Reviewerly/exHarmony huggingface dataset:
The Effect of CEO Turnover on the Auditor’s Audit Effort

본 연구는 최고경영자의 교체가 감사인의 감사노력에 어떠한 영향을 미치는지를 분석하여, 최고경영자의 교체로 인해 발생하는 정보비대칭과 불확실성에 대하여 감사인이 추가적인 감사노력을 하는지 분석하였다. 먼저 최고경영자(CEO) 교체와 감사노력 사이의 관계를 분석하였다. 또한 감사위원회 도입여부에 따라 최고경영자(CEO) 교체와 감사노력 사이의 관계에 어떠한 차이가 있는 지 검증하였다. 2009년부터 2017년까지의 상장기업을 대상으로 하여 5,701개 기업-년 자료를 분석한 결과는 다음과 같다. 첫째, 최고경영자(CEO) 교체가 감사시간과 감사보수로 측정한 감사인의 감사노력에 유의적인 양(+)의 영향을 미치는 것으로 나타났다. 이러한 결과는 감사인이 감사업무를 수행할 때, 피 감사기업의 최고경영자(CEO) 교체에 따른 피 감사기업의 이익조정의 변동성 확대 등 전반적인 감사위험의 증가를 감안하여 감사위험을 낮추기 위해 추가적인 감사노력을 투입하고 있음을 나타낸다. 둘째, 감사위원회가 존재하는 경우, 최고경영자(CEO)교체와 감사인의 감사노력 증가와의 관계가 더욱 커지는 것으로 나타났다. 이러한 결과는 재무제표의 적정한 작성을 감독하는 기능을 수행하는 감사위원회가 해당 기업의 최고경영자(CEO) 교체로 인해 감사위험이 높아질 경우 외부감사인로 하여금 감사노력을 강화하도록 하여 감사품질을 더욱 높이는 보완관계의 역할을 수행한다는 실증적 근거를 제시한다는 점에서 의의를 갖는다.This study examined how the CEO turnover affects the auditor s audit efforts and analyzes whether the auditor makes additional audit efforts for audit risk caused by the CEO turnover. We analyzed the relationship between CEO turnover and audit efforts. We also tested how audit committee affects the relationship between turnover and audit efforts. Data consists of 5,701 firm-years listed in KOSPI from 2009 to 2017. Our empirical findings are as such. First, we found that CEO turnover has a significant positive effect on auditor s audit effort measured by audit hour and audit fees. This result indicates that auditors are making additional audit efforts to reduce the audit risk resulting from CEO change. Second, we discovered that when an audit committee exists, the relationship between the CEO turnover and the auditor s increased audit efforts becomes greater. This finding is meaningful in that internal monitering works as a complementary instrument to external audit in that audit committee requests more audit efforts when firm risk increases due to CEO change.
Secure outsourcing of modular inverses and scalar multiplications on elliptic curves

Currently, pricey data analysis urges outsourcing computations, such as modular inverse and point multiplication in elliptic curve cryptosystem. However, it raises security concerns on the untrusted cloud. Thus, we propose two outsourcing protocols for these two operations, respectively. For efficiency, in the first protocol, only three modular multiplications required by the client to outsource a modular inverse operation. In the second protocol, a point multiplication operation can be carried out just by doing two addition operations on elliptic curves. For security, in both protocols, the input is randomly split into two parts to meet both input and output privacy. The security is theoretically proved under a single server and two non-colluding servers' models, respectively. Furthermore, the cheating behaviours of the servers can be detected by probabilities of 100% and 75%, respectively. Besides the achieved security goal, experiments confirm the significant improvement on efficiency in comparison with the traditional ones.
A RESEARCH REVIEW ON CHILDREN'S ENGLISH TEACHING IN CHINA (1999-2018)

Since 2001, English classes have been gradually set up in primary schools across China, so more and more children are beginning to learn English and the study of children's English teaching has also received increasing attention from scholars. In order to improve the children's English teaching in China in the past 20 years, this study focuses on sorting out its trends and hotspots in this field. Based on the literature retrieved on CNKI, this study discusses the research trends and hotspots of children's English teaching in China, taking 56 papers on children's English teaching included in core journals from 1999 to 2018 as samples. It is found that the trend of research on children's English teaching in China presents a wavelike rise. In addition, its content mainly includes children's cognitive characteristics, the application of multimedia technology and children's phonological awareness. To sum up, this paper summarizes the trends and hotspots of children's English teaching in recent years, hoping to provide reference for future research in this filed and highlight the teaching methods of Children’s English.
Digital Image Slicing and Synthesis using DCT Diagonal Filter Bank

According to Fourier theory signals of many type, are decomposable into their frequency components. This work demonstrates the decomposition of a digital image into its frequency spectrum and its synthetization process from its components to its spatial form. For this purpose, the image as a 2D signal will be transformed into its spectrum using Discrete cosine transform (DCT), decomposed into frequency components using automatically generated diagonal filter bank (DFB) and transformed back to its spatial form using inverse DCT. A running sum of spatial components will provide synthesis of image. Compare to other complex frequency processing analysis and synthesis techniques, the procedure is simple and provides best results. The decomposed components can be used effectively for noise removal as well as image compression at very low computational cost. This system is useful to analyze images for variety of applications including electronic signal communication and image processing for data compression, noise removal, and image analysis for pattern recognition. In neuroscience human visual system response is analyzed in response to Fourier sinusoidal gratings.
Application of satellite remote sensing methods in mineral prospecting in Kosovo, area of Selac

application of satellite remote sensing methods in mineral prospecting in kosovo, area of selac introduction Satellite remote sensing techniques have been used for environmental research for over 45 years.Moreover, geological remote sensing has become a separate scientific subdivision that has found its application in many research problems.The information that can be obtained with the use of remote sensing techniques varies according to the sensors.The most commonly used remote sensing techniques are: airborne laser scanning, multispectral and hyperspectral optical scanners (Cloutis
User content categorisation model, a generic model that combines text mining and semantic models

Social networking websites are growing not only regarding the number of users but also in terms of the user-generated content. These data represent a valuable source of information for several applications, which require the meaning of that content associated with the personal data. However, the current structure of social networks does not allow extracting in a fast and straightforward way the hidden information sought by these applications. Major efforts have emerged from the semantic web community addressing this problem trying to represent the user as accurately as possible. They are not unable to give a sense to the user-generated content. For this, more sense-making needs to be done on the content, to enrich the user profile. In this paper, we introduce a generic model called user content categorisation (UCC). It incorporates the text mining approach into a semantic model to enrich the user profile by including information on user's posts classifications.
Performance analysis of nonlinear activation function in convolution neural network for image classification

Deep learning architectures which are exceptionally deep have exhibited to be incredibly powerful models for image processing. As the architectures become deep, it introduces challenges and difficulties in the training process such as overfitting, computational cost, and exploding/vanishing gradients and degradation. A new state-of-the-art densely connected architecture, called DenseNets, has exhibited an exceptionally outstanding result for image classification. However, it still computationally costly to train DenseNets. The choice of the activation function is also an important aspect in training of deep learning networks because it has a considerable impact on the training and performance of a network model. Therefore, an empirical analysis of some of the nonlinear activation functions used in deep learning is done for image classification. The activation functions evaluated include ReLU, Leaky ReLU, ELU, SELU and an ensemble of SELU and ELU. Publicly available datasets Cifar-10, SVHN, and PlantVillage are used for evaluation.
DEEP LEARNING FOR HISTORICAL DOCUMENT ANALYSIS

This chapter gives an overview of the state of the art and recent methods in the area of historical document analysis. Historical documents differ from the ordinary documents due to the presence of ...
LibGuides: Zoom: Zoom for Faculty/Staff

The basics of using Zoom videoconferencing software. Creating safe and engaging learning environments in Zoom
Protocol State Based Active Probing Method for Detecting Applications of Proprietary Protocol

Network traffic classification and unknown network application identification have become new challenges for network regulators and traffic inspection organizations. In this paper, in order to identify applications of proprietary protocol from the complex network environment, an active probing method based on protocol state is proposed in this paper. Based on the analysis of traffic characteristic and instruction trajectory of the application of proprietary protocol, this paper makes an in-depth study on the encryption and version features of the proprietary application by actively sending probing packets with special formats. The proprietary application studied in this paper is built in the VPS. Network packet analysis tools are used to extract traffic characteristics. We design the probe according to the characteristics. The experimental results show that the proposed active probing method based on protocol state is successful in detecting the application of proprietary protocol.
Performance of Pre-Learned Convolution Neural Networks Applied to Recognition of Overlapping Digits

We analyzed the performance of pre-learned convolution neural networks (CNN) learned with a single-digit image dataset when they were used to recognize images containing two overlapping digits. The pre-learned network was learned using the MNIST database, and the network architecture was the LeCun network. The overlapping digit images were made using images from the MNIST database. Our goal was to clarify two issues: (1) can a network learned for recognition of single digits in an image classify an image that includes two overlapping digits without doing additional learning using a dataset composed of two overlapping digit images? (2) Is a convolutional neural network (CNN) capable of processing stereoscopic vision? If (1) is possible with a CNN, then we don't need to train a huge number of images that include various combinations of more than two digits. If (2) is possible, stereoscopic vision is also possible without having to learn overlapping images. Our results support the conclusion that stereoscopic vision or a similar function is involved in the CNN.
Sensing the insensible using optical schemes: converting the maze problem into a quantum search problem

Many large image processing and data processing scenarios can soon develop into maze problems requiring, say, finding the longest possible path, which are unresponsive, intractable, and challenging to analyze! Such maze problems, of which the traveling salesman decision problem is a special case, are of the Non-determinate Polynomial (NPcomplete) class of problems that are often impossible to solve with finite time and storage. We propose a novel methodology to approach this class of NP-complete problems. We convert a suitably formulated maze problem into a Quantum Search Problem (QSP), and the desired solutions are then sought using the iterative Grover’s Search Algorithm. Thus, we reformulate the entire class of such NP-problems into QSPs. Our current solution deals with two-dimensional perfect mazes with no closed loops. We encode all possible individual paths from the starting point of the maze into a quantum register. A quantum fitness operator applied to the register encodes each qubit with its fitness value. We propose the design of an optical oracle that marks all entities above a certain fitness value and uses the Grover search algorithm to find the optimal marked state in an iterative manner.
Intelligent Urban Traffic Orbit Current Limiting Model Based on Improved Ant Colony Algorithm

With the development of the economy and society and the accelerating pace of life, people have higher travel requirements. Compared with the traditional urban transportation ways, the subway has the advantages of larger passenger capacity and higher efficiency. Besides, the subway can also alleviating traffic congestion on the ground and reducing environmental pollution. Therefore, the development of underground urban rail transit has become a major trend in the world. However, some problems need to be solved, such as passengers’ travel routes choosing and the government's guidance on peak traffic flow, due to the large correlation between rail transit lines, the sharp increase in traffic volume during peak periods, and the major differences in passenger flow between the various sections. Taking Beijing urban rail as an example, this paper analyzed the travel data of each station in some time periods, established the corresponding optimization model, and gave the measures to optimize the travel route and implement the site current limit, which has practical significance for solving urban rail transit problems.
Towards cognitive vehicles: location cognizance

In the context of cognitive vehicles, it is essential to have full awareness of the vehicle location to have a better insight of the operational environment and enhance the driver perception. Although the global navigation satellite system (GNSS) is commonly a practical solution for vehicle localization, it may suffer quality deterioration, accuracy decay and even track loss due to signal blockage and reflection off buildings and big structures. These limitations usually manifest in big cities with dense traffic and active roads which means losing the sense of location, even for a short time, might blur the cognitive system decision making process and jeopardize the safe driving of the vehicle. Consequently, cognitive vehicles should not count only on the GNSS solution for vehicle localization. In this work we establish that the cognitive vehicle location awareness can be achieved through the inner process of interaction with the surrounding environment and observing its static reference elements. This approach is inspired by the way the human brain can assess its position in a known environment by recognizing some landmarks and referential objects. Our proposed solution allows the cognitive vehicle to ascertain its location by interacting with its surroundings. we train a deep neural network to detect some objects of reference, create a prior knowledge of the vehicle environment and estimate the vehicle location by recognizing the objects detection pattern. Finally, the proposed solution will be endorsed by promising results from a real-world scenario, and further work will be proposed to improve the solution.
Assessing understanding of structural relationships: early lessons in a large scale virtual reality deployment

The study and practice of medicine requires a comprehensive understanding of interactions and an accurate perception of spatial relationships within the body. Existing diagnostic, classroom, and patient education materials are limited by 2‐dimensional representations of these 3‐ dimensional associations. Virtual reality is an emerging technology that creates a more lifelike and intuitive learning experience by allowing users to interact with 3D data. This technology can be used to advance the efficiency of understanding the 3‐dimensional associations that constitute the human body. BananaVision is a networked, multiuser virtual reality tool that renders human anatomical structures for the creation of instructional content. It allows users to quickly explore structural relationships as well as identify and dissect in a virtual environment. Here we describe a large‐scale virtual reality deployment and examine its efficacy as a tool to engage students and aid in preparation for a human dissection course. Human gross anatomy dissection students were randomly assigned to three groups. Each group was provided different study tools; an anatomy atlas, autonomous use of BananaVision virtual reality, or autonomous use of BananaVision virtual reality along with a pre‐recorded instructional module. After the study period, students were required to complete an assessment of their knowledge of structural relationships. Students found the BananaVision to be an engaging and useful tool. Additionally, the study group given access to BananaVision along with the module were more efficient in their preparation. However, performance on assessments was similar between groups. As virtual reality technology is implemented in education, the field is in the early stages of evaluating meaningful processes and procedures that demonstrate measurable outcomes. This study begins to address some of the early challenges when integrating a virtual reality deployment in anatomy curriculum. As demands on gross anatomy classrooms continue to increase, it is important to identify technologies and strategies that improve efficiency.
Evaluation of Predictive Capabilities of Similarity Metrics in Machine Learning

This paper explores prediction capabilities of similarity metrics used in machine learning algorithms. Predictive capabilities of various similarity metrics are examined based on their application on data sets of varying sizes and properties and evaluation of derived results. Predicting outcomes in machine learning is fundamental to many different machine learning algorithms and the findings in this paper will clarify how good their predictive capabilities are and under which conditions.
NSF FAIR Chemical Data Publishing Guidelines Workshop on Chemical Structures and Spectra: Major Outcomes and Outlooks for the Chemistry Community

The National Science Foundation Office of Advanced Cyberinfrastructure (NSF-OAC) funded a workshop in March 2019 focused on advancing the sharing of machine-readable chemical structures and spectra. Around 40 stakeholders from the chemistry, chemical information, and software communities took part in the two-day workshop entitled “FAIR Chemical Data Publishing Guidelines for Chemical Structures and Spectra.” Major topics discussed included publishing data workflows and guidelines, FAIR criteria/metadata profiles, value propositions, a publisher implementation pilot, and community support and engagement. This report summarizes the workshop conversations, major outcomes, and target areas for further activities. Primary outcomes from the workshop include identification of key metadata elements for sharing machine-readable structures and spectra, a sample of concise author guidelines, and a publisher proposal to accept enhanced supporting information files including these data types and associated metadata alongside articles. Selected target areas for further activities include the creation of author file and metadata packaging tools to facilitate easy compilation of data, and increased training for stakeholders specifically in the generation and handling of machine-readable file formats. We conclude this report with our outlooks and highlight several related community efforts initiated after the workshop.
Online Databases as a Tool for Research: A Study among Science Research Scholars at the University of Kerala

The sources of information available via the internet are increasing exponentially, leading to a steady increase in the use of the Internet for education and research. For the past few years, free online information sources like e-journals, e-books, e-databases have increased considerably. The present study analysed the use of online databases as a tool for research among the Science research scholars at the University of Kerala. A questionnaire study was carried out to analyse the study. The result of the study shows that the majority of the research scholars (45.16%) were moderately aware of online databases. Also, the majority of the research scholars (64.52%) used online databases daily. The study further revealed that most of the research scholars (52.26%) used an online database because of its up-to-date information and the majority of the researchers (57.42%) used advanced search methods and a fewer respondents (4.52%) used expert search method. A greater part of the respondents (64.52%) opined that online databases are very influential for research and no one stated that it is not at all influential. The study concludes with recommendations to ensure the effective and efficient use of the databases.
Прогнозирование на основе мультипликативной и множественной регрессионной модели сезонности

The article presents a method of statistical study of the dynamics and seasonality of the economic phenomenon with the use of spreadsheets.
Research on an Irregular Pupil Positioning Method

The traditional pupil positioning method is carried out under the assumption that the pupil boundary is a regular circle, and thus the rich texture features around the pupil boundary are eliminated, which directly affects the iris recognition accuracy. To solve this problem, this paper proposes an irregular pupil positioning method, finds the pupil boundary by preprocessing, then uses the least-squares method to accurately fit the pupil boundary. Finally, the algorithm is verified and analyzed by the CASIA database, and the traditional Hough transform method is used as a comparative experiment. The experimental results show that the proposed algorithm is not only suitable for irregular pupil boundaries (including noncircular and concave and convex areas), but also better in positioning time and positioning accuracy.
FUNGSI SISTEM INFORMASI AKUNTANSI DALAM MENINGKATKAN EFISIENSI DAN EFEKTIVITAS PELAPORAN KEUANGAN PADA BADAN USAHA MILIK DAERAH PROVINSI SUMATERA UTARA

Information systems for management facilitate their tasks effectively and efficiently. Effective means a measure of the level of use of resources in a process. The less use of resources, the more efficient the process. Efficient processes are characterized by process improvements, making them cheaper and faster. Whereas efficient means a measure of the level of fulfillment of output or process objectives.The higher the achievement of targets or process objectives, the process is said to be more effective. An effective process is characterized by process improvement, so that it becomes better and safer. Accounting information systems can collect, classify, process, analyze and communicate financial information, thereby helping companies launch business activities therein.
Corpora for Linguists vs. Corpora for Learners Bridging the Gap in Italian L2 Learning and Teaching

This paper aims to shed light on how research findings stemming from Learner Corpus Research (LCR) can inform the development of Data-driven learning (DDL) pedagogical activities. By doing this, it seeks to show how the gap between corpora built to be used by linguists and those tailored for learners can be filled. It starts by defining what a corpus is and how second language learning studies can benefit from the research findings based on corpora, but also from the direct use of corpora in the classroom. Then, it provides an overview of the available native and learner corpora of Italian, and how corpora in general can be adapted for DDL purposes. Finally, it describes an example of how an LCR finding can be used to develop DDL activities. It concludes with some desiderata for the future.