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This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to identify the factors affecting the intention to use peer-to-peer (P2P) mobile payment. Although mobile technology has become part of everyday life, certain actions and services, such as mobile payments, are still used relatively infrequently. In this paper, we analyse consumers' adoption of P2P mobile payment services. Following a review of previous literature in this field, we identify the main factors that determine the adoption of mobile payments, and then perform a logistic regression (LR) analysis and propose a neural network to predict this adoption. From the logistic regression results obtained we conclude that six variables significantly influence intentions to use P2P payment: ease of use, perceived risk, personal innovativeness, perceived usefulness, subjective norms and perceived enjoyment. With respect to the nonparametric technique, we find that the multilayer perceptrons (MLP) prediction model for the use of P2P payment obtains higher AUC values, and thus is more accurate, than the LR model. This paper is a pioneer study of intention to use with mobile payment using these methodologies. The outcome of this research has important implications for the theory and practice of the adoption of P2P mobile payment services. [ABSTRACT FROM AUTHOR]

Research has proven that having high level of emotional intelligence (EI) can reduce the chance of getting mental illness. EI, and its component, can be improved with training, but currently the process is less flexible and very time-consuming. Machine learning (ML), on the other hand, can analyse huge amount of data to discover useful trends and patterns in shortest time possible. Despite the benefits, ML usage in EI training is scarce. In this paper, we studied 92 journal articles to discover the trend of the ML utilisation in the study of EI and its components. This survey aims to pave way for future studies that could lead to implementation of ML in EI training, and to rope in researchers in psychology and computer science to find possibilities of having a generic ML algorithm for every EI's components. Our findings show an increasing trend to apply ML on EI components, and Support Vector Machine and Neural Network are the two most popular ML algorithms used in those researches. We also found that social skill and empathy are the least exposed EI components to ML. Finally, we provide recommendations for future research direction of ML in EI domain, and EI in ML. [ABSTRACT FROM AUTHOR]

Many Q&A sites initially run only in English, and then gradually release their multi-lingual variants to serve users who speak other languages. The launch of such multi-lingual sites always lead to an intense dispute about the pros and cons of multi-lingual sites. Although all arguments and concerns sound reasonable, people can rarely provide solid evidence to convince each other. In this paper, from users' comments about the launch of several non-English Stack Overflow sites, we first identify three major concerns including community split, knowledge needs and interests in other languages, and knowledge fragmentation and duplication. To validate these three concerns, we conduct an evidence-based data analysis and comparison of user characteristics, tag usage and cross-site links between the Russian Stack Overflow and the English Stack Overflow on these three concerns. Our study sheds light on the existence value and risks of multi-lingual Q&A sites. [ABSTRACT FROM AUTHOR]

Given the growing adoption of Internet of Things (IoT) technologies, several approaches have been presented to enable people to increase their control over their smart devices and provide relevant support. Recommendation systems have been proposed in many domains, but have received limited attention in the area of End-User Development (EUD). We propose a novel approach for formulating recommendations in this area, based on deconstructing trigger-action rules into sequences of elements and the links between them. For this purpose, we propose a solution inspired by methods aimed at addressing the sequence-prediction problem. We have used this approach to provide users with two different types of recommendations: full rules for the one being edited, and parts of rules relevant for the next step to take in order to complete the current rule editing. In this paper, we present the design and a first evaluation of the two different possibilities to generate and display recommendations in a block-based EUD environment for creating automations for IoT contexts. [ABSTRACT FROM AUTHOR]

The success of e-learning management systems (e-LMSs) such as MOODLE depends on the usage of students as well as instructor acceptance in a virtual learning environment. E-Learning enables instructors to access educational resources to support traditional classroom teaching. This paper attempts to develop a model to understand and predict the effect of individual characteristics (technology experience [TE] and personal innovativeness [PI]) and e-LMS quality determinants (system quality [SYS-Q], information quality, and service quality) on the continuous use of e-LMS by instructors, which is critical to its success. A total of 219 instructors using MOODLE responded to the survey. The structural equation model (SEM) was employed to test the proposed research model. The SEM results showed that SYS-Q, PI, service quality, and TE have a statistically significant influence on continuous usage of e-LMS by instructors. Furthermore, all determinants of the research model were given as input to an NN model to overcome the simplistic nature of the SEM model. The NN model results showed that service quality is the most important predictor of elearning acceptance followed by SYS-Q, PI, information quality, and TE. This paper attempts to develop a causal and predictive statistical model for predicting instructor e-LMS acceptance. [ABSTRACT FROM AUTHOR]