The purpose for this investigation is to examine the impact of income tax, bonus mechanism, motivating force burrowing to tunneling incentive. The sample choice utilizing purposive examining technique and examined 23 manufacturing companies recorded in Indonesian Stock Exchange (IDX). The samples are chosen by using purposive sampling method and the companies chosen are the among top 30 companies LQ 30 Index in Indonesia Stock Exchange. The information are obtained from optional information of yearly report of fundamental and manufacturing companies recorded in Indonesia Stock Exchange from 2012-2016. The investigation method utilized in this exploration is binary logistic regression analysis. The result of this investigation demonstrates that the income tax is significant and noteworthy to the transfer pricing, tunneling incentives powerful and critical to transfer pricing, while the bonus mechanism does not influence the transfer pricing.
People with disabilities (PWD) view the issue of employment as one of endless concern as they are continuously evaluated based on their disability per se. It is reported that that PWDs have to face challenges in finding jobs that suit their capability but they usually eventually quit from their job after struggling with the challenges that come with it. The increasing trend of a high employment turnover among disabled employees in Malaysia has spurred the direction in this study toward examining the concept of leader-member exchange (LMX) which examines the quality of supervisor-subordinate relationships. This study is important because it will bring new insights on how managers can integrate PWDs at the workplace by enhancing their social exchange relationship (dyadic), especially their leadership skills. It was discovered that all components of LMX namely affect, professional respect, contribution and loyalty are all important factors that ensure a good relationship between supervisors and PWDs. In addition, it was found that types of disabilities and PWD working sectors has no mean difference toward the LMX components that influence this dyadic relationship.
The study aims to examine the firm-specific factors such as firm size, profitability and asset tangibility in the capital structure decisions (leverage) on a sample of twenty construction firms in Malaysia and Singapore from 2009 to 2018, with 200 observations. The sample firms are chosen based on convenience sampling technique and the availability of the data. Prior studies documented inconclusive findings on the determinants of capital structure and different industries tend to reveal different patterns of relationship. In addition, the empirical evidence on comparative analysis between construction firms in Malaysia and Singapore is lacking. Hence, the objective of this study is to extend the prior work by investigating the impact of the determinants on capital structure on the construction firms in Malaysia and Singapore. The study uses panel data analysis to test the effectivity of trade-off, pecking order and agency cost theories of capital structure. The empirical findings reveal positive and significant association between firm size and capital structure for Singapore firms. Meanwhile, profitability and asset tangibility correlate negatively with capital structure. As for Malaysian firms, the three determinants exhibit insignificant association with the capital structure. The study only examines 10 construction firms in Malaysia and 10 construction firms in Singapore, therefore, the small sample size becomes the limitation of the study. Nevertheless, the findings of this study may contribute to the body of knowledge on the importance of some firm-specific determinants such as profitability, tangible assets, and firm size in order to determine the optimal level of capital structure for firms in these countries.
Covid-19 is an unprecedented crisis that has affected almost all industry players including education. It has transformed our way of life and introduced a new normal to how things are done. As an effort to contain the outbreak of pandemic Covid-19, universities have shifted to online learning. In line with this, Universiti Teknologi MARA (UiTM) has decided to execute open and distance learning (ODL) for the current semester until 31 December 2020. ODL introduces a different learning environment as compared to the traditional classroom that requires students to be self-reliant in learning new things. Hence, the purpose of the study is to explore students’ experiences in the process of knowledge transfer through ODL specifically for accounting subjects. A questionnaire was distributed to students who were taking the subject of Introduction to Financial Accounting and Introduction to Cost Accounting in UiTM Pahang Kampus Raub and a total of 206 responses were received. The study found over half of the students enjoy learning through ODL but only one-third were looking forward to having ODL for the next semester. Poor internet connection is the main reason found in the study that makes ODL not preferred by the students. At the same time, few features were highlighted by the students about ODL such as the advantage of pre-recorded video to catch up the new material and flexibility for them to learn at their own pace.
E-commerce has transcended the traditional way of shopping into a new and more efficient way of purchasing. Through this approach, businesses are conducted through the internet in which activities such as information searching, information sharing, products and services purchasing are performed. There are many reasons why online purchasing has become one of the most preferred channels for customers to purchase goods or services. This study is conducted to examine crucial factors related to customers' satisfaction in online shopping and specifically looking at four factors: information quality, service quality, security/privacy and website design and their relationship with customers' satisfaction in online purchasing. 320 questionnaires were distributed through purposive sampling techniques to students from a public university in Malaysia. The findings revealed that website design and information quality were the most impactful factors that influence customer’s online purchasing. These findings are useful for online retailers and marketers to understand consumers' behaviour when purchasing online, thus enabling them to develop more effective marketing strategies. For future research, this study suggests that the population of the study should be expanded and other types of variables should be included such as mediating and moderating variables to obtain more accurate and precise findings. It is also suggested that the research could be conducted using random samplings so that the findings can be generalised.
The famous financial scandal of Enron, WorldCom and 1 Malaysia Development Berhad has harmed the auditor’s reputation as the protector of shareholders’ rights. Auditors have done their part by conducting systematic audit procedures and “What Could Go Wrong” analysis in assessing the possible risk area to assist fraud detection in the client’s financial matters. However, fraud cases never seem to decline. Regardless of any safeguarding measures established, fraud incidents can just occur and be worsened by economic downturn and prolonged inflation especially after the pandemic ends. Additionally, the characteristic of the modern business environment, technology sophistication and new generation traits had challenged Cressey Fraud Triangle Theory on its validity and relevancy. Therefore, associating all these possible challenges into consideration, this study aims to review prior literature related to the evolution of Cressey fraud theory to propose a new insight in considering relevant motivation factors that drive fraud penetrations. From the review, the study discovered the need for a detailed evaluation and research on the essential fraud element in constructing an all-rounded fraud prevention mechanism.
Teaching and learning processes can be effectively improved when the learning activities encounter an individual’s experience and interaction with their surrounding environments. Such interaction is highly significant throughout the learning process due to its role in enabling student participation and learning knowledge integration. This paper presents the option of teaching pedagogy for statistics teaching via project-based learning. This teaching method conduces cognitive development during the learning process by fostering active learning as it implements the application as opposed to the memorisation of statistical concepts. Therefore, a project was carried out with underpinned a common topic by all groups of students. The approach was incorporated in an undergraduate Statistical Methods class consisted of 34 business economics students in which they were to evaluate the learning process at the end of the course. Data collection consisted of closed-form and open-ended questions directed to the students for them to express their perceptions of project-based learning. The results showed that throughout the phases of project-based learning implementation, the students found that the processes of data presentation and analysis and research result discussion were challenging phases. They revealed the need for adjustment while carrying out a project, which was in line with the insight regarding the benefits and limitations faced by population. In particular, the aspect of limitations included the complexity of group work relationships and accessibility of resources applied in the project, while the benefit aspects are an increased level of student engagement in the learning process and their understanding of statistical concepts.
Insight Journal Vol. 7 DOI: 10.24191/ij.v7i1.58 This research study is endeavoured to discover the factors that will lead the consumers’ staying intention at green hotels, especially of those who practise the Shariah Law (Islamic green hotels). Nowadays, there has been an increase in public concern regarding environmental issues. Consumers are more environmentally aware than they were in past decades. Hotels are among the largest contributors of energy consumers in the tertiary building sector, which contribute to some negative impacts to the earth at the same time. The most obvious negative impact of hotels on environment are solid waste generation and disposal. Therefore, numerous consumers are supportive of green consumption and consider it as a successful method to protect the environment. In Islamic green hotels, Muslim-friendly amenities have also been provided to protect Muslims travellers’ welfare that is by providing them a comfortable prayer room, the Holy Quran and Islamic practices booklets, prayer mats and a direction of Qibla. A set of questionnaires was distributed to 256 respondents among the community and tourists in Kuching, Sarawak. The dependent variable in this study was consumers’ staying intention while the independent variables were green image, green satisfaction, Muslim amenities and lifestyle and price fairness. This research study also made use of the Statistical Package for Social Science (SPSS) software to analyse the result based on the questionnaires distributed to the respondents. Based on the result of analysis, it is shown that the green image and green satisfaction were found of having a positive significant that influence the consumers’ staying intention at the Islamic green hotels. Besides that, Muslim amenities and lifestyle was also found to have a positive significant impact that influences the consumers’ staying intention. Most Muslims were likely to choose a destination with Islamic practice to fulfil their daily duties. However, green price fairness did not significantly influence the consumers’ staying intention. This is because if green image, green satisfaction, Muslim amenities, and lifestyle meet the requirements of the Muslim tourists, they would not be affected by the price. As a matter of fact, price may be the least factor of consideration by the tourists in choosing a hotel to stay.
Conventional tourism had opened a “window” for Islamic tourism to operationalize which at present is expanded throughout the world. As the Muslim population is rising rapidly, Muslim consumer market should be critically concerned by tourism businesses to satisfy the needs and wants of the consumers. This paper attempts to explain the concept of Islamic tourism in the context of maqasid shariah – protection of religion, protection of intellect, protection of life, protection of wealth and protection of offspring; discusses the role of Islamic religiosity in shaping tourists’ behaviour, as well as tourism industry’s role in applying Islamic tourism according to maqasid shariah, from the tourism industry’s perspective and tourists’ perspective. To date, there is still lack of knowledge and related literature review on the implementation of maqasid shariah in the concept of tourism industry. The ultimate goals of shariah are vital as the platform in the development of Islamic tourism as well as Islamic religiosity among Muslim consumers because it resembles the value of Islamic concept in tourism perspectives.
In this paper, we describe a set of filters, implemented in the Insight Toolkit www.itk.org, for converting an image from Cartesian co-ordinate space to Polar co-ordinate space and vice-versa. Cartesian to Polar conversion of an image is a useful operation in preprocessing stage of certain image-processing algorithm where feature of interest has simplified representation in the polar space. This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.
Recently, the scientific community has been proposing several automatic algorithms to biomedical image segmentation procedure, being an interesting and helpful approach to assist both technicians and radiologists in this time-consuming and subjective task. One of these interesting and widely used image segmentation method could be the voxel intensity-based algorithms, e.g. image histogram threshold methods, which have been intensively improved in the past decades. Recently, an interesting approach that gained focus is the logistic classification (LC) for object detection in biomedical images. Even though the general concept behind the LC method is fairly known, the proper method’s optimization still commonly adjusted by hand which naturally adds a level of uncertainty and subjectivity in the general segmentation performance. Therefore, an empirical LC optimization is presented, offering a ITK class that performs the LC parameters optimization based on empirical input data analysis. It is worth mentioning that the LogisticContrastEnhancementImageFilter class showed here is also applied on others computational problems, being briefly explained in this document.
This document describes an ITK class implementing an Adaptive Moment Estimator (Adam) optimizer algorithm within the Insight Toolkit ITK www.itk.org. Adam is an adaptive gradient descent optimizer, which independently adaptively estimates the gradient descent step for each parameter, at each iteration, based on stored past gradients. The optimizer stores exponentially decaying averages of past gradients to estimate first moment (the mean) and the second moment (the variance) of the gradients to formulate update rule for present iteration. The Adam optimizer compares favorably to other adaptive learning-method algorithms, converges faster, and is robust to saddle point. This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the algorithm described in this paper.
Superpixel algorithms have proven to be a useful initial step for segmentation and subsequent processing of images, reducing computational complexity by replacing the use of expensive per-pixel primitives with a higher-level abstraction, superpixels. They have been successfully applied both in the context of traditional image analysis and deep learning based approaches. In this work, we present a general- ized implementation of the simple linear iterative clustering (SLIC) superpixel algorithm that has been generalized for n-dimensional scalar and multi-channel images. Additionally, the standard iterative im- plementation is replaced by a parallel, multi-threaded one. We describe the implementation details and analyze its scalability using a strong scaling formulation. Quantitative evaluation is performed using a 3D image, the Visible Human cryosection dataset, and a 2D image from the same dataset. Results show good scalability with runtime gains even when using a large number of threads that exceeds the physical number of available cores (hyperthreading).
This document describes a new remote module implemented for the Insight Toolkit ITK, itkTextureFeatures. This module contains two texture analysis filters that are used to compute feature maps of N-Dimensional images using two well-known texture analysis methods. The two filters contained in this module are itkScalarImageToTextureFeaturesImageFilter (which computes textural features based on intensity-based co-occurrence matrices in the image) and itkScalarImageToRunLengthFeaturesImageFilter (which computes textural features based on equally valued intensity clusters of different sizes or run lengths in the image). The output of this module is a vector image of the same size than the input that contains a multidimensional vector in each pixel/voxel. Filters can be configured based in the locality of the textural features (neighborhood size), offset directions for co-ocurrence and run length computation, the number of bins for the intensity histograms, the intensity range or the range of run lengths. This paper is accompanied with the source code, input data, parameters and output data that we have used for validating the algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.
In an earlier Insight Journal article, we introduced an ITK implementation of the adaptive patch-based image denoising algorithm described in [3]. We follow-up up that offering with a generalized non-local, patch-based ITK class framework and a refactored denoising class. In addition, we provide two ITK implementations of related, well-known algorithms. The first is a non-local super resolution method described in [1, 2]. The second is the multivariate joint label fusion algorithm of [4, 5] with additional extensions, denoted as “joint intensity fusion”, which will be described in a forthcoming manuscript. Accompanying these ITK classes are documented programming interfaces which use our previously introduced unique command line interface routines. Several 2-D examples on brain imaging data are provided to qualitatively demonstrate performance.
Strain quantifies local deformation of a solid body. In medical imaging, strain reflects how tissue deforms under load. Or, it can quantify growth or atrophy of tissue, such as the growth of a tumor. Additionally, strain from the transformation that results from image-to-image registration can be applied as an input to a biomechanical constitutive model.This document describes N-dimensional computation of strain tensor images in the Insight Toolkit (ITK), www.itk.org. Two filters are described. The first filter computes a strain tensor image from a displacement field image. The second filter computes a strain tensor image from a general spatial transform. In both cases, infinitesimal, Green-Lagrangian, or Eulerian-Almansi strain can be generated.This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.
The Insight Toolkit (ITK) utilizes a generic design for image processing filters that allows many developers to rapidly implement new algorithms. While ITK filters benefit from a platform-independent and versatile multithreading capability, the current implementation does not easily achieve high performance. First, ITK relies on a static decomposition of the image into subsets of equal size which is highly inefficient when the computational complexity varies between subsets (unbalanced workloads). Second, the current domain decomposition is limited to subdivide the input domain along a single dimension (typically the slice dimension in a 3-D volume), which causes a multithreading under-utilization when the number of threads is larger than the size of this dimension when using massively parallel compute systems. We previously presented a new itk::TBBImageToImageFilter class that replaced the static task decomposition by a dynamic task decomposition for improved workload balancing, in which the job scheduling task was optimized using the Intel® Threading Building Blocks (TBB) library. In this work, we propose a new multidimensional dynamic image decomposition approach that allows decomposition over an arbitrary number of dimensions. This new generic multithreading capability, combined with the TBB dynamic task scheduler, substantially improves multithreading performance when using massively parallel processors.
This document describes a new remote module implemented for the Insight Toolkit (ITK), itkBoneMorphometry. This module contains bone analysis filters that compute features from N-dimensional images that represent the internal architecture of bone. The computation of the bone morphometry features in this module is based on well known methods. The two filters contained in this module are itkBoneMorphometryFeaturesFilter. which computes a set of features that describe the whole input image in the form of a feature vector, and itkBoneMorphometryFeaturesImageFilter, which computes an N-D feature map that locally describes the input image (i.e. for every voxel). itkBoneMorphometryFeaturesImageFilter can be configured based in the locality of the desired morphometry features by specifying the neighborhood size. This paper is accompanied by the source code, the input data, the choice of parameters and the output data that we have used for validating the algorithms described. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.
The anisotropic diffusion algorithm has been intensively studied in the past decades, which could be considered as a very efficient image denoising procedure in many biomedical applications. Several authors contributed many clever solutions for diffusion parameters fitting in specific imaging modalities. Furthermore, besides improvements regarding the image denoising quality, one important variable that must be carefully set is the conductance, which regulates the structural edges preservation among the objects presented in the image. The conductance value is strongly dependent on image noise level and an appropriate parameter setting is, usually, difficult to find for different images databases and modalities. Fortunately, thanks to many efforts from the scientific community, a few automatic methods have been proposed in order to set the conductance value automatically. Here, it is presented an ITK class which offers a simple collection of the most common automatic conductance setting approaches in order to assist researchers in image denoising procedures using anisotropic-based filtering methods (such as well described in the AnisotropicDiffusionFunction class).
