Reviewerly/exHarmony huggingface dataset:
Audit quality and earnings management after communicating Key Audit Matters (KAMs) in the UAE – audacity and auditors’ perspectives

Research Question: Does the introduction of Key Audit Matters (KAMs) increase audit quality and reduce agency cost associated with it?Does the introduction of KAMs decrease Earnings Management (EM) and agency cost associated with it?Motivation: There have been many studies in the past few years testing the impact of KAMs on the quality of the audit and EM in the developed economies like the United States and United Kingdom, however very few studies exist in the context of the UAE.Moreover, many studies on this topic uses quantitative techniques whereas this study employs qualitative method of interview to judge the perception of auditors about the audit quality and EM in the UAE after the introduction of KAMs. Idea:The research seeks the perception of auditors about the quality of audit and EM after the introduction of communicating KAMs in the UAE. Data and Tools:A semi-structured interview is conducted with thirteen auditors -six Big-4 and seven Non-Big 4 audit firms. Findings:The findings show that the introduction of KAMs enhances the quality of audit in the form of better communication and transparency, especially, in big listed firms.It increases the managerial accountability which results in minimizing the agency cost of equity.However, the study also reveals that the auditors have the audacity to confess the pressures from clients and audit firms which lead to the hidden anomalies and EM practices that resulted in decreasing the audit quality, especially in non-Big 4 firms. Contribution:The research shows that there is no uniformity in audit practice in UAE where own nationals (Emirates) enjoy more flexibility compare to foreigners.In addition, a difference exists in the audit practices of Big-4 and Non-Big 4 auditors.The non-existence Vol. 20, No. 2 of stringent audit regulatory body lead to effect the overall quality of the audit.The government should implement a uniform and robust audit regulatory body to ensure the audit quality.
Intelligent Software Service Configuration Technology Based on Association Mining

Abstract Association relationship and types between software service configuration parameters determine that the configuration items must meet the corresponding rules and constraints in parameter value and setting order. However, the distribution of software services and the architecture based on the loose coupling of heterogeneous components make the association of configuration parameters more concealed. The correlation and constraints of configuration parameters are crucial to ensure system configuration. When deploying, migrating, and updating systems, violating constraints will lead to configuration errors, resulting in system failures. To minimize the loss caused by configuration errors, association rules between configuration items can be mined to avoid potential incorrect settings. This paper proposes an intelligent software service configuration technology based on association mining by analyzing configuration items. The sample data of the configuration file is extracted from the open source code base, and the configuration file is parsed to obtain the configuration item name and value. Then mine the association rule of each configuration item pair based on the configuration item name, value, and type. According to the mined configuration constraints, configuration item error detection, cascade update and value recommendation are performed. Finally, the proposed method is tested and evaluated on six typical open source software, and the experimental results prove the accuracy and effectiveness of the method in this paper.
Embedding Time-Series Features into Generative Adversarial Networks for Intrusion Detection in Internet of Things Networks

In recent years, Generative Adversarial Networks (GAN) have become powerful industrial tools to facilitate various learning tasks, including anomaly detection. This chapter studies a number of GAN architectures used for anomaly detection in the data stream. Moreover, a novel approach is proposed for embedding the dynamic characteristics of the data stream into the GAN-based detector structures. In this process, a GAN model is also proposed for efficient estimation of a confidence measure during the operation that reflects how well samples can be assigned to benign data. Furthermore, this chapter designs an intrusion detection system by developing a GAN-based anomaly detector. To do this, we study the effect of the proposed approach and the selected GAN-based approaches in detecting malicious intrusions in an Internet of Things (IoT) network. Experiments are evaluated in terms of false alarm and missed alarm detection rates. The obtained results indicate the effectiveness of the proposed GAN-based detection approach for the respective task.
An Information Theoretic Approach to Symbolic Learning in Synthetic Languages

An important aspect of using entropy-based models and proposed "synthetic languages", is the seemingly simple task of knowing how to identify the probabilistic symbols. If the system has discrete features, then this task may be trivial; however, for observed analog behaviors described by continuous values, this raises the question of how we should determine such symbols. This task of symbolization extends the concept of scalar and vector quantization to consider explicit linguistic properties. Unlike previous quantization algorithms where the aim is primarily data compression and fidelity, the goal in this case is to produce a symbolic output sequence which incorporates some linguistic properties and hence is useful in forming language-based models. Hence, in this paper, we present methods for symbolization which take into account such properties in the form of probabilistic constraints. In particular, we propose new symbolization algorithms which constrain the symbols to have a Zipf-Mandelbrot-Li distribution which approximates the behavior of language elements. We introduce a novel constrained EM algorithm which is shown to effectively learn to produce symbols which approximate a Zipfian distribution. We demonstrate the efficacy of the proposed approaches on some examples using real world data in different tasks, including the translation of animal behavior into a possible human language understandable equivalent.
Singularity Analysis for the Perspective-Four and Five-Line Problems

This paper deals with image-based visual servoing and pose estimation by observing four and five lines. Our main interest is to determine the relative configurations of the camera and the observed lines that lead to problems in control and stability. Since it is equivalent to finding the singularities of the corresponding Jacobian matrix, we use tools from computational algebraic geometry to seek configurations such that all of its minors vanish simultaneously. By choosing a suitable basis for this matrix, we revisit the problem in the case of three lines to show that one type of the singularities is when the camera lies on the hyperboloid of one sheet uniquely defined by the lines. This result is further exploited to prove that the one-dimensional singularities, if any, in the case of n lines appear when the camera lies on the transversals to the observed lines. Thus, by forcing the transversals to be complex, we can avoid the aforementioned type of singularities in the case of four lines although the algebra shows that there can always be up to 10 inevitable singular locations of the camera for the other type of singularity. For five lines, we find out that there are no singularities in the generic case. The singularities are also characterized for four and five lines with orthogonality and parallelism constraints. Furthermore, a visual servoing library is used to conduct some simulated experiments to substantiate the theoretical results. As expected, we observe problems in control in the vicinity of a singularity as well as increased errors in pose estimation.
iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications

To overcome the limitations of standard datasets with data at a wide-variety of scales and captured in the various conditions necessary to train neural networks to yield efficient results in ADAS applications, this paper presents a self-built open-to-free-use ‘iVS dataset’ and a data annotation tool entitled ‘ezLabel’. The iVS dataset is comprised of various objects at different scales as seen in and around real driving environments. The data in the iVS dataset are collected by employing a camcorder in vehicles driving under different conditions, e.g., light, weather and traffic, and driving scenarios ranging from city traffic during peak and normal hours to freeway traffics during busy and normal conditions. Thus, the collected data are wide-ranging and captured all possible objects at various scales appearing in real-time driving situations. The data collected in order to build the dataset has to be annotated before use in training the CNNs and so this paper presents an open-to-free-use data annotation tool, ezLabel, for data annotation purposes as well.
THE EFFECTIVENESS OF USING HANGMAN GAME TO STRENGTHEN YOUNG LEARNERS’ VOCABULARY

This research was conducted to find out the evidence of Hangman game in strengthening young learners vocabulary to the Year-7 students of SMP Negeri 4 Teluk Keramat-Sambas in Academic Year 2019/2020. This research was a pre-experimental research with one group pre-test and post-test design. The population of this research was all of Year-7 students with the total number of students were 114 students. The cluster random sampling was taken in this research as the technique of sampling, the participants consisted of 26 students. Referring to the research findings, it revealed that the technique could make students vocabulary increased significantly. It can be seen when the students learn vocabulary. Their attention and involvement indicated that the technique gave positive effect on students vocabulary mastery. They already understood the meaning and how to spell words correctly through Hangman game. The result of the research showed that Hangman game was effective to increase students vocabulary achievement. It was proven by the result of t-test computation is higher than t-critical value (9.24&gt;2.060). This means that the Null Hypothesis (H0) which is the implementation of Hangman game does not affect the young learners vocabulary significantly was rejected, while the Alternative Hypothesis which is the implementation of Hangman game affect the young learners vocabulary significantly was accepted. The score of effect size was 1.8 and qualified as strong effect. To conclude, Hangman game enabled the students to recollect vocabulary easily and generate the students motivation in learning process. Thus, the writer suggested this technique to be implemented in teaching vocabulary.
Target tracking method based on interference detection

Considering the problems of similarity interference, partial occlusions, and changes in scale during target tracking, a target tracking method based on interference detection is proposed, which is an improvement over the Siamese fully convolutional classification and regression neural network (SiamCAR) approach. Under the proposed framework, the marginal distribution of the feature maps is used to determine the presence or absence of interferents. When interference is present in a scene, a motion vector composed of the predicted value obtained through a Kalman filter is used as the basis for target prediction. Experiments on the benchmark LaSOT dataset show that the proposed algorithm based on SiamCAR, which introduces motion features, achieves the best performance in videos with similar object interference, partial occlusions, fast motion, and small target tracking, as compared with the classical SiamCAR and other excellent target tracking algorithms.
Telegram App in E-learning of English Reading: Impact on Students’ Reading Ability and Its Efficacy

Telegram is a messaging application which offers fast and reliable messaging and calls, and it has specific feature as a cloud-based messenger with seamless sync. During COVID-19 pandemic, telegram contributes big support in continuing teaching learning process. In this paper, telegram contribution in assisting E-learning of English reading will be revealed, particularly the impact on reading ability and its efficacy on learning. Pre-experimental method was the chosen research design in the research. The research location was at SMP Muhammadiyah Parepare and took 14 sample fromthe third-grade students which was chosen using accidental sampling. The data was gathered using reading test of narrative text to knowthe impact of telegram on students’ reading ability, and questionnaire was distributed to get data on telegram’s efficacy on students learning of English reading. Thus, it was analyzed using quantitative analysis. Based on the reading test, it was found in pre-test that students’ reading skill can be categorized fair based on national education standard 2018 as the average students’ reading score was 61. Therefore, the assistance of telegram in accommodating the learning during pandemic is assumed helpful in boosting students’ learning on English reading. The results of the data analysis on reading test showed that telegram application can support the students’ learning on English reading. It is indicated by the scoring gap between pre-test and post-test. Regarding the data from questionnaire through google form, the students agreed that telegram is effective and has benefits in helping students’ English reading.
Rocky Mountain Region Science Exchange 2020—EarthMAP and the Colorado River Basin

The U.S. Geological Survey (USGS) Rocky Mountain Region (RMR) hosted USGS scientists, managers, program coordinators, and
Towards Better Generalization of Deep Neural Networks via Non-Typicality Sampling Scheme

Improving the generalization performance of deep neural networks (DNNs) trained by minibatch stochastic gradient descent (SGD) has raised lots of concerns from deep learning practitioners. The standard simple random sampling (SRS) scheme used in minibatch SGD treats all training samples equally in gradient estimation. In this article, we study a new data selection method based on the intrinsic property of the training set to help DNNs have better generalization performance. Our theoretical analysis suggests that this new sampling scheme, called the nontypicality sampling scheme, boosts the generalization performance of DNNs through biasing the solution toward wider minima, under certain assumptions. We confirm our findings experimentally and show that more variants of minibatch SGD can also benefit from the new sampling scheme. Finally, we discuss an extension of the nontypicality sampling scheme that holds promise to enhance both generalization performance and convergence speed of minibatch SGD.
The Improved Quantum Secret Sharing Protocol Based on Grover Algorithm

Abstract Quantum secret sharing(QSS) is a cryptographic protocol based on the uncertainty principle and quantum no-cloning theorem and is an essential aspect in quantum computing. This paper describes the Grover search algorithm and how to improve QSS protocol by Grover Algorithm. The QSS based on the Grover algorithm is more secure and easier to operate compared to old QSS protocols. Some scientists improved the security of the first QSS protocol based on QSA by changing the check mode. After that, a protocol with quite different procedures was proposed. The improved one enhances the feasibility of the protocol and it could also make sure that the communication is secure from the attacks.
Smart mobility solutions in smart cities

A flying electric car in the smart city is a type of personal air vehicle that provides door-to-door transportation by both ground and air.In this article, we review the latest developments in flying cars.The autonomous flying car detects and evaluates the environment, uses digital technologies to control and navigate itself without collision, travels in space, can take off and land (VTOL), and uses electric propulsion.Today, several companies are developing flying cars: Terrafugia, Aero Mobil, Volocopter, Lilium, Hyundai, Uber, Bosch.Many prototypes have been built in the last years using a variety of flight technologies and some have true VTOL performance.
Assessment of Factors Influencing the Survival of Breast Cancer Patients using a Machine Learning Approach

Breast cancer is one of the deadliest diseases, claiming approximately 627,000 lives worldwide in 2018–2019. Therefore, early detection of breast cancer through automation in the prediction of the disease will help the medical industry to cure this disease at an early stage and thereby reduce the risk of death drastically. In the present study, the Breast Cancer Wisconsin (Diagnostic) Data Set has been taken from the University of California Irvine (UCI) Machine Learning Repository. The dataset (n=699) contained a total of 30 predictor parameters and one dependent parameter. The dependent variable referred to the type of cancer tissue, i.e., benign or malignant. To predict the type of cancer tissue present in the patient, prediction models were built using 1) Logistic Regression (LR), 2) Decision Tree Classifier (DTC), 3) Random Forest Classifier (RFC), 4) K Nearest Neighbor (KNN), 5) Support Vector Machine (SVM), and 6) Ada Boost Classifier (ABC). To improve the accuracy of the model, a correlation matrix was used and the top 8 features were selected. To improve the accuracy even further, the Synthetic Minority Oversampling Technique (SMOTE) was used to eliminate the problem of class imbalance, and then accuracy was compared before and after SMOTE. The Precision, Recall, and F1 scores are the performance metrics that have been taken into consideration for selecting the best model for the analysis. The results of the study reveal that the KNN algorithm gives the highest accuracy of 95.321% after the SMOTE technique is applied to each of the six algorithms. It has been revealed that while SMOTE aids in the accuracy of some algorithms, it affects the performance of others. This research may be turned into realistic tools that can be utilized in the medical field to more accurately predict the stage of disease for better treatment management.
Extreme expected values and their applications in quantum metrology

We consider the probability distribution when the monotonic function $F(X)$ of the independent variable $X$ takes the maximum or minimum expected value under the two constraints of a certain probability and a certain expected value of the independent variable $X$. We proposed an equal probability and equal expected value splitting method. With this method, we proved four inequalities, and two of them can be reduced to Jensen's inequalities. Subsequently, we find that after dividing the non-monotone function $H(X)$ into multiple monotone intervals, the problem of solving the maximum and minimum expected values of $H(X)$ can be transformed into the problem of solving the extreme value of a multiple-variable function. Finally, we apply the proved theory to solve three problems in quantum information processing. When studying the quantum parameter estimation in Mach-Zehnder interferometer, for an equal total input photon number, we find an optimal path-symmetric input state that makes the quantum Fisher information take the maximum value, and we prove that the NOON state is the path-symmetric state that makes the quantum Fisher information takes the minimum value. When studying the quantum parameter estimation in Landau-Zener-Jaynes-Cummings model, we find the optimal initial state of the cavity field that makes the system obtain the maximum quantum Fisher information. Finally, for an equal initial average photon number, we find the optimal initial state of the cavity field that makes the Tavis-Cummings quantum battery have the maximum stored energy and the maximum average charging power.
Defining an open source CAD workflow for experimental music and media arts

The practice of designing and building instruments, interfaces and hardware in general, became a crucial part of contemporary audio and media arts productions. This task could benefit from the high performance tools offered by state of the art Open source Computer Aided Design (CAD). Although these applications have reached a good level of maturity, their use in the artistic field is still not so widespread, due to an initial barrier probably caused by a lack of accessible documentation and best practices.
Security and Risk Analysis of Financial Industry Based on the Internet of Things

Internet of Things finance is a new type of financial industry in which Internet of Things enterprises and financial institutions borrow information and communication technology and Internet of Things technology and use algorithms and models to realize payment, positioning, investment, measurement, capital financing, asset management, and information intermediary services. The purpose of this paper is to implement risk management and control based on the Internet of Things by enumerating, comprehending, and all-embracing data, effectively improve reliability and efficiency, and minimize risks under certain conditions. First this article on the Internet of things and Internet of things played a role in financial giving relevant summary and interpretation of the analysis of the present condition, and the rate of financial security and risk is based on Internet environment, and for financial security and risk measurement, positioning and computing scheme, attached to the unit and multiple positioning and Taylor calculation model, is used to determine and control the financial risk. In addition, beBS-T encryption model is used to encrypt financial nodes to strengthen financial security and reduce financial risks. AHP is then used to establish a risk judgment matrix to evaluate the financial system and calculate the index of risk weight. After that, simulation algorithm is used to check nodes and risk measurement experiments. The improved ant colony algorithm NACO is compared with the basic algorithm TACO, and the further improved TSN metric algorithm with increment is proposed. The conclusion is drawn as follows: The improved ant colony algorithm NACO is 23.46% lower than the basic ant colony algorithm TACO in the failure jitter end of risk measurement, while the incremental I-NACO algorithm shows better utility and is 7.1% lower than the NACO algorithm in the delay jitter of risk measurement. The Internet of Things is taking a deep role in the financial field, and its shape is in the making. In the process of controlling financial risks, it can make a great contribution to gradually innovate financial forms and reform business models.