Cyber Security Violation in I0T-Enabled Bright Society: A Proposed Framework

The undesirable consequences of ICT proliferation remains a big concern. The rise in Internet of Things (IoT) have further exacerbated security and information privacy challenges. One main reason is organizations and individuals constantly violate regulations and rules. While cybersecurity and privacy scholars accentuate on the likelihood of rule violations at the individual and organizational levels, the evidence for and discussion of this concept is still scant. This study proposes an empirical response to the Bright ICT initiative of the Association of Information System. This initiative aims to drastically eliminate adverse effect of Internet of Things (IoT). However, a robust privacy and cybersecurity model is needed. This study draws on the selective organizational information privacy and security violation model and delineate it at individual level. Specifically, attitude towards behaviour and subjective norms, contextual conditions, rule and regulatory conditions, perceived risk of violating a privacy or security rule, economic and non-economic strain constructs are hypothesized to determine the likelihood of a privacy and cybersecurity rule violation. In this context, pertinent cybersecurity literatures for IoT-enabled environment were examined to suggest solutions to reduce the dark side of IoT-enabled bright society. This paper presents the proposed model

Bridging XML and Relational Databases: An Effective Mapping Scheme based on Persistent

XML has emerged as the leading medium for data transfer over the World Wide Web. At the present days, relational database is still widely used as the back-end database in most organizations. Since there is mismatch in these two structures, an effective mapping scheme is definitely essential that provides seamless integration with relational databases. On the other hand, an immutable labeling scheme is certainly significant to dentify the XML nodes uniquely as well as supports dynamic update without having the existing labels to be re-labeled when there is an occurance of dynamic update. As such, in this paper, we propose s-XML by adopting the Persistent Labeling scheme as the annotation scheme to ensure seamless integration with relational database and able to support updates without the need to re-construct the existing labels. We conduct experiments to show that s-XML performs better in terms of mapping the XML nodes to relational databases, query retrieval and dynamic update compared to the existing approaches.DOI:http://dx.doi.org/10.11591/ijece.v2i2.21

Intracranial Hemorrhage Annotation for CT Brain Images

In this paper, we created a decision-making model to detect intracranial hemorrhage and adopted Expectation Maximization(EM) segmentation to segment the Computed Tomography (CT) images. In this work, basically intracranial hemorrhage is classified into two main types which are intra-axial hemorrhage and extra-axial hemorrhage. In order to ease classification, contrast enhancement is adopted to finetune the contrast of the hemorrhage. After that, k-means is applied to group the potential and suspicious hemorrhagic regions into one cluster. The decision-making process is to identify whether the suspicious regions are hemorrhagic regions or non-regions of interest. After the hemorrhagic detection, the images are segmented into brain matter and cerebrospinal fluid (CSF) by using expectation-maximization (EM) segmentation. The acquired experimental results are evaluated in terms of recall and precision. The encouraging results have been attained whereby the proposed system has yielded 0.9333 and 0.8880 precision for extra-axial and intra-axial hemorrhagic detection respectively, whereas recall rate obtained is 0.9245 and 0.8043 for extra-axial and intra-axial hemorrhagic detection respectively

Adaptive web service selection based on data type matching for dynamic web service composition

Although there are many web services provided for access in World Wide Web (WWW), some services are not available at all times.It is very important to ensure all services are available when a service composition takes place.A web service that meets the requirements of the workflow but does not match the data type will still cause a failure in composition.To address this concern, we propose an adaptive web service selection method which is able to replace a current web service which has been used for composition but fails during execution time.The proposed algorithm will select the most appropriate web service based on web service discovery engine recommendation and match the requirement based on WSDL description. Upon matching the requirements of the workflow, the selected web service will be matched according to the input and output data type. The goal of this paper is to ensure every web service that meets the requirements of the workflow does not get rejected when the data type does not fulfill the matching criteria

Webs: A web accessibility barrier severity metric

A novel metric for quantitatively measuring the severity of websites barriers that limit the accessibility for disabled people is proposed. The metric is based on the Web Content Accessibility Guidelines (WCAG 2.0), which is the most adopted voluntary web accessibility standard internationally that can be tested automatically. The proposed metric is intended to rank the accessibility barriers based on their severity rather than the total conformance to priority levels.Our metric meets the requirements as a measurement for scientific research. An experiment is conducted to assess the results of our metric and to reveal the commonplace violations that persist in websites and affect disabled people interacting with the web

Machine learning methods to predict particulate matter PM2.5 [version 1; peer review: 2 approved]

Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions

Transcriptional activation of the Axl and PDGFR-α by c-Met through a ras- and Src-independent mechanism in human bladder cancer

<p>Abstract</p> <p>Background</p> <p>A cross-talk between different receptor tyrosine kinases (RTKs) plays an important role in the pathogenesis of human cancers.</p> <p>Methods</p> <p>Both NIH-Met5 and T24-Met3 cell lines harboring an inducible human c-Met gene were established. C-Met-related RTKs were screened by RTK microarray analysis. The cross-talk of RTKs was demonstrated by Western blotting and confirmed by small interfering RNA (siRNA) silencing, followed by elucidation of the underlying mechanism. The impact of this cross-talk on biological function was demonstrated by Trans-well migration assay. Finally, the potential clinical importance was examined in a cohort of 65 cases of locally advanced and metastatic bladder cancer patients.</p> <p>Results</p> <p>A positive association of Axl or platelet-derived growth factor receptor-alpha (PDGFR-α) with c-Met expression was demonstrated at translational level, and confirmed by specific siRNA knock-down. The transactivation of c-Met on Axl or PDGFR-α <it>in vitro </it>was through a <it>ras</it>- and Src-independent activation of mitogen-activated protein kinase/extracellular signal-regulated kinase (MEK/ERK) pathway. In human bladder cancer, co-expression of these RTKs was associated with poor patient survival (<it>p </it>< 0.05), and overexpression of c-Met/Axl/PDGFR-α or c-Met alone showed the most significant correlation with poor survival (<it>p </it>< 0.01).</p> <p>Conclusions</p> <p>In addition to c-Met, the cross-talk with Axl and/or PDGFR-α also contributes to the progression of human bladder cancer. Evaluation of Axl and PDGFR-α expression status may identify a subset of c-Met-positive bladder cancer patients who may require co-targeting therapy.</p