Reviewerly/exHarmony huggingface dataset:
IMPACT OF CORONA PANDEMIC (COVID-19) ON EXTERNAL AUDIT ON JORDANIAN BANKS

The study aimed to demonstrate the impact of the Corona pandemic on the external audit process according to the auditing standards in Jordanian banks, and the researcher relied on the deductive approach by distributing a questionnaire dealing with the impact of the pandemic on the audit steps and procedures in accordance with international standards. The study found that the Corona pandemic had a statistical effect on planning procedures, determining the level of materiality, auditing risks, and methods of collecting evidence, and affected the contents of the auditor's report in light of the Corona pandemic. The study recommends the need to focus on developing audit procedures and using modern and electronic technologies in carrying out audits. The need for professional bodies in Jordan to develop procedures and guidelines and hold training courses for auditors on the Corona pandemic and how to deal with it.
Comparative Analysis of Teacher Education in India and Bangladesh: An Overall Outlook

India and Bangladesh are two neighbouring countries with huge cultural similarities and have same cultural background. Both the countries have glorious past and prestigious educational background, not only that, once both the countries belong to a same country. Teacher Education is an important programme related to the development of the teacher proficiency and competence. Regarding Teacher Education, India and Bangladesh have remarkable stand in respect of the global scenario. Effort is given here to make a comparative study on Teacher Education between India and Bangladesh. The chief objectives of the study are to draw the clear picture of the development of Teacher Education in India, to elucidate the development of Teacher Education in Bangladesh and to make comparison between India and Bangladesh in respect of various dimensions of Teacher Education. It is largely a qualitative study. This study finds that regarding status of Teacher Education, there is no significant difference in Teacher Education between India and Bangladesh, though India is quite ahead of Bangladesh. Keywords- Teacher Education; Teacher proficiency; Qualitative study
Multi-Sparse Gaussian Process: Learning based Semi-Parametric Control

A key challenge with controlling complex dynamical systems is to accurately model them. However, this requirement is very hard to satisfy in practice. Data-driven approaches such as Gaussian processes (GPs) have proved quite effective by employing regression based methods to capture the unmodeled dynamical effects. However, GPs scale cubically with number of data points n, and it is often a challenge to perform realtime regression. In this paper, we propose a semi-parametric framework exploiting sparsity for learning-based control. We combine the parametric model of the system with multiple sparse GP models to capture any unmodeled dynamics. MultiSparse Gaussian Process (MSGP) uses multiple sparse models with unique hyperparameters for each one, thereby, preserving the richness and uniqueness of each sparse model. For a query point, a weighted sparse posterior prediction is performed based on N neighboring sparse models. Hence, the prediction complexity is significantly reduced from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) to O(Npu <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), p and u are data points and pseudo-inputs respectively for each sparse model. We validate MSGP's learning performance for a quadrotor using a geometric controller in simulation. Comparison with GP, sparse GP, and local GP shows that MSGP has higher prediction accuracy than sparse and local GP, with significantly lower time complexity than all three. We also validate MSGP on a real quadrotor setup for unmodeled mass, inertia, and disturbances. The experiment video can be seen at: https://youtu.be/zUk1ISux6ao.
Multiple Target Prediction for Deep Reinforcement Learning

This paper presents the multiple target prediction for model-free deep reinforcement learning. Traditionally, the learning algorithm based on deep Q network (DQN) suffers from slow convergence in learning process which usually constrains the system performance. To speed up the learning process, this study incorporates the prediction network and the auxiliary replay memory in DQN which allows us to predict multiple target values over different actions rather than relying on a single target value from an action. The prediction network is trained in a sequential manner by using the samples from replay memory while the auxiliary replay memory is introduced to store the states and the predicted targets which are related to individual possible actions that are taken. With these two additional components functioned in DQN algorithm, the resulting approach can efficiently predict Q values and rewards to train an agent with comprehensive target values. The experiments on different tasks demonstrate the merit of multiple target prediction in reinforcement learning.
Attestation Infrastructures for Private Wallets

In this paper we focus on one part of the trust infrastructures needed for the future virtual assets industry, namely the attestation infrastructure related to key management in private wallet systems. Our focus is on regulated private wallets utilizing trusted hardware, and the capability of the wallet to yield attestation evidence suitable to address requirements in several use-cases, such as asset insurance and regulatory compliance. We argue that attestation services will be needed as a core part of the key management lifecycle for private wallets in true decentralized systems.
Performance Comparison of Data Mining Algorithms Which Occupy the Top: C4.5 and SVM

Data is a collection of various kinds of facts that are stored but do not have meaning. Mining is a mining process. So data mining can be interpreted as the process of mining large and complex amounts of data for new knowledge or information that can be useful for data owners. There is a sequence of systematic ways to solve problems in Data Mining, known as Data Mining algorithms. The IEEE International Conference on data mining which was conducted in 2006 produced the 10 most frequently used data mining algorithms by the research community around the world. Two of the ten most commonly used algorithms are the C4.5 algorithm and the Support Vector Modeling (SVM) algorithm. The methodology used in this research is The Knowledge Discovery in Database (KDD) stage. This study aims to compare the C4.5 with the SVM in terms of performance where what will be seen is the value of Area Under Curve (AUC), Receiver Operating Characteristic (ROC), Accuracy, Error, Precison, and Recall.
Using Mind Mapping to Teach Speaking Skill to the Tenth Grade Students of MAN Palopo

The purpose of this research is to find out whether the students who are taught by using mind mapping have better achievement than the students who were taught by using a non-mapping strategy. This research used a quasi-experimental method. The population of this research consisted of tenth-grade students of MAN Palopo. The writer used purposive sampling, where there were two classes as the sample. They were; class X MIA I and X MIA II. Each class consisted of 30 students. The writer gave pretest and posttest to the students—three aspects of speaking skill assessed, namely accuracy, fluency, and comprehensibility. The result of this research shows that the students who were taught by using mind mapping did not have better achievement than the students who were taught by using non-mind mapping. The data showed that t-obtained was higher than t-table (t-o &gt; t-table). The findings of t-obtained were 0.216, whereas the level of significance of 0.05 (5%) was 2.002. It means that 2.002 ≥ 0.216. The orienting number of significance shows that 0.830 &gt; 0.05. Therefore, the Null Hypothesis (H0) accepted, and the Alternative Hypothesis (H1) rejected. Some problems found by the writer namely, the students switch code to speak up because they did not know the English meaning of the words, the students still read the text to speak because they did not memorize the language pattern that had been given. So, they had difficulties in speaking without reading the text. They felt hungry, tired, and bored in the class, and some of the students had meetings’ organization after they were an exam.
A HYBRID DEEP NEURAL NETWORK FOR ASPECT BASED SENTIMENT ANALYSIS ON RAJYA SABHA QUESTIONS

In the modern era, Technology and Politics are strongly connected and have become inseparable.This paper proposes a hybrid approach by using unsupervised and supervised techniques for analyzing the innumerable opinions expressed in the Rajya Sabha Question Hour to extract aspects, sentiments and perform Aspect Based Sentiment Analysis (ABSA).An unsupervised Attention-Based Aspect Extraction-Long Short-Term Memory (ABAE-LSTM) network is used to identify the 'N' cluster of aspects present in the corpus and categorize the aspect terms present in the questions into one of these aspect categories.Supervised Neural Networks, Target Dependent-LSTM (TD-LSTM), Target Control-LSTM (TC-LSTM) and Attention Term Aspect Extraction-LSTM (ATAE-LSTM) are employed to perform ABSA.Experiments conducted on the unsupervised ABAE-LSTM show high coherence scores between the aspect terms, and this also creates a gold standard training data set with aspect and sentiment labels in the domain.Results from supervised techniques then display promising accuracy for all three models.
Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields

Analysis of heterogeneous patterns in complex spatio-temporal data finds usage across various domains in applied science and engineering, including training autonomous vehicles to navigate in complex traffic scenarios. Motivated by applications arising in the transportation domain, in this paper we develop a model for learning heterogeneous and dynamic patterns of velocity field data. We draw from basic nonparameric Bayesian modeling elements such as hierarchical Dirichlet process and infinite hidden Markov model, while the smoothness of each homogeneous velocity field element is captured with a Gaussian process prior. Of particular focus is a scalable approximate inference method for the proposed model; this is achieved by employing sequential MAP estimates from the infinite HMM model and an efficient sequential GP posterior computation technique, which is shown to work effectively on simulated data sets. Finally, we demonstrate the effectiveness of our techniques to the NGSIM dataset of complex multi-vehicle interactions.
Scalable Vector Gaussian Information Bottleneck

In the context of statistical learning, the Information Bottleneck method seeks a right balance between accuracy and generalization capability through a suitable tradeoff between compression complexity, measured by minimum description length, and distortion evaluated under logarithmic loss measure. In this paper, we study a variation of the problem, called scalable information bottleneck, in which the encoder outputs multiple descriptions of the observation with increasingly richer features. The model, which is of successive-refinement type with degraded side information streams at the decoders, is motivated by some application scenarios that require varying levels of accuracy depending on the allowed (or targeted) level of complexity. We establish an analytic characterization of the optimal relevance-complexity region for vector Gaussian sources. Then, we derive a variational inference type algorithm for general sources with unknown distribution; and show means of parametrizing it using neural networks. Finally, we provide experimental results on the MNIST dataset which illustrate that the proposed method generalizes better to unseen data during the training phase.
Presenting a Method for Improving Echo Hiding

In this article, one of the most important methods of steganography on VoIP called echo hiding is improved. This method has advantages in maintaining the statistical and perceptual characteristics of audio signals as well as security against the sensitivity of the human audio system (HAS). However, it has lots of errors in detecting coded and hidden messages, which is detectable using existing steganalysis methods. The percentage of extracting messages in these improved methods of echo hiding is high, but they lower the security of the method. In this article, a method is presented to improve the method of extracting echo hiding, and enhance its security through a combined method based on spread spectrum. To improve the extraction, a wrong hypothesis is corrected and substituted. To improve security using a pseudo-random key generation algorithm, spread spectrum and echo hiding methods are used randomly. To evaluate the proposed extraction, numerous extraction tests are carried out in the normal state and in the event of attacks. A steganalyser has also been used to assess security improvements. The results gained through different experiments on the security of steganography indicate a 3-percent increase in steganalysis errors. The proposed extraction method was modified based on the main method and resulted in more than 10% improvement.
Emotion Invariant Speaker Embeddings for Speaker Identification with Emotional Speech

Emotional state of a speaker is found to have significant effect in speech production, which can deviate speech from that arising from neutral state. This makes identifying speakers with different emotions a challenging task as generally the speaker models are trained using neutral speech. In this work, we propose to overcome this problem by creation of emotion invariant speaker embedding. We learn an extractor network that maps the test embeddings with different emotions obtained using i-vector based system to an emotion invariant space. The resultant test embeddings thus become emotion invariant and thereby compensate the mismatch between various emotional states. The studies are conducted using four different emotion classes from IEMOCAP database. We obtain an absolute improvement of 2.6% in accuracy for speaker identification studies using emotion invariant speaker embedding against average speaker model based framework with different emotions.
PENGGUNAAN KONJUNGSI SEBAGAI UNSUR KOHESI PADA BERITA UTAMA SURAT KABAR HARIAN SINGGALANG EDISI MEI—JUNI 2020

ABSTRACTThe purpose of this study is to describe the use of conjunction on utama news of Harian Singgalang edition Mei—Juni 2020. This type of research is qualitative research using descriptive methods. The data in this study were obtained from the results of a documentation study with the observation and note technique. The techniques used to analyze data are data identification, data presentation, and drawing conclusions. The research results found 978 times the use of conjunctions in 45 data sources with 638 sentences. Coordinative conjunctions were found 518 times, subordinative conjunctions were found 336 times, correlative conjunctions were found 14 times, conjunctions between sentences were found 102 times, and conjunctions between paragraphs was found 8 times. Based on the results of the study, it is concluded that the most common conjunctions found are coordinative conjunctions while the least conjunctions found are conjunctions between paragraphs. Besides, the use of correct conjunctions outweighs the use of improper conjunctions. Kata Kunci: Konjungsi, Kohesi, Berita Utama
Multiscale Saliency Detection for Colored 3D Point Clouds Based on Random Walk

Saliency detection for 3D visual data has been actively studied, but relatively little effort has been made to detect both of the geometric and photometric saliency for large-scale colored 3D point clouds (LSC3DPCs). We propose a random walk based multiscale saliency detection algorithm for LSC3DPCs acquired by terrestrial light detection and ranging devices. We employ Fast Point Feature Histogram descriptor and Lab colors to estimate the geometric and photometric features of points, respectively. We partition an input LSC3DPC model into supervoxel clusters at three different scales of octree. Then we build a fully-connected graph of clusters at each scale such that an edge connecting two clusters with more dissimilar features to each other is assigned a higher weight. We perform random walk simulation on the graphs at multiple scales to yield multiscale saliency maps, respectively, which are then averaged together to generate a final saliency map. Experimental results show that the proposed method estimates the global and local saliency of LSC3DPCs more faithfully compared with the existing method.
Organization of online courses of technological design in the conditions of tripartite social partnership

The object of this research is the process of organization and implementation of technological training of college and university students in studying educational modules and professional modules in the conditions of tripartite social partnership, such as college&amp;ndash;university&amp;ndash;enterprise, organized as online courses. The subject of this research is the remote teaching technique for students in the context of elaboration of technological processes for manufacturing machine parts. The author examines such important aspect of the topic as the variants of planning educational space within the framework of tripartite social partnership with the integration of actual projects and modeling of professional activity of the specialist in students&amp;rsquo; project-based activity. The article reviews the step-by-step procedure of implementation of project-based learning technique in the course of technological training of university and college students, oriented towards formation of professional competences and job functions of professional standards. The results of pedagogical experiment on the formation of technological competences in the course of implementation of project-based learning in the specifically arranged educational space are presented. The scientific novelty consists in implementation of project-based learning technologies in the conditions of quasi-professional activity in fulfillment of actual production projects by the college and university students. The author&amp;rsquo;s special contribution consists in the development of algorithm for joint implementation of project-based learning technique for training college and university students in the profession-oriented educational space. The main conclusions lie in substantiation of high pedagogical effectiveness of the planned project-based learning in terms of social partnership between the college&amp;ndash;university&amp;ndash;enterprise that reconstructs profession-oriented educational space.
Two Training Strategies for Improving Relation Extraction over Universal Graph

This paper explores how the Distantly Supervised Relation Extraction (DS-RE) can benefit from the use of a Universal Graph (UG), the combination of a Knowledge Graph (KG) and a large-scale text collection. A straightforward extension of a current state-of-the-art neural model for DS-RE with a UG may lead to degradation in performance. We first report that this degradation is associated with the difficulty in learning a UG and then propose two training strategies: (1) Path Type Adaptive Pretraining, which sequentially trains the model with different types of UG paths so as to prevent the reliance on a single type of UG path; and (2) Complexity Ranking Guided Attention mechanism, which restricts the attention span according to the complexity of a UG path so as to force the model to extract features not only from simple UG paths but also from complex ones. Experimental results on both biomedical and NYT10 datasets prove the robustness of our methods and achieve a new state-of-the-art result on the NYT10 dataset. The code and datasets used in this paper are available at https://github.com/baodaiqin/UGDSRE.
An improved Dynamic material Characterisation of carbon/epoxy composites at intermediate strain rates

This paper reports an experimental investigation on strain rate dependence of carbon/epoxy composites at intermediate strain rates. Dynamic tensile tests at strain rates between 0.1 and 200 per second are performed using a high-speed servo-hydraulic test machine equipped with an improved load introduction device. The data reduction analysis is performed to handle the inertial effect (i.e., system ringing on force measurement) during the testing. Load introduction technique is improved to mitigate the system ringing and lightweight grip systems are designed in order to increase the natural frequency of the test system. In addition, the inertial induced problems are resolved for strain measurement using Digital Image Correlation (DIC) technique. A general and robust experimental technique to characterise strain rate behaviour of carbon/epoxy composite materials are thoroughly investigated. As a result, an improved test protocol to perform dynamic mechanical testing at intermediate strain rates using a high-speed servo-hydraulic machine is proposed.
Design socialmente responsável: ontem e hoje

Society is built around a process of complex transformations, articulated by the strength of connectivity and the internet.In this sense, we seek to discuss the designer's contributions to the social dimension with a focus on sustainable development.Based on a bibliographic and documentary survey, we sought to understand the concepts and definitions of sustainable development and design for sustainability and related themes to answer the following question: how can design collaborate with projects in the social dimension, integrating innovation and sustainable development?Theoretical studies resulted in an analysis supported by Vezzoli's studies and produced an important direction for possible local actions in the social dimension, as well as a better understanding of the contributions of design to sustainable development in the social context.
Visual Sentiment Analysis for Few-Shot Image Classification Based on Metric Learning

Visual sentiment analysis is an interesting and chal-lenging research problem. that investigates sentiment estimation from images. Most studies have focused on estimating a few specific sentiments and their intensities, using several complex convolutional neural network (CNN) models. However, sentiment estimation from a small number of images using few-shot 1-way learning has not been sufficiently investigated. This research aims to accurately estimate sentiment using few-shot 1-way learning from given images that evoke different emotions. We first introduce a visual sentiment dataset based on Plutchik’s wheel of emotions, called Senti8PW. We perform a few-shot image classification using Senti8PW, where we present a highly accurate deep neural network model with a small number of parameters and convolutions. We then use Senti8PW to perform experiments using few-way 1-shot learning. We also employ the Euclidean distance and Cosine similarity as a metric of our proposed model. Each emotion is assumed to have a probability distribution. After training our deep neural network, we predict an evoked emotion for a given unknown image. We also perform experiments to compare our proposed model with existing models. The classification system of four layers of convolutions with 5-way 1-shot learning proves to be the best in terms of balancing accuracy and a number of model parameters. Thus, results demonstrate that our model outperforms existing state-of-the-art algorithms with regard to the of balance between accuracy and parameter number.
Semi-Supervised Learning of CNN for Land-Use Classification from Aerial Photograph

In this study, we consider generating efficiently labeled data from map symbols of GIS data as a means to efficiently increase the data. Further, we propose to perform semi-supervised learning using this method. We demonstrate the effectiveness of the proposed method in 6 classes of land-use classification.