Gas Dynamics in the Barred Seyfert Galaxy NGC4151 - II. High Resolution HI Study

We present sensitive, high angular resolution (6" x 5") 21-cm observations of the neutral hydrogen in the nearby barred Seyfert galaxy, NGC4151. These HI observations, obtained using the VLA in B-configuration, are the highest resolution to date of this galaxy, and reveal hitherto unprecedented detail in the distribution and kinematics of the HI on sub-kiloparsec scales. A complete analysis and discussion of the HI data are presented and the global properties of the galaxy are related to the bar dynamics presented in Paper I.Comment: 13 pages including 9 figures and 3 tables; accepted for publication in MNRA

Digital Memories Based Mobile User Authentication for IoT

The increasing number of devices within the IoT is raising concerns over the efficiency and exploitability of existing authentication methods. The weaknesses of such methods, in particular passwords, are well documented. Although alternative methods have been proposed, they often rely on users being able to accurately recall complex and often unmemorable information. With the profusion of separate online accounts, this can often be a difficult task. The emerging digital memories concept involves the creation of a repository of memories specific to individuals. We believe this abundance of personal data can be utilised as a form of authentication. In this paper, we propose our digital memories based two-factor authentication mechanism, and also present our promising evaluation results. Keywords—Digital memories, authentication, IoT, securit

The Open Access Advantage Revisited

This paper is a revision of one that appeared in 2008, incorporating the many developments and changes that have happened since then.published_or_final_versio

Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

The Open Access Advantage

Hong Kong Open Access Committeepublished_or_final_versio

Resilience in American Indian and Alaska Native Public Health: An Underexplored Framework

Objective: To conduct a systematic literature review to assess the conceptualization, application, and measurement of resilience in American Indian and Alaska Native (AIAN) health promotion. Data Sources: We searched 9 literature databases to document how resilience is discussed, fostered, and evaluated in studies of AIAN health promotion in the United States. Study Inclusion and Exclusion Criteria: The article had to (1) be in English; (2) peer reviewed, published from January 1, 1980, to July 31, 2015; (3) identify the target population as predominantly AIANs in the United States; (4) describe a nonclinical intervention or original research that identified resilience as an outcome or resource; and (5) discuss resilience as related to cultural, social, and/or collective strengths. Data Extraction: Sixty full texts were retrieved and assessed for inclusion by 3 reviewers. Data were extracted by 2 reviewers and verified for relevance to inclusion criteria by the third reviewer. Data Synthesis: Attributes of resilience that appeared repeatedly in the literature were identified. Findings were categorized across the lifespan (age group of participants), divided by attributes, and further defined by specific domains within each attribute. Results: Nine articles (8 studies) met the criteria. Currently, resilience research in AIAN populations is limited to the identification of attributes and pilot interventions focused on individual resilience. Resilience models are not used to guide health promotion programming; collective resilience is not explored. Conclusion: Attributes of AIAN resilience should be considered in the development of health interventions. Attention to collective resilience is recommended to leverage existing assets in AIAN communities

BAT: block analytics tool integrated with blockchain based IoT platform

The Internet of Things (IoT) is currently the paradigm of connectivity and driving force behind the state-of-art applications and services. However, the exponential growth of the number of IoT devices and services, their distributed nature, and scarcity of resources has increased the number of security and privacy concerns ranging from the risks of unauthorized data alterations to the potential discrimination enabled by data analytics over sensitive information. A blockchain based IoT-platform is introduced to address these issues. Built upon the tamper-proof architecture, the access management mechanisms ensure the authenticity and integrity of data. Moreover, a novel approach called Block Analytics Tool (BAT), integrated with the platform is proposed to analyze and make predictions on data stored on blockchain. BAT enables the data-analysis applications to be developed using the data stored in the platform in an optimized manner acting as an interface to off-chain processing. A pharmaceutical supply chain is the use case scenario to show the functionality of the proposed platform. Furthermore, a model to forecast the demand of the pharmaceutical drugs is investigated using a real-world data set to demonstrate the functionality of BAT. Finally, the performance of BAT integrated with the platform is evaluated