Informatics, Volume 5, Issue 4 (December 2018) – 8 articles

Writing desktop applications in JavaScript offers developers the opportunity to create cross-platform applications with cutting-edge capabilities. However, in doing so, they are potentially submitting their code to a number of unsanctioned modifications from malicious actors. Electron is one such JavaScript application framework which facilitates this multi-platform out-the-box paradigm and is based upon the Node.js JavaScript runtime—an increasingly popular server-side technology. By bringing this technology to the client-side environment, previously unrealized risks are exposed to users due to the powerful system programming interface that Node.js exposes. In a concerted effort to highlight previously unexposed risks in these rapidly expanding frameworks, this paper presents the Mayall Framework, an extensible toolkit aimed at JavaScript security auditing and post-exploitation analysis. This paper also exposes fifteen highly popular Electron applications and demonstrates that two-thirds of applications were found to be using known vulnerable elements with high CVSS (Common Vulnerability Scoring System) scores. Moreover, this paper discloses a wide-reaching and overlooked vulnerability within the Electron Framework which is a direct byproduct of shipping the runtime unaltered with each application, allowing malicious actors to modify source code and inject covert malware inside verified and signed applications without restriction. Finally, a number of injection vectors are explored and appropriate remediations are proposed. Full article

Online reviews are an important source of opinion to measure products’ quality. Hence, automated opinion mining is used to extract important features (aspect) and related comments (sentiment). Extraction of correct aspect-sentiment pairs is critical for overall outcome of opinion mining; however, current works still have limitations in terms of identifying special compound noun and parent-child relationship aspects in the extraction process. To address these problems, an aspect-sentiment pair extraction using the rules and compound noun lexicon (ASPERC) model is proposed. The model consists of three main phases, such as compound noun lexicon generation, aspect-sentiment pair rule generation, and aspect-sentiment pair extraction. The combined approach of rules generated from training sentences and domain specific compound noun lexicon enable extraction of more aspects by firstly identifying special compound noun and parent-child aspects, which eventually contribute to more aspect-sentiment pair extraction. The experiment is conducted with the SemEval 2014 dataset to compare proposed and baseline models. Both ASPERC and its variant, ASPER, result higher in recall (28.58% and 22.55% each) compared to baseline and satisfactorily extract more aspect sentiment pairs. Lastly, the reasonable outcome of ASPER indicates applicability of rules to various domains. Full article

The gaming industry has evolved into a multi-functional smart city that combines integrated casinos and entertainment (ICE). ICE logistics involve supply chains with various stages in geographically-distributed locations and with limited and complex storage and warehouses. Challenges are to leverage demands, traffic, and storage allocation in ICE logistics. The decentralized structure of blockchain technology allows all parties to participate in ICE logistics. Its cryptography-based, immutable nature gives the assurance of security. This research deals with the design and application of blockchains in ICE logistics. We first adopt a Concentric Value Circles (CVC) model to identify the requirements and business opportunities that use blockchain technology in ICE logistics. We develop an open, automated, and transparent platform, TransICE, which utilizes the feature of smart contracts in blockchain technology and adopts a decentralized model, Hawk, where no financial transactions are stored on the blockchain to hold privacy of transactions publicly. Two cases, (1) the Shipment Pricing and Scheduling process and (2) the Pickup, Shipping and Delivery process in TransICE, are studied to illustrate the applications and feasibility of the proposed TransICE platform and the developed smart contracts of the Hawk model. Full article

Touch plays a fundamental role in our daily interactions, allowing us to interact with and perceive objects and their spatial properties. Despite its importance in the real-world, touch is often ignored in virtual environments. However, accurately simulating the sense of touch is difficult, requiring the use of high-fidelity haptic devices that are cost-prohibitive. Lower fidelity consumer-level haptic devices are becoming more widespread, yet are generally limited in perceived fidelity and the range of motion (degrees of freedom) required to realistically simulate many tasks. Studies into sound and vision suggest that the presence or absence of sound can influence task performance. Here, we explore whether the presence or absence of contextually relevant sound cues influences the performance of a simple haptic drilling task. Although the results of this study do not show any statistically significant difference in task performance with general (task-irrelevant) sound, we discuss how this is a necessary step in understanding the role of sound on haptic perception. Full article

Counterfeit and unaccounted postage stamps used on mailings cost postal administrations a significant amount of money each year. Corporate and individual clients become victim to stamp fraud and incur losses when security teams investigate such mailings. The blockchain technology is supposed to be a solution to make postage stamps market transparent and to guarantee invariability of stamps volume produced and used. The blockchain-based supply chain for postage stamps is introduced in the article. Full article

Background: The WHO framework on integrated people-centred health services promotes a focus on the needs of people and their communities to empower them to have a more active role in their own health. It has advocated five strategies including: Engaging and empowering people and communities; co-ordinating services within and across sectors; and, creating an enabling environment. Any implementation of these strategies needs to occur at individual, community, and health service levels. Useful steps to reorganising health service provision are already being guided by existing models of care linked to increased adoption and use of digital technologies with examples including: Wagner’s Chronic Care Model (CCM); Valentijn’s Rainbow Model of Integrated Care (RMIC); and Phanareth’s et al.’s Epital Care Model (ECM). However, what about individuals and the communities they live in? How will strategies be implemented to address known inequities in: the social determinants of health; access to, and use of digital technologies, and individual textual, technical, and health literacies? Proposal of a matrix framework: This paper argues that people with complex and chronic conditions (PwCCC) living in communities that are at risk of being under-served or marginalised in health service provision require particular attention. It articulates a step-by-step process to identify these individuals and co-produce mechanisms to engage, empower and ultimately emancipate these individuals to become activated in living with their conditions and in their interactions with the health system and community. This step-by-step process focuses on key issues related to the design and role of digital services in mitigating the effects of the health service inequity and avoiding the creation of an e-health divide amongst users when advocating digital behaviour change initiatives. This paper presents a matrix framework providing a scaffold across three inter-related levels of the individual; the provider, and the health and care system. The matrix framework supports examination of and reflection on the design and role of digital technologies in conjunction with pre-existing motivational instruments. This matrix framework is illustrated with examples from practice. Conclusion: It is anticipated that the matrix framework will evolve and can be used to map and reflect on approaches and practices aiming to enrich and stimulate co-production activities supported by digital technology focused on enhancing people-centred health services for the marginalised. Full article

Credit scoring is generally recognized as one of the most significant operational research techniques used in banking and finance, aiming to identify whether a credit consumer belongs to either a legitimate or a suspicious customer group. With the vigorous development of the Internet and the widespread adoption of electronic records, banks and financial institutions have accumulated large repositories of labeled and mostly unlabeled data. Semi-supervised learning constitutes an appropriate machine- learning methodology for extracting useful knowledge from both labeled and unlabeled data. In this work, we evaluate the performance of two ensemble semi-supervised learning algorithms for the credit scoring problem. Our numerical experiments indicate that the proposed algorithms outperform their component semi-supervised learning algorithms, illustrating that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework. Full article

A crucial factor in Big Data is to take advantage of available data and use that for new discovery or hypothesis generation. In this study, we analyzed Large-scale data from the literature to OMICS, such as the genome, proteome or metabolome, respectively, for skin conditions. Skin acts as a natural barrier to the world around us and protects our body from different conditions, viruses, and bacteria, and plays a big part in appearance. We have included Hyperpigmentation, Postinflammatory Hyperpigmentation, Melasma, Rosacea, Actinic keratosis, and Pigmentation in this study. These conditions have been selected based on reasoning of big scale UCSF patient data of 527,273 females from 2011 to 2017, and related publications from 2000 to 2017 regarding skin conditions. The selected conditions have been confirmed with experts in the field from different research centers and hospitals. We proposed a novel framework for large-scale available public data to find the common genotypes and phenotypes of different skin conditions. The outcome of this study based on Advance Data Analytics provides information on skin conditions and their treatments to the research community and introduces new hypotheses for possible genotype and phenotype targets. The novelty of this work is a meta-analysis of different features on different skin conditions. Instead of looking at individual conditions with one or two features, which is how most of the previous works are conducted, we looked at several conditions with different features to find the common factors between them. Our hypothesis is that by finding the overlap in genotype and phenotype between different skin conditions, we can suggest using a drug that is recommended in one condition, for treatment in the other condition which has similar genes or other common phenotypes. We identified common genes between these skin conditions and were able to find common areas for targeting between conditions, such as common drugs. Our work has implications for discovery and new hypotheses to improve health quality, and is geared towards making Big Data useful. Full article