Reviewerly/exHarmony huggingface dataset:
The Impact of Continuous Auditing Technology in ERP System on Improving the Internal Audit Performance: A Field Study

The study objective is to investigate the impact of CA technology adoption in ERP system on improving the internal audit performance. To achieve this objective, the levels of changing from traditional auditing to CA in ERP system have been presented, whereas this new technology has the capability to improve the internal audit performance. Moreover, the literature related to the study has been reviewed, and a field study has been conducted using the questionnaire list and interviews with internal auditors, managers, as well as IT staff (programmers and analysts of information systems) in the selected companies. The findings of the study indicated that (1) there is significant difference among the categories of the study on the impact of CA technology in ERP system on improving IA performance, (2) there is a significant relationship between adopting continuous auditing in ERP system and the internal audit performance, and (3) there is a significant impact of adopting continuous auditing technology on improving the internal audit performance.
Approximation algorithm for spherical $ k $-means problem with penalty

&lt;p style='text-indent:20px;'&gt;The &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-means problem is a classical combinatorial optimization problem which has lots of applications in many fields such as machine learning, data mining, etc. We consider a variant of &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-means problem in the spherical space, that is, spherical &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-means problem with penalties. In the problem, it is allowable that some nodes in the spherical space can not be clustered by paying some penalty costs. Based on local search scheme, we propose a &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ \left(4 (11+4\sqrt{7})+ \epsilon\right) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-approximation algorithm using singe-swap operation, where &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ \epsilon $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is a positive constant.&lt;/p&gt;
MSNet: multi-scale network for crowd counting

Nowadays, due to various challenges such as large-scale variation of population, mutual occlusion, perspective distortion and so on, crowd counting has gradually become a hot issue in computer vision. To address the large- scale variation exists in the images, in this paper, we propose a novel multi-scale network called MSNet which aims to maintain continuous variations and count the number of pedestrians accurately. While most state-of-the- arts multi-scale and multi-column networks aim to integrate the scale information of heads with different size, lots of researches still need to do to achieve continuous variations. In MSNet, specifically, the first ten layers of the visual geometry group network(VGG) are used as the backbone to extract the rough features of images and a multi-scale block is employed to maintain the scale information which contains several receptive kernels to obtain a better performance towards the difficulty of scale-variation. Inspired by the knowledge that using multiple small receptive field kernels to replace a single large receptive field will get a better performance, we utilize two dilated convolutions with the receptive field of 5 to replace the large kernel. Our MSNet has moderate increase in computation, and we evaluate our method on three benchmark datasets including ShanghaiTech (Part A: MAE=59.6, RMSE=96.1; Part B: MAE=7.5, RMSE=12.1), UCF-CC-50(MAE=207.9, RMSE=273.8) and UCF-QNRF(MAE=93, RMSE=158) to show the outperformance of our method.
Bee-Inspired Algorithm for Groups of Cyber-Physical Robotic Cleaners with Swarm Intelligence

This chapter dwells upon the control of groups of autonomous cyber-physical robotic cleaners designed for wet cleaning of rooms in large public or commercial buildings. It presents various strategies to control the movements of a robot group or those of robots within a group. It also discusses the factors affecting the intragroup behavior of robots. The author offers a method for building robot formations, which is based on using the wet trails of the leading robot. To control groups of cyber-physical robotic cleaners, the chapter proposes an algorithm that combines a global strategy based on the bees search algorithm with elements of swarm intelligence, and a local formation-building method for orientation to the leading robot and its neighbors. The author has simulated some robot group control and formation tasks in NetLogo. The results can be used to solve other similar problems, such as harvesting, deactivating the area from radioactive substances, disinfecting the area from viruses and others.
Improved Light Field Compression Efficiency through BM3D-Based Denoising Using Inter-View Correlation

Multi-view or light field images have recently gained much attraction from academic and commercial fields to create breakthroughs that go beyond simple video-watching experiences. Immersive virtual reality is an important example. High image quality is essential in systems with a near-eye display device. The compression efficiency is also critical because a large amount of multi-view data needs to be stored and transferred. However, noise can be easily generated during image capturing, and these noisy images severely deteriorate both the quality of experience and the compression efficiency. Therefore, denoising is a prerequisite to produce multi-view-based image contents. In this paper, the structural characteristics of linear multi-view images are fully utilized to increase the denoising speed and performance as well as to improve the compression efficiency. Assuming the sequential processes of denoising and compression, multi-view geometry-based denoising is performed keeping the temporal correlation among views. Experimental results show the proposed scheme significantly improves the compression efficiency of denoised views up to 76.05%, maintaining good denoising quality compared to the popular conventional denoise algorithms.
Findings of the Shared Task on Machine Translation in Dravidian languages

This paper presents an overview of the shared task on machine translation of Dravidian languages. We presented the shared task results at the EACL 2021 workshop on Speech and Language Technologies for Dravidian Languages. This paper describes the datasets used, the methodology used for the evaluation of participants, and the experiments’ overall results. As a part of this shared task, we organized four sub-tasks corresponding to machine translation of the following language pairs: English to Tamil, English to Malayalam, English to Telugu and Tamil to Telugu which are available at https://competitions.codalab.org/competitions/27650. We provided the participants with training and development datasets to perform experiments, and the results were evaluated on unseen test data. In total, 46 research groups participated in the shared task and 7 experimental runs were submitted for evaluation. We used BLEU scores for assessment of the translations.
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents

This paper proposes a new task regarding event reason extraction from document-level texts.Unlike the previous causality detection task, we do not assign target events in the text but only provide structural event descriptions, and such settings accord more with practice scenarios.Moreover, we annotate a large dataset FinReason for evaluation, which provides Reasons annotation for Financial events in company announcements.This task is challenging because the cases of multiple-events, multiple-reasons, and implicit-reasons are included.In total, FinReason contains 8,794 documents, 12,861 financial events and 11,006 reason spans.We also provide the performance of existing canonical methods in event extraction and machine reading comprehension on this task.The results show a 7 percentage point F1 score gap between the best model and human performance, and existing methods are far from resolving this problem.
Multivalent Graph Matching Problem Solved by Max-Min Ant System

This paper presents a multivalent graph matching problem and proposes a max-min ant system for its resolution. Multivalent graph matching is a very combinatorial problem where a node (edge) in one graph can match with more than one node (edge) in the other graph. We formalize this problem as an extended graph edit distance problem by adding possibilities of splitting and merging operations. Then, we employ an ant colony based optimization algorithm, the max-min ant system, to solve this very combinatorial problem. A local search is also integrated to enhance the solution quality. The efficiency of the proposed approach is verified on a symbol data set in several aspects. The results show that the proposed approach can be very useful in case of noise when the bijective graph matching-based approaches are not usually robust.
Personalized recommendation method of multimedia network assisted English teaching resources based on particle swarm optimization

In order to overcome the problem of low accuracy in traditional personalized recommendation of Multimedia Assisted English teaching resources, a personalized recommendation method of multimedia network assisted English teaching resources based on particle swarm optimization is proposed. According to the principle of personalized recommendation, this paper analyzes the relationship between students and multimedia network assisted English teaching resources, and constructs the personalized recommendation function of assisted English teaching resources. Using particle swarm optimization method, according to the process of particle swarm optimization, the personalized recommendation function of auxiliary English teaching resources is solved, and the effective personalized recommendation results are obtained. Experimental results show that, compared with the traditional personalized recommendation method, the function solving accuracy of the proposed method is lower, and the personalized recommendation result is more accurate.
Quality control (QC) of an institutional repository: a hands-on

Purpose The purpose of this paper is to show the quality control process and techniques of an open-source digital library (DL)/institutional repository (IR) in an institution. It will also demonstrate the challenges for quality control of a digital library in an institution. Design/methodology/approach It will explain metadata quality control and structural quality control and some matters that are important for an ideal IR. It also describes data migration from one repository to another. Findings Metadata without quality is just junk. Library personnel should have adequate library science educational qualifications to do the DL work. Non-professionals may create junk in the IR. Quality control can ensure the standard of a digital IR. Originality/value This study drowns upon the practical experience of DSpace quality control of a private university library in Bangladesh.
An Empirical Study on the Use of Student-Focused Approaches in the Software Engineering Teaching

The software industry is not satisfied with the preparation level of newly graduated professionals in Computing undergraduate courses.There is a predominance of traditional approaches to the Software Engineering (SE) teaching which proved to be inefficient, because they focus on the content from the professor´s viewpoint.This research aims to investigate if the use of studentfocused approaches in the SE teaching can develop more technical competencies to apply in industry than when traditional approaches are applied.For this, an iterative model has been defined to integrate the main student-focused approaches and a controlled experiment was carried out in four undergraduate courses.The data were collected from structured interviews with students and analyzed using ANOVA.The results showed no significant statistical difference between studentfocused and traditional teaching approaches in the development of SE competencies.However, these results were impacted by the motivation and commitment of the experiment students.
Detecting Automatically Managed Accounts in Online Social Networks: Graph Embeddings Approach

The widespread of Online Social Networks and the opportunity to commercialize popular accounts have attracted a large number of automated programs, known as artificial accounts. This paper focuses on the classification of human and fake accounts on the social network, by employing several graph neural networks, to efficiently encode attributes and network graph features of the account. Our work uses both network structure and attributes to distinguish human and artificial accounts and compares attributed and traditional graph embeddings. Separating complex, human-like artificial accounts into a standalone task demonstrates significant limitations of profile-based algorithms for bot detection and shows the efficiency of network structure-based methods for detecting sophisticated bot accounts. Experiments show that our approach can achieve competitive performance compared with existing state-of-the-art bot detection systems with only network-driven features. The source code of this paper is available at: http://github.com/karpovilia/botdetection.
Design of Face Recognition Access Entrance Guard System with Mask Based on Embedded Development

Abstract This paper investigates all kinds of access entrance guard control systems at home and abroad and analyzes their advantages and disadvantages. COVID-19 epidemic has a serious impact on life, travel must wear a mask, so that the access entrance guard control system can not carry out face recognition under the mask. In view of this kind of situation, an embedded face recognition access entrance guard control system with mask based on EAIDK-310 development board is designed. The system can complete face recognition and body temperature measurement without contact, and drive the motor to open the gate. After testing, the system is suitable for deployment in companies, communities, campuses and other small application scenarios. It is convenient to travel and can reduce the impact of the epidemic.
Open Access Publication Status of Educational Technology with The Bibliometrics Approach

The study aims to map the status of open access to scientific publications globally in the field of "educational technology."The study obtains data from Scopus using the document search service.Researchers analyze it using the bibliometric approach using Scopus analysis features and VOSviewer applications, namely document cited, publication growth, author, author network, country, type, institution affiliation, subject area, keyword network, and source.The study identifies keywords related to educational technology to search for and identify related articles from the Scopus database with 1,675 academic documents published from 1969 to 2019.From the study, we could find a trend towards an increasing number of open access research in "educational technology" every year globally.
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings

Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain. In this regard, the implicit assumption is that there exists (at least approximately) a ground-truth bijection such that a given input from either domain can be accurately reconstructed from successive application of the respective mappings. But in many applications no such bijection can be expected to exist and large reconstruction errors can compromise the success of cycle-consistent training. As one important instance of this limitation, we consider practically-relevant situations where there exists a many-to-one or surjective mapping between domains. To address this regime, we develop a conditional variational autoencoder (CVAE) approach that can be viewed as converting surjective mappings to implicit bijections whereby reconstruction errors in both directions can be minimized, and as a natural byproduct, realistic output diversity can be obtained in the one-to-many direction. As theoretical motivation, we analyze a simplified scenario whereby minima of the proposed CVAE-based energy function align with the recovery of ground-truth surjective mappings. On the empirical side, we consider a synthetic image dataset with known ground-truth, as well as a real-world application involving natural language generation from knowledge graphs and vice versa, a prototypical surjective case. For the latter, our CVAE pipeline can capture such many-to-one mappings during cycle training while promoting textural diversity for graph-to-text tasks. Our code is available at github.com/QipengGuo/CycleGT *A condensed version of this paper has been accepted to AISTATS 2021. This version contains additional content and updates.
Interpolating between Positive and Completely Positive Maps: A New Hierarchy of Entangled States

A new class of positive maps is introduced. It interpolates between positive and completely positive maps. It is shown that this class gives rise to a new characterization of entangled states. Additionally, it provides a refinement of the well-known classes of entangled states characterized in terms of the Schmidt number. The analysis is illustrated with examples of qubit maps.
An end-to-end text spotter with text relation networks

Abstract Reading text in images automatically has become an attractive research topic in computer vision. Specifically, end-to-end spotting of scene text has attracted significant research attention, and relatively ideal accuracy has been achieved on several datasets. However, most of the existing works overlooked the semantic connection between the scene text instances, and had limitations in situations such as occlusion, blurring, and unseen characters, which result in some semantic information lost in the text regions. The relevance between texts generally lies in the scene images. From the perspective of cognitive psychology, humans often combine the nearby easy-to-recognize texts to infer the unidentifiable text. In this paper, we propose a novel graph-based method for intermediate semantic features enhancement, called Text Relation Networks . Specifically, we model the co-occurrence relationship of scene texts as a graph. The nodes in the graph represent the text instances in a scene image, and the corresponding semantic features are defined as representations of the nodes. The relative positions between text instances are measured as the weights of edges in the established graph. Then, a convolution operation is performed on the graph to aggregate semantic information and enhance the intermediate features corresponding to text instances. We evaluate the proposed method through comprehensive experiments on several mainstream benchmarks, and get highly competitive results. For example, on the , our method surpasses the previous top works by 2.1% on the word spotting task.
Developing ESL Writing Content Through Online Discussions on WhatsApp

This study uses WhatsApp to facilitate online discussions in developing content idea for ESL writing. A quasi-experimental study was conducted with 33 pre-university students participating in online discussions on WhatsApp for five weeks to support face-to-face instructions. Pre-test, post-test, and five weekly writing tests were administered, examined, and analysed to identify the participants' writing performance. The findings show significant improvement in the experimental group's essay writing scores after using WhatsApp for online discussions. The participants were able to use the content generated from the MWEG in supporting the arguments in their essays. Besides content idea development, the experimental group saw the WhatsApp online discussions as boosting confidence, raising motivation, and encouraging interactions among students. The themes that emerged from the WhatsApp group interaction were lively discussions, sharing of opinions, and turn-taking. While the control group also improved in their test scores, it was not as significant as the experimental group.
SD‐SHO: Security‐dominated finite state machine state assignment technique with a satisfactory level of hardware optimization

Recently, the finite state machine (FSM)-based digital controllers are susceptible to fault-injection and side-channel attacks, which makes FSM security a more prominent factor. FSM optimality is another crucial element when designed. A state encoding approach is applied for FSM security and optimization. This article proposes the security-dominated FSM state assignment technique (SD-SHO), which obtains a satisfactory level of FSM optimization as well. It is a deterministic algorithm and consists of two key techniques, such as state assignment using an improved quadratic sum code, and state assignment using a gradient-based interior point method. A fuzzy bi-level programming logic is introduced in the proposed approach for regulating the constituting algorithms optimally. Experiments are conducted to evaluate the FSM security and optimality using the MCNC FSM benchmarks. Results indicate a substantial reduction in the error masking probability using SD-SHO. It also demonstrates that SD-SHO achieves a satisfactory level of area and power reduction compared with other existing works.
Edge detection with mixed noise based on maximum a posteriori approach

&lt;p style='text-indent:20px;'&gt;Edge detection is an important problem in image processing, especially for mixed noise. In this work, we propose a variational edge detection model with mixed noise by using Maximum A-Posteriori (MAP) approach. The novel model is formed with the regularization terms and the data fidelity terms that feature different mixed noise. Furthermore, we adopt the alternating direction method of multipliers (ADMM) to solve the proposed model. Numerical experiments on a variety of gray and color images demonstrate the efficiency of the proposed model.&lt;/p&gt;
Computer-Assisted Interactive Platform Design for Online Music Teaching Based on Cloud Computing

Combining multimedia speech recognition technology and harmony technology, one can realize automatic control and optimization in the process of music creation. This improves the quality and efficiency of music creation. This paper designs a computer-assisted interactive platform for music teaching and develops an interactive creation platform for computer-aided music teaching embedded within the Linux root file system. In the cloud computing environment, music teaching resource scheduling, information compilation and data storage design are carried out, and the music speech recognition algorithm is optimized. Considering that the tones include pitch and overtone components, the signal they contain has a wide spectralcharacteristic, so harmonic weighting will have a large peak at the fundamental frequency of each note. The research shows that the platform designed in this paper has a strong ability to detect and recognize the voice signal during music creation, and this improve the interactive creation quality of music teaching.Keywords: Deep Learning; Online Music Teaching;Signal Processing; Signal De-noisingCite AsH. Jiang, Computer-Assisted Interactive Platform Design for Online Music Teaching Based on Cloud Computing, Engineering Intelligent Systems, vol. 27 no. 1, pp. 47-54, 2019.