Reviewerly/exHarmony huggingface dataset:
The Impact of Blockchain on Accounting Information Systems

The objective of the research is to clarify what Blockchain technique is and the developments of its various uses, and identify the accounting perspective of this technique and the possibility of using it in accounting work. It also aims at demonstrating how to utilize its Blockchain technique in accounting information systems, and identify the most important potential effects of the use of technique in accounting information systems. To achieve the research objectives, a descriptive approach was adopted to discuss the fundamentals of Blockchain technique and its most important accounting implications, in addition, its significance for the usefulness and reliability of the development of automated computing accounting systems. The researchers concluded that the use of Blockchain technology for accounting work requires analysis of the related impact on the development of accounting information systems in terms of their implementations and modules, with the need to fully transition in the design of accounting information systems to electronic sophisticated programs and applications developed and technologically advanced.
The new trend for search engine optimization, tools and techniques

&lt;span&gt;Search Engines are used to search any information on the internet. &lt;br /&gt; The primary objective of any website owner is to list their website at the top of all the results in Search Engine Results Pages (SERPs). Search Engine Optimization is the art of increasing visibility of a website in Search Engine Result Pages. This art of improving the visibility of website requires the tools and techniques; This paper is a comprehensive survey of how a Search Engine (SE) works, types and parts of Search Engine and different techniques and tools used for Search Engine Optimization (SEO.) In this paper, we will discuss the current tools and techniques in practice for Search Engine Optimization.&lt;/span&gt;
HyGADE: Hybrid of Genetic Algorithm and Differential Evolution Algorithm

In the area of evolutionary algorithm, Genetic algorithm and Differential evolution algorithms are the most popular algorithms. Both algorithms used to determine different types of optimization problems. One of them is finding a solution which is close to global minima. In this paper, we have suggested a hybrid algorithm of genetic algorithm and differential evolution algorithm. Crossover operator is the main part of the genetic algorithm while a mutation operator is the main part of the differential evolution algorithm. We tried to get the result close with global optimum with this algorithm. In this paper, we used the mutation operator of differential evolution with improved 3-parent crossover of genetic algorithm. We have tested this algorithm on benchmark functions. This algorithm compared with the basic genetic algorithm and DE algorithm and genetic algorithm with modified crossover.
Deep Neural Network Models for Question Classification in Community Question-Answering Forums

Automatic generation of responses to questions is a challenging problem that has applications in fields like customer support, question-answering forums etc. Prerequisite to developing such systems is a requirement for a methodology that classifies questions as yes/no or opinion-based questions, so that quick and accurate responses can be provided. Performing this classification is advantageous, as yes/no questions can generally be answered using the data that is already available. In the case of an opinion-based or a yes/no question that wasn't previously answered, an external knowledge source is needed to generate the answer. We propose a LSTM based model that performs question classification into the two aforementioned categories. Given a question as an input, the objective is to classify it into opinion-based or yes/no question. The proposed model was tested on the Amazon community question-answer dataset as it is reflective of the problem statement we are trying to solve. The proposed methodology achieved promising results, with a high accuracy rate of 91% in question classification.
Particle Swarm Optimization – Support Vector Machine (PSO-SVM) Algorithm for Journal Rank Classification

Support Vector Machine (SVM) is a method with basic classification principles for data that can be separated linearly. As it developed, SVM is designed to work on non-linear problems by incorporating kernel concepts in high-dimensional space. The SVM method implemented in this study for classifying international journals using the SCImago Journal Rank (SJR) dataset. To overcome the disadvantages of SVM performance, the researchers used Particle Swarm Optimization (PSO) to optimize its performance. The purpose of using PSO is to get a better classification performance based on the parameters and functions of the kernel used and to approach the SJR classification system. The process includes normalizing and processing the data on the PSO, followed by implementation using the SVM method. The accuracy results obtained from PSO-SVM are 63.12% using Linear kernels. Based on these results, it assumed that PSO-SVM is still unable to optimize the approach in the SJR classification system if the system is 100% accurate.
Statistical Features-Based Violence Detection in Surveillance Videos

Research over detecting anomalous human behavior in crowded scenes has created much attention due to its direct applicability over a large number of real-world security applications. In this work, we propose a novel statistical feature descriptor to detect violent human activities in real-world surveillance videos. Standard spatiotemporal feature descriptors are used to extract motion cues from videos. Finally, a discriminative SVM classifier is used to classify violent/non-violent scenes present in the videos with the help of feature representation formed out of the proposed statistical descriptor. Efficiency of the proposed approach is tested on crowd violence and hockey fight benchmark datasets.
The build-up construction of quasi self-dual codes over a non-unital ring

There is a local ring [Formula: see text] of order [Formula: see text] without identity for the multiplication, defined by generators and relations as [Formula: see text] We study a recursive construction of self-orthogonal codes over [Formula: see text] We classify, up to permutation equivalence, self-orthogonal codes of length [Formula: see text] and size [Formula: see text] (called here quasi self-dual codes or QSD) up to the length [Formula: see text]. In particular, we classify Type IV codes (QSD codes with even weights) up to [Formula: see text].
Reversible Data Hiding Based on Structural Similarity Block Selection

Reversible data hiding (RDH) methods are widely used in many privacy-sensitive real-time applications for digital images. As an efficient RDH method, prediction-error histogram (PEH) shifting technique has found wide application for its high efficiency in increasing embedding capacity. Nowadays, the performance of most PEH-based methods is evaluated only by Peak Signal to Noise Ratio (PSNR) or Structural Similarity (SSIM) Index. However, more and more applications require high quality in both PSNR and SSIM. Therefore, in this paper, we propose a new RDH method to concentrate on both PSNR and SSIM performance, which involves two key techniques: 1) an SSIM-based block selection technique, 2) a PEH-based optimal expansion bins selection technique. Through block selection, the original image is divided into two parts: smooth blocks and rough blocks. We select pixels of smooth block for double embedding and leave pixels of rough block unchanged. According to the optimal expansion bins, reasonable pixels are chosen for embedding such that the embedding distortion can be reduced. With these improvements, our proposed method has higher SSIM and PSNR after embedding compared to other PEH-based methods. The experimental results demonstrate its superiority over some state-of-the-art counterpart and other conventional PEH-based works.
Augmented Reality-based Media on Molecular Hybridization Concepts Learning

Students' problems in understanding of the hybridization process need to be overcome by using interactive media. Therefore, this study aimed to analyze the results of the validation test and the feasibility test of learning media based on Augmented Reality (AR) on the concept of hybridization. The research method used was the Design-based Research (DBR) method based on technology that supports the learning process within two stages, namely the analysis stage and the design and development stage. Data collecting instruments used were a validation test questionnaire and a limited trial sheet. The media was validated by 3 expert lecturers and 10 respondents of Chemical Education Department students at UIN Sunan Gunung Djati Bandung, Indonesia. Overall validation test results obtained an average value of robserved of 0.88. The validation test obtained a value above 0.30, which showed that the learning media was valid and can be used for the limited trial. The limited trial obtained an average value of 88.59 % within a feasible category. This research concluded that AR learning media on the concept of hybridization is appropriate to be used as teaching material in the learning.
Emirati-Accented Speaker Identification in Stressful Talking Conditions

This research is dedicated to improving “text-independent Emirati-accented speaker identification performance in stressful talking conditions” using three distinct classifiers: “First-Order Hidden Markov Models (HMM1s), Second-Order Hidden Markov Models (HMM2s), and Third-Order Hidden Markov Models (HMM3s).” The database that has been used in this work was collected from 25 per gender Emirati native speakers uttering eight widespread Emirati sentences in each of neutral, shouted, slow, loud, soft, and fast talking conditions. The extracted features of the captured database are called “Mel-Frequency Cepstral Coefficients (MFCCs).” Based on HMM1s, HMM2s, and HMM3s, average Emirati-accented “speaker identification accuracy in stressful conditions” is 58.6%, 61.1%, and 65.0%, respectively. The achieved “average speaker identification accuracy in stressful conditions based on HMM3s” is so similar to that attained in “subjective assessment by human listeners".
Towards the implementation of requirements management specific practices (SP 1.1 and SP 1.2) for small‐ and medium‐sized software development organisations

There is a significant need to give careful consideration to the Capability Maturity Model Integration (CMMI) level 2 specific practices (i.e. SP 1.1 ‘understand requirements’ and SP 1.2 ‘obtain commitment to requirements’), especially in the context of small- and medium-sized software development organisations, in order to assist such organisations in effectively managing their requirements engineering processes. In this study, the authors propose an abstract-level model for each of these two specific practices as well as cover the initial evaluation of the models. In addition, necessary templates and checklists are also provided for each proposed model. The proposed models are based on a significant amount of research in software process improvement, CMMI and requirements engineering. The initial evaluation of the proposed models was executed using an expert panel review process. The results showed that the proposed models provide ease of learning and ease of use, provide stakeholder satisfaction and can be applied to small-and medium-sized software development organisations. It is important to highlight that this study contributes not only to the implementation of SP 1.1 and SP 1.2 of REQM process area in the context of small- and medium-sized software development organisations but also to the body of knowledge on REQM.
Augmented reality for cultural heritage education

In the past few years, it is noticed a fast adoption and increase of mobile devices that are able to retrieve information quickly, regardless of the user's physical environment. Thus, application developers have leveraged it by developing a variety of applications using mobile Augmented Reality (AR) in domains such as cultural heritage education, shopping, gaming, and more. AR technology overlays a virtual object into the real world that enables users to have virtual and real-world experience at the same time. During a visit to a cultural heritage site such as a museum or an art gallery, the learning experience and enjoyment is necessary for a visitor and AR introduces new opportunities to improve them. We propose an AR-based framework for cultural heritage education. We aim to promote cultural heritage education by enriching user's experience and at the same time to offer the knowledge with the most enjoyable way. In order to evaluate our framework, we have developed a server application and an AR-based smartphone application.
Multiple Object Tracking for Dense Pedestrians by Markov Random Field Model with Improvement on Potentials

Pedestrian tracking in dense crowds is a challenging task, even when using a multi-camera system. In this paper, a new Markov random field (MRF) model is proposed for the association of tracklet couplings. Equipped with a new potential function improvement method, this model can associate the small tracklet coupling segments caused by dense pedestrian crowds. The tracklet couplings in this paper are obtained through a data fusion method based on image mutual information. This method calculates the spatial relationships of tracklet pairs by integrating position and motion information, and adopts the human key point detection method for correction of the position data of incomplete and deviated detections in dense crowds. The MRF potential function improvement method for dense pedestrian scenes includes assimilation and extension processing, as well as a message selective belief propagation algorithm. The former enhances the information of the fragmented tracklets by means of a soft link with longer tracklets and expands through sharing to improve the potentials of the adjacent nodes, whereas the latter uses a message selection rule to prevent unreliable messages of fragmented tracklet couplings from being spread throughout the MRF network. With the help of the iterative belief propagation algorithm, the potentials of the model are improved to achieve valid association of the tracklet coupling fragments, such that dense pedestrians can be tracked more robustly. Modular experiments and system-level experiments are conducted using the PETS2009 experimental data set, where the experimental results reveal that the proposed method has superior tracking performance.
RETRACTED ARTICLE: Investor behaviour monitoring based on deep learning

We, the Editors and Publisher of the journal Behaviour & Information Technology, have retracted the following article which was part of the Special Issue on Behaviour Monitoring and Management of Customers, People and Organizations using Deep Learning:Song Wang, Xiaoguang Wang, Fanglin Meng, Rongjun Yang & Yuanjun Zhao (2020) Investor behaviour monitoring based on deep learning, Behaviour & Information Technology, DOI: 10.1080/0144929X.2020.1717627After publication it came to our attention that the person named as the Guest Editor of the Special Issue was impersonated by a fraudulent entity and the articles were not reviewed fully in line with the journal’s peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract the articles. The authors have been informed of this decision.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.
C/N0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers

The carrier-to-noise ratio (C/N0) is an important indicator of the signal quality of global navigation satellite system receivers. In a vector receiver, estimating C/N0 using a signal amplitude Kalman filter is a typical method. However, the classical Kalman filter (CKF) has a significant estimation delay if the signal power levels change suddenly. In a weak signal environment, it is difficult to estimate the measurement noise for CKF correctly. This article proposes the use of the adaptive strong tracking Kalman filter (ASTKF) to estimate C/N0. The estimator was evaluated via simulation experiments and a static field test. The results demonstrate that the ASTKF C/N0 estimator can track abrupt variations in C/N0 and the method can estimate the weak signal C/N0 correctly. When C/N0 jumps, the ASTKF estimation method shows a significant advantage over the adaptive Kalman filter (AKF) method in terms of the time delay. Compared with the popular C/N0 algorithms, the narrow-to-wideband power ratio (NWPR) method, and the variance summing method (VSM), the ASTKF C/N0 estimator can adopt a shorter averaging time, which reduces the hysteresis of the estimation results.
Sentiment analysis by deep learning approaches

We propose a model for carrying out deep learning based multimodal sentiment analysis. The MOUD dataset is taken for experimentation purposes. We developed two parallel text based and audio basedmodels and further, fused these heterogeneous feature maps taken from intermediate layers to complete thearchitecture. Performance measures–Accuracy, precision, recall and F1-score–are observed to outperformthe existing models.
Feature Learning for Enhanced Security in the Internet of Things

Identifying Internet of Things (IoT) devices by their Radio Frequency (RF) fingerprint has important security implications. As the number of connected devices grows, current authentication mechanisms are becoming more susceptible to device spoofing attacks. To combat this, we exploit hardware imperfections in the RF transmit chain to extract device-specific features that uniquely identify an emitter, providing an additional layer of security. This is accomplished with a complex-valued Variational Autoencoder that has a Gaussian Mixture (GMVAE) prior on the latent variables' marginal distribution. By exploiting sequential information in the RF time-series data, we achieve processing gain by integrating multiple latent-space representations from a single device. We test and analyze the proposed approach on real WiFi data and obtain excellent classification results. We also test the proposed model on an Out-of-Distribution (OOD) detection task.
Research on Ship Electronic Power System Fault Diagnosis Based on Expert System

Abstract Aiming at the difficulty in locating the fault point quickly and accurately after the ship electronic power system fault. In this paper, fault diagnosis expert system is used to diagnose the fault occurred to ensure stability of power supply of ship power system. The possible faults are divided into four categories on the base of analysis of ship electrical system fault characteristics. Frame structure is used to describe the connections between the various parts of the ship power system in knowledge presentation. Expert system uses production rules to describe the specific fault and combine the ideal of fuzzy inference to dealing with the uncertainty in fault diagnosis at the same time. The expert system designed in this paper can find fault points and provide solutions quickly and accurately and improves the reliability of the ship power system by test.
Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach

How to recognize targets with similar appearances from remote sensing images (RSIs) effectively and efficiently has become a big challenge.Recently, convolutional neural network (CNN) is preferred in the target classification due to the powerful feature representation ability and better performance.However, the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming.Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small, overfitting occurs and leads to a poor predictive performance.To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability.It improves the storage ability and processing efficiency of RSIs.Furthermore, Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model.It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure.In addition, considering the efficiency of the Naïve Bayes classifier, a distributed Naïve Bayes classifier is designed to reduce the training cost.Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy.The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.
Experimental Analysis of Ant System on Travelling Salesman Problem Dataset TSPLIB

INTRODUCTION: Traveling Salesman Problem (TSP) is one of the vast research areas and has been considered as sub problems in many fields apart from computer science and also in the field of computer science.OBJECTIVES: This paper deals with the comparison of Ant System Ant System (AS) which is a variant of Ant Colony Optimization.METHODS: The performance of the Ant System is analysed by applying it on the Travelling Salesman Problem (TSP).The optimal results found on TSP using AS has been analysed with the elapsed time taken to find the optimal results, its mean, median, variance and the standard deviation.RESULTS: And also, the quality of solutions has been made by calculating the percentage of the optimality and the deviation of the solutions from the TSPLIB provides best known solutions.For instances, TSPLIB data sets have been used.CONCLUSION: Totally, 7 instances have been executed with three different set of parameters for AS and the results are analysed in terms of different parameter settings and performance metrics on each of it.The role of parameters has also been discussed along with the experimental results.
Touché: First Shared Task on Argument Retrieval

Technologies for argument mining and argumentation processing are maturing continuously, giving rise to the idea of retrieving arguments in search scenarios. We introduce Touché, the first lab on Argument Retrieval featuring two subtasks: (1) the retrieval of arguments from a focused debate collection to support argumentative conversations, and (2) the retrieval of arguments from a generic web crawl to answer comparative questions with argumentative results. The goal of this lab is to perform an evaluation of various strategies to retrieve argumentative information from the web content. In this paper, we describe the setting of each subtask: the motivation, the data, and the evaluation methodology.