Reviewerly/exHarmony huggingface dataset:
The Role of the Mechanisms of Information Technology Governance in Activating the Risk Management of Computerized Accounting Information Systems According to (NIST800-37) Framework of Internal Control

This study assesses and analyzes the reality of the mechanisms of information technology governance and risk management of computerized accounting information systems, in addition to testing and analyzing their relationships. It highlights the effect of using the mechanisms of information technology governance in reducing the risks of computerized accounting information systems in business organizations in the Kurdistan Region of Iraq. The aim of this study is to identify the availability of mechanisms of information technology governance in business organizations in the Kurdistan Region, the risks ofcomputerized accounting information systems and how to manage them as well as the inadequacy of controls for the security of these systems.In order to achieve these goals, four main hypotheses are adopted and directed to test the reality of the research variables. The study adopts the descriptive analytical method within the framework of the theoretical presentation of the research and variables, and uses the statistical analysis in writing the practical side of the study. Data is collected through a questionnaire to obtain the opinions of 56 auditors and managers of joint-stock companies in the Kurdistan Region of Iraq.The research reached a set of findings and recommendations, the most prominent of which is that the mechanisms of information technology governance has a role in activating risk management of computerized accounting information technology, and that the mechanisms of information technology governance has an effect on risk management of computerizedaccounting information systems. The most important recommendation is the need for organizations to adopt internal control frameworks related to protecting computerized accounting information systems to protect them from the risks related to them.
A Neural Document Language Modeling Framework for Spoken Document Retrieval

Recent developments in deep learning have led to a significant innovation in various classic and practical subjects, including speech recognition, computer vision, question answering, information retrieval and so on. In the context of natural language processing (NLP), language representations learned by referring to autoregressive language modeling or autoencoding have shown giant successes in many downstream tasks, so the school of studies have become a major stream of research recently. Because the immenseness of multimedia data along with speech have spread around the world in our daily life, spoken document retrieval (SDR), which aims at retrieving relevant multimedia contents to satisfy users' queries, has become an important research subject in the past decades. Targeting on enhancing the SDR performance, the paper concentrates on proposing a neural retrieval framework, which assembles the merits of using language modeling (LM) mechanism in SDR and leveraging the abstractive information learned by the language representation models. Consequently, to our knowledge, this is a pioneer study on supervised training of a neural LM-based SDR framework, especially combined with the pretrained language representation methods. A series of empirical SDR experiments conducted on a benchmark collection demonstrate the good efficacy of the proposed framework, compared to several existing strong baseline systems.
Text Complexity in English Textbooks for Junior High School: A Systemic Functional Perspective

This study investigates the progression of text complexity based on Systemic Functional Linguistics theory.Being largely qualitative, this study analyzed three consecutive textbooks (grade VII, VIII, and IX) issued by the Ministry of Education.The data were analyzed in terms of lexical density, lexical variation, and grammatical complexity to determine the level of complexity of the texts.The findings show that the lexical density and lexical variation fluctuated as the level progressed.It means that the lexical density and lexical variation throughout the textbooks were found to have an inconsistent development from lower to higher grades.Meanwhile, the findings in grammatical intricacy indicate that the complexity of texts decreased as the level progressed.Thus, it can be concluded that the texts required reviewing to correspond to the stages of students' intellectual development.
Automatic Product Saleability Prediction using Sentiment Analysis on User Reviews

From past few decades information technology industry is on the rise and software development companies thrive to provide the best results for the consumers. Sentiment Analysis is a powerful tool that can help the software industry and company to better evaluate user needs and cater the software in a way to maximise the sales potential. Sentiment Analysis combined with machine learning techniques can help us learn about the industrial trends. Greater than 40 thousand Exabyte (10^18) of data is estimated to be a part of the internet out of which 80% is unstructured and can be processed to useful means using NLP techniques. In proposed work sentiment analysis has been applied on user review to predict its saleability or in simpler words: How well a product will sell? Customer feedback was collected from users through a feedback form which required them to express their satisfaction with the product by answering a set of questions which serves as features and input to the machine which evaluates the features such as user interface, Performance, feasibility, cost effectiveness and customer service by extracting the polarity from each. The result shows that sentiment analysis is a viable option to predict the saleability of a product. The empirical results are close to the customer's own expected probability of buying.
The Usage of Mobile Learning: Comparative Studies among Technical and Vocational Education Students in Selected Universities

This research is conducted mainly to examine the difference among Technical and Vocational Education (TVE) students at Universiti Pendidikan Sultan Idris (UPSI) and Universiti Tun Hussein Onn Malaysia (UTHM)on their computer proficiency, knowledge on mobile learning(ML), application preferred, and attitude towards ML. Quantitative research method via questionnaire distributed to 618 participants. T-test analysis was used to obtainthe differences among students in both the universities via Statistical Package for Social Sciences (SPSS) version 23.0 for Windows. The results showed there was no difference found among these students. Hereby, ML is vital for lifelong learning and the access to mobile devices are available for everyone these days. It has made ML to be a tool to learn regardless of time and place among the students today.
An Entropy Clustering Approach for Assessing Visual Question Difficulty

We propose a novel approach to identify the difficulty of visual questions for Visual Question Answering (VQA) without direct supervision or annotations to the difficulty. Prior works have considered the diversity of ground-truth answers of human annotators. In contrast, we analyze the difficulty of visual questions based on the behavior of multiple different VQA models. We propose to cluster the entropy values of the predicted answer distributions obtained by three different models: a baseline method that takes as input images and questions, and two variants that take as input images only and questions only. We use a simple k-means to cluster the visual questions of the VQA v2 validation set. Then we use state-of-the-art methods to determine the accuracy and the entropy of the answer distributions for each cluster. A benefit of the proposed method is that no annotation of the difficulty is required, because the accuracy of each cluster reflects the difficulty of visual questions that belong to it. Our approach can identify clusters of difficult visual questions that are not answered correctly by state-of-the-art methods. Detailed analysis on the VQA v2 dataset reveals that 1) all methods show poor performances on the most difficult cluster (about 10% accuracy), 2) as the cluster difficulty increases, the answers predicted by the different methods begin to differ, and 3) the values of cluster entropy are highly correlated with the cluster accuracy. We show that our approach has the advantage of being able to assess the difficulty of visual questions without ground-truth (i.e., the test set of VQA v2) by assigning them to one of the clusters. We expect that this can stimulate the development of novel directions of research and new algorithms.
Complex verification of rule-based knowledge bases using VTF-logic

In this paper, we consider a complex procedure for verifying rule-based knowledge bases using logics with vector semantics in the form of VTF-logic with a special representation of facts and rules. The described technique allows solving verification tasks such as identifying unrelated facts, identifying incomplete rules, identifying logical circles, checking the correspondence between a set of hypotheses and a set of terminal facts, identifying contradictions, identifying silent rules, and identifying abnormal breaks in the inference chains. Rules in the knowledge base are arranged in a causal way. It means if the same fact is included in the right part of one rule and the left part of another, the first rule is always performed earlier. As a result, the verification procedure is linear in terms of the number of rules and exponential in terms of the number of starting facts. However, the amount of calculations can be significantly reduced by selecting groups of facts related to a particular hypothesis. The novelty of the study is the use of logics with vector semantics for rules verification. This retains the ability to inference in case of anomalous values of truth. This logic provides to use the inference engine for dynamic verification of knowledge. As a result, you do not need to include additional architectural elements into the system (for example, rule set dependency chart), create external verification programs, etc. In fact everything is provided by regular means of the expert system. Static verification is provided by a special representation of facts and rules.
Linear Codes over the Finite Ring &amp;lt;i&amp;gt;Z&amp;lt;/i&amp;gt;&amp;lt;sub&amp;gt;15&amp;lt;/sub&amp;gt;

In this paper, the structure of the non-chain ring Z 15 is studied.The ideals of the ring Z 15 are obtained through its non-units and the Lee weights of elements in Z 15 are presented.On this basis, by the Chinese Remainder Theorem, we construct a unique expression of an element in Z 15 .Further, the Gray mapping from 15 n  to 2 15 n  is defined and it's shown to be distance pre- served.The relationship between the minimum Lee weight and the minimum Hamming weight of the linear code over the ring Z 15 is also obtained and we prove that the Gray map of the linear code over the ring Z 15 is also linear.
Distributed Cloud Platform Access Collaborative Selection Algorithm based on Multi-objective Optimization

Abstract Decentralization is the mainstream trend of large-scale center construction. Based on the requirements of mass service access, big data processing and high reliability of State Grid 95598 cloud platform, a distributed cloud platform access collaboration selection algorithm based on multi-objective optimization is proposed. The algorithm considers load balance of distributed cloud platform and average user experience quality. In addition, it measures the comprehensive performance of the cloud platform from users and load balance of distributed cloud platform based on the synergy degree in the selection process in order to avoid “cask effect”. The simulation results show that the algorithm can adjust the access cloud centers of users in real time and balance loads of cloud centers, which can provide technical support for cloud platform construction in the era of big data.
Measurement of Student Outcomes Attainment for ABET Accreditation in Electromechanical Engineering Department in UOT

Student outcomes attainment represents the core of the ABET accreditation.In this paper, a details of the four years' work in Energy and Renewable Energy Program in Electromechanical Engineering Department in University of Technology was demonstrated, concentrating on the process of student outcomes attainment measurement.The attainment of student outcomes represents a challenge in all programs in Iraqi Universities.The process monitored by ABET Foundation expertise through IREX and they approved that the process reached right route although the degree of attainment needs more work in the coming years to reach accreditation level.
THE EFFECTIVENESS OF LEARNING WRITING NARRATIVE BASED ON EXPERIENCE WITH CLUSTERING AND MODELING TECHNIQUES IN THE 5th GRADE PRIMARY SCHOOL STUDENTS

This study aims to demonstrate the effectiveness of clustering techniques and modelling techniques in learning to write narrative essays based on personal experience. From the results of preliminary observations, it was found that by using conventional methods, students' ability in learning to write narrative essays based on personal experience was still lacking. The use of clustering and modelling techniques in learning to write narrative essays based on personal experience was expected to have a significant influence. This type of research used quantitative research that was True Experiment Design with pre-test, post-test control group design. The research findings showed that an increase in students’ learning outcomes in the experimental class by using clustering techniques and modelling techniques. Based on the results of the study, it can be concluded that the use of clustering and modelling techniques in learning to write narrative essays based on personal experience had a significant effect.
Catch the Shadow: Person Tracking Under Occlusion with a Single RGB-D Camera

Locomotion in physical space is one of the most natural forms of interaction in applications such as virtual reality systems. Although there are many algorithms to track walking people, existing methods mostly fail to cope with occluded bodies in the setting of multi-person tracking with one camera. This paper proposes a method to overcome this challenge by fusing skeletal with shadow data, both of which are captured by a single RGB-D camera. Skeletal tracking provides the positions of people that can be captured directly, while their shadows are used to track them when they are no longer visible. Our experiments confirm that this method can efficiently handle full occlusions. It thus has substantial value in resolving the occlusion problem in multi-person tracking, even with other kinds of cameras.
Interpersonal Meaning Analysis of Selected Song Lyrics from Queen’s Greatest Hits Album

This research aims to analyze five selected song lyrics from Queen's Greatest Hits album, and it is to generate the meanings from the analysis results.The objectives of this study are to describe the types of interpersonal meaning by looking at the most dominant mood types and value of modality.The design of this research is qualitative data collection and analysis.The main sources of data were taken from Queen's Greatest Hits album which are Bohemian Rhapsody, Somebody to Love, Don't Stop me Now, We are the Champions, and You're my Best Friend song lyrics.The data were analyzed by using Halliday's Systemic Functional Linguistics theory (Halliday, 1994), which is focused on mood types (Gerot & Wignell, 1994) and the value of modality (Eggins, 1994).The analysis shows that Declarative Mood and median modality are dominantly used in the song lyrics.Declarative mood realizing the speech function of a statement is used to convey information.Meanwhile, median modality tends to positions the speaker equally with the listener when the expression of willingness is used and also generated how strong the speaker commits himself to the truth.
Research Status of Relationship between Seismic Instrument Intensity and Ground Motion Parameters

Abstract Seismic intensity can simply reflect the extent and distribution of earthquake’s impact and damage. A comprehensive and detailed assessment of intensity requires a long time and a lot of manpower and material resources. The ambiguity of the intensity assessment index and the subjectivity of the professionals often result in differences in assessment results. Seismic instrument intensity based on strong earthquake observation records can effectively avoid the above problems, and can quickly and objectively assess the impact and possible damage degree of the surface near the strong earthquake station. This paper summarizes the research progress of seismic instrument intensity and points out the development direction of seismic instrument intensity.
A Prototype of FPGA Based on Genetic Algorithm Core Connected to a Cluster

In this paper, it was proposed to design and implement a system of parallel processing to find the required solutions from selective algorithms as quickly.Motives for writing paper are the current performance of computer systems that depend on evolutionary algorithms (EA), and the wide spread of the (EA), and its application to a very wide range of scientific fields, and by taking advantage of the Field programmable gate array (FPGA) board due to its high speed of implementation.The values were validated and the Genetic Algorithm (GA) was used as a functional model and implementation.Also, in the most important stages, the process of calculating fitness function, which is considered an executive criterion for the (GA), with terminal computers with high speeds and medium specifications, was done for the purposes of calculating fitness function independently of Board.Identical results were obtained at 100% accuracy by applying the work to a non-linear quadratic.
THE EFFECT OF WEST QURNA OILFIELD ON THE PART OF EUPHRATES RIVER, NORTHERN BASRA GOVERNORATE, IRAQ: GEOMORPHIC AND STRUCTURAL GEOLOGY INTEGRATED ANALYSIS

The study utilized geomorphic and structural geology analysis integrated analysis to understand the effect of West Qurna Oil Field (or West Qurna Structure) on the part of Euphrates river route, whether it’s related to neotectonic activity or it’s a natural phenomenon. The Euphrates River Drainage Basin is located in the northern part of the Basra governorate in the south of the Mesopotamian Basin. The channel of the river is mostly straight and characterized by large loops. The channel sinuosity of this river system is analyzed in order to draw conclusions on the neotectonic activity of the region. The original status of the river thalweg was digitized from recent satellite images using the new Landsat 8 Scenes. Sinuosities at different sample section lengths were computed in a GIS software environment ArcGIS 10, providing so-called ‘sinuosity spectra’ for each point of the analyzed using a moving-window method and different window sizes channels. High sinuosity sections were correlated to geological subsurface structures of West Qurna Oil Field identified, indicating their neotectonic activity. A structural analysis (geometric and genetic analyses) performed to identify its type, origin and to find its relevance to main regional tectonic events. The geometric analysis detect that the bending fold mechanism (vertical force) could responsible about form West Qurna oil. Thus, thinning feature associated with West Qurna Oil Field. This mechanism could be connected with growth of salt structure of Hormuz Salt below it as confirmed by negative residual anomaly associated with West Qurna Oil Field.
Petri Net Based Intelligent Substation Fault Diagnosis

In order to further improve the effectiveness and comprehensiveness of substation fault diagnosis, this paper proposes a substation fault diagnosis method to effectively use the secondary system to monitor alarm information online. First, the original fault information received by the intelligent substation from each monitoring system is denoised. Then, the analytical model and Petri net model for the primary and the secondary system in the substation are established respectively, which are able to accurately locate the fault that may occur and obtain the reasons for the protection and switch misoperation with the secondary system diagnosis results. The case studies show that the method can identify fault elements and give detailed fault reasons under the mis-operations of protection and switch, and therefore can further optimize the substation fault diagnosis.
Comparison between handwritten word and speech record in real-time using CNN architectures

This paper presents the development of a system of comparison between words spoken and written by means of deep learning techniques. There are used 10 words acquired by means of an audio function and, these same words, are written by hand and acquired by a webcam, in such a way as to verify if the two data match and show whether or not it is the required word. For this, 2 different CNN architectures were used for each function, where for voice recognition, a suitable CNN was used to identify complete words by means of their features obtained with mel frequency cepstral coefficients, while for handwriting, a faster R-CNN was used, so that it both locates and identifies the captured word. To implement the system, an easy-to-use graphical interface was developed, which unites the two neural networks for its operation. With this, tests were performed in real-time, obtaining a general accuracy of 95.24%, allowing showing the good performance of the implemented system, adding the response speed factor, being less than 200 ms in making the comparison.
Channel Attention Based Generative Network for Robust Visual Tracking

In recent years, Siamese trackers have achieved great success in visual tracking. Siamese networks can achieve competitive performance in both accuracy and speed. However, they may suffer from the performance degradation due to the case of large pose variations, out-of-plane, etc. In this paper, we propose a novel real-time Channel Attention based Generative Network (AGSNet) for Robust Visual Tracking. AGSNet can better recognize the targets undergoing signifi-cant appearance variations and having similar distractors. The AGSNet model introduces a channel favored feature attention to the template branch to enhance the discriminative capacity and uses a simple generative network in the instance branch to capture a variety of target appearance changes. With the end-to-end offline training, our model can achieve robust visual tracking in a long temporal span.Experimental results on benchmark datasets OTB-2013 and OTB-2015, demonstrate that our proposed tracker outperforms other approaches while runs at more than 40 frames per second.
Selective Convolutional Network: An Efficient Object Detector with Ignoring Background

It is well known that attention mechanisms can effectively improve the performance of many CNNs including object detectors. Instead of refining feature maps prevalently, we reduce the prohibitive computational complexity by a novel attempt at attention. Therefore, we introduce an efficient object detector called Selective Convolutional Network (SCN), which selectively calculates only on the locations that contain meaningful and conducive information. The basic idea is to exclude the insignificant background areas, which effectively reduces the computational cost especially during the feature extraction. To solve it, we design an elaborate structure with negligible overheads to guide the network where to look next. It’s end-to-end trainable and easy-embedding. Without additional segmentation datasets, we explores two different train strategies including direct supervision and indirect supervision. Extensive experiments assess the performance on PASCAL VOC2007 and MS COCO detection datasets. Results show that SSD and Pelee integrated with our method averagely reduce the calculations in a range of 1/5 and 1/3 with slight loss of accuracy, demonstrating the feasibility of SCN.