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The purpose of this study is to explore the integration of Shariah compliance in the information system of Islamic financial institutions (IFIs) in the context of Malaysia.

By applying qualitative approach in the form of in-depth/structured interview of qualified respondents within Islamic financial industries.

The result of this study indicates that information system advancement will give an increasing level of competitive advantages. Also, the result indicates that the internal control and information system played a vital role in ensuring the Shariah compliance and translating and circulating the Shariah guidelines among the IFIs’ departments and staffs. In terms of Shariah integration in information system, there is a consideration during the development of an information system. Shariah will be an element that needs to be accounted for to develop the information system for IFI.

This includes the scope of the study which is based on Malaysian Islamic banks only. Hence, future studies are recommended to extend this endeavor to other contexts as well. Furthermore, although the initial sample was covering nine IFIs, only two IFIs accepted to participate in the interview. It is suggested that the future studies involve more participants and apply different research techniques such as focus groups or questionnaire survey.

Make sure employees who are in charge of performing any function related to Shariah (i.e. Shariah review, Shariah audit, Shariah research, Shariah risk management) have a basic knowledge on information technology (IT) and information system. Continuous trainings for IFIs’ employees covering the information system and internal control system issues related to the Shariah compliance. Focus on seminars and conferences on outstanding issues related to information system technology in IFIs. Promoting programs and subjects specialized in information system technology in IFIs. IFIs should allocate a budget for system development or enhancement in the financial budget ensuring that IT system is incorporated in Shariah compliance. IFIs should consider enhancement of Shariah compliance encompass and the alignment into the IT system as continuous process, as well as one of their strategic plan aspects. Bank Negara Malaysia as a regulatory body of IFIs should emphasize on regulating the Shariah aspects with regard to the IT system.

This paper’s contributions lies in the enhancement of the development of the Shariah compliance literature, as well in the integration of Shariah compliance and information system in IFIs.

Most of the conventional humanoid modeling approaches are not successful in coupling different branches of the tree-type humanoid robot. In this paper, a tree-type upper body humanoid robot with mobile base is modeled. The main purpose of this work is to model a non holonomic mobile platform and to develop a hybrid algorithm for avoiding dynamic obstacles. Decoupled Natural Orthogonal Complement methodology effectively combines different branches of the humanoid body during dynamic analysis. Collision avoidance also plays an important role along with modeling methods for successful operation of the upper body wheeled humanoid robot during real-time operations. The majority of path planning algorithms is facing problems in avoiding dynamic obstacles during real-time operations. Hence, a multi-fusion approach using a hybrid algorithm for avoiding dynamic obstacles in real time is introduced.

The kinematic and dynamic modeling of a humanoid robot with mobile platform is done using screw theory approach and Newton–Euler formulations, respectively. Dynamic obstacle avoidance using a novel hybrid algorithm is carried out and implemented in real time. D star lite and a geometric-based hybrid algorithms are combined to generate the optimized path for avoiding the dynamic obstacles. A weighting factor is added to the D star lite variant to optimize the basic version of D star lite algorithm. Lazy probabilistic road map (PRM) technique is used for creating nodes in configuration space. The dynamic obstacle avoidance is experimentally validated to achieve the optimum path.

The path obtained using the hybrid algorithm for avoiding dynamic obstacles is optimum. Path length, computational time, number of expanded nodes are analysed for determining the optimality of the path. The weighting function introduced along with the D star lite algorithm decreases computational time by decreasing the number of expanding nodes during path generation. Lazy evaluation technique followed in Lazy PRM algorithm reduces computational time for generating nodes and local paths.

Modeling of a tree-type humanoid robot along with the mobile platform is combinedly developed for the determination of the kinematic and dynamic equations. This paper also aims to develop a novel hybrid algorithm for avoiding collision with dynamic obstacles with minimal computational effort in real-time operations.

The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability.

Pattern structures allow one to approach the knowledge extraction problem in case of partially ordered descriptions. They provide a way to apply techniques based on closed descriptions to non-binary data. To provide scalability of the approach, the author introduced a lazy (query-based) classification algorithm.

The experiments support the hypothesis that closure-based classification and regression allow one to both achieve higher accuracy in scoring models as compared to results obtained with classical banking models and retain interpretability of model results, whereas black-box methods grant better accuracy for the cost of losing interpretability.

This is an original research showing the advantage of closure-based classification and regression models in the banking sphere.

The purpose of this chapter is to present a model to support a humanizing approach to international education that is sustainable and facilitates respectful service, scholarship and teaching. University faculty engage internationally through teaching, service and scholarship. All three require faculty to possess strong trusting connections to the international community in which they operate. Literature suggests that the impulse to initiate service learning and scholarship with communities foreign to the faculty too early can be detrimental. A deep relationship between the faculty and community built upon trust and mutual respect is the key to successful internationally situated service, teaching and scholarship. However, such relationships require time to develop and many universities cannot support faculty toward developing international relationships. The Deep Field School presents a way of blending teaching, service and scholarship in a way that supports the nurturing of long-term relationships. The Deep Field School is an internationally situated short-term faculty led study abroad that operates with a commitment toward the long term and closely adheres to the humanizing principle that learning is a process not an outcome. A deep field school operating in Peru is presented as a case study along with guidelines for development.

Online learning has come to stay in a technologically advancing world with increasing populations. The search for ways to make online learning more efficient and effective in some developing countries continues as the accompanying issues in developing country contexts abound. This paper explores the issues that underlie online learning management in a developing country context, focusing on the Moodle, Sakai and Zoom platforms used in some selected public universities in Ghana.

The study relied on the qualitative approach to data collection and a descriptive design for analysis. Using the social constructivism theory, the paper discussed the critical issues students, and to a lesser extent, instructors encounter in their engagement on these platforms for academic learning.

The study found that the inefficient use of these platforms is due to several factors including the high cost of data, poor perception of the quality of online learning compared to traditional in-person contacts, poor attitude of students, low participation in online learning, lack of computers and poor internet connectivity among others.

It is important to mention that this study was limited to some selected public universities in Ghana. Data could have been collected from a wider sample including other Ghanaian tertiary institutions or some other developing countries. The similarity of study outcomes in other developing countries, however, suggests that similar results would have been obtained in an international sample.

Despite the robustness of the Learning Management systems in place, the evidence suggests that their utilisation is far less than optimal. However, with relevant policies and the provision of needed technical support, training, provision of equipment like computers for use by both students and instructors and efficient internet connectivity, the LMS platforms could be more efficient for online learning.

The authors conducted this research using original data from interviews in the selected public universities in Ghana. The data was meant to inform the discussion on some of the critical issues that underlie online learning in a developing country context. Although the study relied on data from selected public universities in a single country, its outcomes reflect fundamental issues of online learning in a developing country context which find relevance in available study outcomes.

Feature space heterogeneity exists widely in various application fields of classification techniques, such as customs inspection decision, credit scoring and medical diagnosis. This paper aims to study the relationship between feature space heterogeneity and classification performance.

A measurement is first developed for measuring and identifying any significant heterogeneity that exists in the feature space of a data set. The main idea of this measurement is derived from a meta-analysis. For the data set with significant feature space heterogeneity, a classification algorithm based on factor analysis and clustering is proposed to learn the data patterns, which, in turn, are used for data classification.

The proposed approach has two main advantages over the previous methods. The first advantage lies in feature transform using orthogonal factor analysis, which results in new features without redundancy and irrelevance. The second advantage rests on samples partitioning to capture the feature space heterogeneity reflected by differences of factor scores. The validity and effectiveness of the proposed approach is verified on a number of benchmarking data sets.

Measurement should be used to guide the heterogeneity elimination process, which is an interesting topic in future research. In addition, to develop a classification algorithm that enables scalable and incremental learning for large data sets with significant feature space heterogeneity is also an important issue.

Measuring and eliminating the feature space heterogeneity possibly existing in the data are important for accurate classification. This study provides a systematical approach to feature space heterogeneity measurement and elimination for better classification performance, which is favorable for applications of classification techniques in real-word problems.

A measurement based on meta-analysis for measuring and identifying any significant feature space heterogeneity in a classification problem is developed, and an ensemble classification framework is proposed to deal with the feature space heterogeneity and improve the classification accuracy.

In the last years, the design and implementation of web-based education systems has grown exponentially, spurred by the fact that neither students nor teachers are bound to a specific location and that this form of computer-based education is virtually independent of any specific hardware platform. These systems accumulate a large amount of data: educational data mining and learning analytics are the two much related fields of research with the aim of using these educational data to improve the learning process. In this chapter, the authors investigate the peer assessment setting in communities of learners. Peer assessment is an effective didactic strategy, useful to evaluate groups of students in educational environments such as high schools and universities where students are required to answer open-ended questions to increase their problem-solving skills. Furthermore, such an approach could become necessary in the learning contexts where the number of students to evaluate could be very large as, for example, in massive open online courses. Here the author focus on the automated support to grading open answers via a peer evaluation-based approach, which is mediated by the (partial) grading work of the teacher, and produces a (partial as well) automated grading. The author propose to support such automated grading by means of two methods, coming from the data-mining field, such as Bayesian Networks and K-Nearest Neighbours (K-NN), presenting some experimental results, which support our choices.

This paper is written to increase awareness and focus on the choice of executive coaches. There is a growing trend of selecting coaches based solely on a certification. This methodology of coach selection could lead to disappointing and lackluster results.

This scope of the paper is executive coaching within organizations, with a specific focus on the potential prerequisite of a coaching certification. The paper is written from the author's point of view. The author has interacted with and interviewed numerous coaches and leaders and extensive personal experience on the subject.

The results are that quality coaching is primarily about whom the coach is, not what techniques or certifications they hold. The “self” of the coach is the instrument of change, not a bag of tricks accumulated during a “coaching college”.

Coaching certification for many is a defense against personal accountability and responsibility: the responsibility to select and hire quality coaches and the responsibility to be a quality coach. Orchestrating quality coaching programs is difficult and laborious work that cannot be minimized or made more efficient. By certifying coaches we could be sterilizing otherwise potentially powerful relationships and denuding overall quality learning experiences (let alone creativity and further learning) on a grand scale. Any attempt to make the choice of a coach more efficient will potentially reduce the quality of the future coaching experience. Perhaps coaching leaders to coach is a more valuable and meaningful intervention then merely coaching one person's performance.

Quality coaching experiences consistently and significantly increase performance for many leaders. This paper is intended to help leaders and those that assist them in coach selection and coaching program orchestration increase the value and return on investment of coaching interventions.

What is the objective of giving personal feedback? Ideally it is to help people to improve their performance, but whenever we start to talk to others about their behaviour, we can be on difficult ground. There is a danger that whatever we say may be treated as negative criticism as opposed to constructive help. It is the way in which we phrase feedback which determines whether it is a negative comment or a positive one.