Putting Question-Answering Systems into Practice

Design of an Inclusive Financial Privacy Index (INF-PIE): A Financial Privacy and Digital Financial Inclusion Perspective

Financial privacy is an important part of an individual's privacy, but efforts to enhance financial privacy have often not been given enough prominence by some countries when advancing financial inclusion. This impedes under-served communities from utilizing financial services. This article adopts a design science approach to create an INclusive Financial Privacy IndEx (INF-PIE) from the two perspectives of financial privacy and digital financial inclusion to help ensure financial services for a wide range of populations. This article first examines the privacy policies of Mobile Wallet and Remittance (MWR) apps (a digital financial solution), uses an analytics approach for extracting semi-structured information components; and based on text categorization and topic modeling, creates privacy policy compliance scores. In particular, it analyses the privacy policies using natural language processing techniques such as Term Frequency-Inverse Document Frequency (tf-idf) and Latent Dirichlet Allocation (LDA). This article then develops a digital financial inclusion score through a multivariate analysis of indexes extracted from the global findex dataset using Principal Component Analysis (PCA). Finally, the INF-PIE framework is established to analyze various countries and assess their financial privacy and digital financial inclusion practices. This framework can show how countries’ relative data privacy compliance and digital financial inclusion practices underscore their inclusive financial privacy.

An Ensemble of Ensembles Approach to Author Attribution for Internet Relay Chat Forensics

With the advances in Internet technologies and services, social media has been gained extreme popularity, especially because these technologies provide potential anonymity, which in turn harbors hacker discussion forums, underground markets, dark web, and so on. Internet relay chat (IRC) is a real-time communication protocol actively used by cybercriminals for hacking, cracking, and carding. Hence, it is particularly urgent to identify the authors of threat messages and malicious activities in IRC. Unfortunately, author identification studies in IRC remain as an underexplored area. In this research, we perform novel IRC text feature extraction methods and propose the first author attribution version of the deep forest (DF) model that is an ensemble of ensembles that utilizes the fusion of ensemble learning techniques. Our approach is supported by autonomic IRC monitoring. Experiments show that our approach is highly effective for author attribution and attains high accuracy even when the number of candidates is large while training data is limited.