Training and Prediction

Machine Learning as a Service (MLaaS) operators provide model training and prediction on the cloud. MLaaS applications often rely on centralised collection and aggregation of user data, which could lead to significant privacy concerns when dealing with sensitive personal data. To address this problem, we propose PrivEdge, a technique for privacy-preserving MLaaS that safeguards the privacy of users who provide their data for training, as well as users who use the prediction service. With PrivEdge, each user independently uses their private data to locally train a one-class reconstructive adversarial network that succinctly represents their training data. As sending the model parameters to the service provider in the clear would reveal private information, PrivEdge secret-shares the parameters among two non-colluding MLaaS providers, to then provide cryptographically private prediction services through secure multi-party computation techniques. We quantify the benefits of PrivEdge and compare its performance with state-of-the-art centralised architectures on three privacy-sensitive image-based tasks: individual identification, writer identification, and handwritten letter recognition. Experimental results show that PrivEdge has high precision and recall in preserving privacy, as well as in distinguishing between private and non-private images. Moreover, we show the robustness of PrivEdge to image compression and biased training data. The source code is available at https://github.com/smartcameras/PrivEdge.
APPROACH TO THE SYNTHESIS OF NEURAL NETWORK STRUCTURE DURING CLASSIFICATION

Evaluating the number of hidden neurons necessary for solving of pattern recognition and classification tasks is one of the key problems in artificial neural networks. Multilayer perceptron is the most useful artificial neural network to estimate the functional structure in classification. In this paper, we show that artificial neural network with a two hidden layer feed forward neural network with d inputs, d neurons in the first hidden layer, 2d+2 neurons in the second hidden layer, k outputs and with a sigmoidal infinitely differentiable function can solve classification and pattern problems with arbitrary accuracy. This result can be applied to design pattern recognition and classification models with optimal structure in the number of hidden neurons and hidden layers. The experimental results over well-known benchmark datasets show that the convergence and the accuracy of the proposed model of artificial neural network are acceptable. Findings in this paper are experimentally analyzed on four different datasets from machine learning repository.
Similarity Based Information Retrieval Using Levenshtein Distance Algorithm

Sentence similarity plays an important role in many text-related research and applications such as information retrieval, information recommendation, natural language processing, machine translation and translation memory, and etc. Calculating similarity between sentences is the basis of measuring the similarity between texts which is the key to document classification and clustering.Sentence similarity partially depends on the word similarity.This system will display a similar text of field, areas and other facts in document retrieval.This paper uses a sentence matching method of the Levenshtein Distance algorithm.The similarity between words can be calculated from the spelling of words or the meaning of words.Sentence similarity: The similarities between words in different sentences have a great influence on the similarity between two sentences.This system is retrieved from a similar sentence that included Theories, Methods and other facts in the document database.
Deceptive Opinion Detection Using Machine Learning Techniques

Nowadays, online reviews have become a valuable resource for customer decision making before purchasing a product.Research shows that most of the people look at online reviews before purchasing any product.So, customers reviews are now become a crucial part of doing business online.Since review can either promote or demote a product or a service, so buying and selling fake reviews turns into a profitable business for some people now a days.In the past few years, deceptive review detection has attracted significant attention from both the industrial organizations and academic communities.However, the issue remains to be a challenging problem due to the lack of labeled dataset for supervised learning and evaluation.Also, study shows that both the state of the art computational approaches and human readers acquire an error rate of about 35% to 48% in identifying fake reviews.This study thoroughly investigated and analyzed customers' online reviews for deception detection using different supervised machine learning methods and proposes a machine learning model using stochastic gradient descent algorithm for the detection of spam review.To reduce bias and variance, bagging and boosting approach was integrated into the model.Furthermore, to select the most appropriate features in the feature selection step, some rules using regular expression were also generated.Experiments on hotel review dataset demonstrate the effectiveness of the proposed approach.
The Nexus Between Good Governance Indicators And Human Development Index In Africa: An Econometric Analysis

This study examines the association between good governance indicators and the human development index in Africa. Accordingly, it uses the panel data of 49 African countries from 2000-2018 on the six World Bank governance indicators (WGIs) and the UNDP aggregate human development index (HDI). The data are analyzed using descriptive statistics and panel regression analysis. The descriptive statistical analysis shows that most of the countries that are scoring high in the governance indicators are also scoring high in the human development index. It also indicates that Africa's average score in all governance indicators from 2000-2018 ranges between 36.2 % and 40.4%, while the score for human development was 50.8%. Using a one-year moving average, the calculated improvement rates for the eighteen years in all the governance and human development indicators were meager. The finding from the panel regression analysis attests only the three good governance indicators - the rule of law, regulatory quality, and political stability and absence of violence - are significantly and directly associated with the human development index. The finding implies that policy makers in African countries should give emphasis on these three good governance indicators to augment their human development effort.
The Use of Smart Technologies in the Professional Training of Students of the Law Departments for the Development of their Critical Thinking

The objective of this study was to find out how effective the use of smart technologies is in the professional training of students of the law departments, in particular for the formation and development of their critical thinking. According to the author, the combination of the potential of smart technologies in learning and the development of critical thinking in students allows the most optimal solution of the contradictions between the needs of a modern society and the current practice of professional training of students, in particular, students of the law departments. The experimental model with the use of smart technologies was tested in the academic courses â€œIntellectual Property and Copyrightâ€ and â€œFundamentals of Administrative Lawâ€, which are part of the special subjects of the educational and professional training program for students of the Speciality 081: Law. In order to analyze the results obtained and the efficiency ratio of the experimental model against the traditional model and STATA Software was applied. The study showed the efficiency of the use of smart technologies in the formation and development of critical thinking in future lawyers. The author concluded that the use of smart technologies in the professional training of students of the law departments also facilitates feedback, which increases studentsâ€™ learning motivation and allows monitoring of changes in student development. This model can be applied to teaching other subjects in both full-time and distance learning. https://doi.org/10.26803/ijlter.19.3.10
A Bio-Inspired Navigation Strategy Fused Polarized Skylight and Starlight for Unmanned Aerial Vehicles

The migratory animals are capable of traveling long distances by fusing varied information, such as the halteres, the star positions, and the polarization pattern.Motivated by the navigation strategy of migratory animals, a bio-inspired navigation strategy based on polarized skylight, starlight, and halteres is presented in this paper.To enhance the environmental adaptability of the polarization navigation system, a sun vector calculation method tightly fusing the degree of polarization (DoP) and the azimuth of polarization (e-vector) is proposed.By setting the threshold of DoP values of the observation points, the DoP and the e-vector within the threshold range can be selected to deduce the sun position.As such, the robustness and reliability of the sun position can be improved.Concerned with the issue of attitude and heading determination in GNSS-challenged conditions, an integrated navigation strategy combined with polarized light and starlight is proposed.In addition, a Kalman filter is adopted to fuse the navigation data of polarized light, starlight, and inertial sensors.Finally, simulations are conducted to validate the performance of the integrated model.The results illustrated that the proposed navigation strategy is capable of determining the attitude and heading in the scenarios of which the Global Navigation Satellite System (GNSS) is disturbed.
Super-Resolution Geomagnetic Reference Map Reconstruction Based on Dictionary Learning and Sparse Representation

Geomagnetic matching navigation plays a vital role in the navigation and guidance field, and the construction of geomagnetic reference maps plays a significant role in geomagnetic matching navigation.This paper addresses the problem of generating high-resolution geomagnetic reference maps with limited measured data.Most existing methods can hardly satisfy the accuracy requirements.We base our study on the theory of image super-resolution reconstruction and approach this problem from the perspective of dictionary learning and sparse representation.We propose a method of sparse dictionary initialization based on prior information from rectangular harmonic analysis, and then the K-SVD algorithm is applied to the dictionary training process to improve the performance of the sparse dictionary.Three components of the geomagnetic field are considered for multi-channel sparse representation to enhance the quality of the constructed maps.Experimental results show that our proposed method outperforms other geomagnetic reference map construction methods in the reconstructed precision as well as the robustness to noise.
Blockchain Implementation to Verify Archives Integrity on Cilegon E-Archive

A good archive management system must consider information security aspects, such as availability, confidentiality, and integrity. The Cilegon E-Archive (CEA) system is a centralized system for managing the lifecycle of archives. The existing CEA system has several problems, including a single point of failure, low data availability, and difficulty in proving the originality of files. This paper introduces a prototype for a new CEA system that integrates IPFS and the Ethereum private network. In addition, CEA DApp is developed as an interface for users in interacting with CEA system, and its functionality is managed by a smart contract. The results show that the conducted improvements into the CEA system highly improved the system security in terms of preventing archival forgeries.
An unsupervised semantic sentence ranking scheme for text documents

This paper presents Semantic SentenceRank (SSR), an unsupervised scheme for automatically ranking sentences in a single document according to their relative importance. In particular, SSR extracts essential words and phrases from a text document, and uses semantic measures to construct, respectively, a semantic phrase graph over phrases and words, and a semantic sentence graph over sentences. It applies two variants of article-structure-biased PageRank to score phrases and words on the first graph and sentences on the second graph. It then combines these scores to generate the final score for each sentence. Finally, SSR solves a multi-objective optimization problem for ranking sentences based on their final scores and topic diversity through semantic subtopic clustering. An implementation of SSR that runs in quadratic time is presented, and it outperforms, on the SummBank benchmarks, each individual judge’s ranking and compares favorably with the combined ranking of all judges.
Colorimetric Method for Discharge Status Diagnostics based on Optical Spectroscopy and Digital Images

Discharge is a process, which involves changes in multiple species, and it is important for researchers to be able to describe changes in discharge status holistically. In this study, we applied AC and DC high voltage power sources to a short-gap needle-plane device in an atmospheric environment. We propose two new corona diagnosis methods based on spectral data and visible images. The experimental results demonstrate that, in the RGB system, the proportion of the blue component in AC corona images has the characteristic of decrease-stable-increase with voltage, and the corresponding result of the spectra is stable-increase. In the XYZ system, the AC corona chromaticity coordinates of both images and spectra demonstrate a linear fitting result with the voltage, and the image results contain a retracement process, showing shows the increase-stable-decrease trend of chromaticity coordinates x and y, which correlates well with oscilloscope results. The difference between the image and spectra results indicates that, compared to the spectra, high-resolution visible images are more sensitive to imperceptible changes in colorimetry information. The change of DC corona colorimetry characteristics can illustrate the change in AC corona colorimetry characteristics. Additionally, the dominant wavelength is proven to reflect the discharge species, and can reflect the vibrational temperatures. To the best of our knowledge, this paper represents the first attempt to systematically introduce colorimetry theory into discharge images and spectra. We successfully obtained colorimetric indicators with the linearity and low-dimension characteristics, which correspond well with known physical parameters.
USE OF BLOCK CHAIN TECHNOLOGY IN PROVIDING SECURITY DURING DATA SHARING

This article overviews a chain based methodologies for a few security administrations. The Existing framework is contrasted and the proposed framework and it was discovered that the proposed framework has preferred execution over the existing one. Square chain offers a creative way to deal with putting away data, executing exchanges, performing capacities, and building up trust in an open situation. Many consider the square chain as an innovative leap forward for cryptography and digital security. Square chain administrations incorporate verification, secrecy, security and access control list (ACL), information and asset provenance, and uprightness affirmation. Every one of these administrations is basic for the current appropriated applications, particularly because of the huge measure of information being handled over the systems and the utilization of distributed computing. Square chain method is furnished with validation, inspecting, and responsibility, and consequently, it can fill in as a promising instrument for giving secure information correspondence on the system. Validation guarantees that the client is who he/she professes to be. Privacy ensures that information can't be perused by unapproved clients. Protection gives the clients the capacity to control who can get to their information. Provenance permits an effective following of the information and assets alongside their proprietorship and usage over the system. Trustworthiness helps in checking that the information has not been changed or adjusted. These administrations are right now overseen by concentrated controllers, for instance, a declaration authority. Along these lines, the administrations are inclined to assaults on the incorporated controller. Then again, the square chain is made sure about and disseminated records that can help settle a large number of the issues with centralization. From a security viewpoint, the square chain is made and kept up utilizing a distributed overlay arrangement and made sure about through shrewd and decentralized use of cryptography with swarm processing. Block chain offers an imaginative way to deal with putting away data, executing exchanges, performing capacities, and setting up a trust in an open environment. Many consider the square chain as an innovative leap forward for cryptography and digital security, with use cases running from internationally conveyed digital money frameworks. A decentralized distributed storage arranges has been presented with numerous favorable circumstances over the server farm based capacity. Comparable to conventional arrangement, decentralized distributed storage organize use customer side encryption to keep up information security.
Cross-Dataset Transfer Driver Expression Recognition via Global Discriminative and Local Structure Knowledge Exploitation in Shared Projection Subspace

Facial expression is one of the important characteristics of drivers during driving. It is very useful in safe driving detection. Recognizing drivers' expressions by the facial images can be solved with machine learning classification strategies. To obtain a reliable reorganization performance, most of approaches assume that the facial images in the training and testing datasets are independently and identically distributed. However, for real time drivers' facial expression recognition, due to vehicle motion, changes in illumination, noise and head movement, the features displayed for the training dataset may be not valid for the testing dataset. To solve this problem, a novel approach is proposed for cross-dataset transfer driver expression recognition via global discriminative and local structure knowledge exploitation in shared projection subspace (GD-LS-SS). By leaning a shared common subspace, GD-LS-SS utilizes the local geometrical structure of data by exploiting the knowledge of graph topology, meanwhile exploiting the global discriminative information by using the pairwise constrained knowledge between the source and labeled target data. Taking advantage of kernel trick, the kernel version of GD-LS-SS is proposed to learn the kernel projection for handling nonlinear cross-dataset transfer and to further promote the recognition accuracy. Experiments on the KMU-FED dataset show that the satisfactory recognition performance of GD-LS-SS outperforms several traditional non-transfer and related transfer approaches.
Recurrent bag-of-features for visual information analysis

Deep Learning (DL) has provided powerful tools for visual information analysis. For example, Convolutional Neural Networks (CNNs) are excelling in complex and challenging image analysis tasks by extracting meaningful feature vectors with high discriminative power. However, these powerful feature vectors are crushed through the pooling layers of the network, that usually implement the pooling operation in a less sophisticated manner. This can lead to significant information loss, especially in cases where the informative content of the data is sequentially distributed over the spatial or temporal dimension, e.g., videos, which often require extracting fine-grained temporal information. A novel stateful recurrent pooling approach, that can overcome the aforementioned limitations, is proposed in this paper. The proposed method is inspired by the well-known Bag-of-Features (BoF) model, but employs a stateful trainable recurrent quantizer, instead of plain static quantization, allowing for efficiently processing sequential data and encoding both their temporal, as well as their spatial aspects. The effectiveness of the proposed Recurrent BoF model to enclose spatio-temporal information compared to other competitive methods is demonstrated using six different datasets and two different tasks.
Factors and Paths of Transformation and Upgradation of Chemical Industry in Shandong, China

In the context of the Internet, there is a historical fusion between the new round of scientific and technological revolution and industrial transformation and the accelerated transformation of economic development methods. The task of the Shandong chemical industry’s transformation and upgradation is arduous and urgent. In this context, this article studies the influencing factors and development paths of the transformation and upgradation of the Shandong chemical industry. It provides theoretical guidance for high-quality and sustainable development of the Shandong chemical industry. First, this paper uses the fuzzy cognitive map tool to learn the historical laws of the transformation and upgradation of the Shandong chemical industry. It then determines the path of chemical industry transformation and upgradation, according to the interactions and weights between the learned indicators. By adjusting the influence coefficient between the indicators on each path, the degree of change in the influence of the indicator on the target value is analyzed. The critical path and the key indicators on the path according to the degree of impact are determined. Finally, the key path selected in this article is: return on investment (C8) → unit production value environmental consumption (C9) → number of R&amp;D personnel (C4) → R&amp;D expenditure internal expenditure (C6) → industrial output (C1) → energy consumption coefficient (C3). The key indicator on this path is R&amp;D expenditure internal expenditure. According to the results, the development suggestion for the transformation and upgradation of the chemical industry in Shandong is to take the path of green development through technological innovation.