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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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13 pages, 1805 KiB  
Article
An Enhanced Inference Algorithm for Data Sampling Efficiency and Accuracy Using Periodic Beacons and Optimization
by James Jin Kang, Kiran Fahd and Sitalakshmi Venkatraman
Big Data Cogn. Comput. 2019, 3(1), 7; https://doi.org/10.3390/bdcc3010007 - 16 Jan 2019
Cited by 1 | Viewed by 3427
Abstract
Transferring data from a sensor or monitoring device in electronic health, vehicular informatics, or Internet of Things (IoT) networks has had the enduring challenge of improving data accuracy with relative efficiency. Previous works have proposed the use of an inference system at the [...] Read more.
Transferring data from a sensor or monitoring device in electronic health, vehicular informatics, or Internet of Things (IoT) networks has had the enduring challenge of improving data accuracy with relative efficiency. Previous works have proposed the use of an inference system at the sensor device to minimize the data transfer frequency as well as the size of data to save network usage and battery resources. This has been implemented using various algorithms in sampling and inference, with a tradeoff between accuracy and efficiency. This paper proposes to enhance the accuracy without compromising efficiency by introducing new algorithms in sampling through a hybrid inference method. The experimental results show that accuracy can be significantly improved, whilst the efficiency is not diminished. These algorithms will contribute to saving operation and maintenance costs in data sampling, where resources of computational and battery are constrained and limited, such as in wireless personal area networks emerged with IoT networks. Full article
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21 pages, 1223 KiB  
Article
The Next Generation Cognitive Security Operations Center: Adaptive Analytic Lambda Architecture for Efficient Defense against Adversarial Attacks
by Konstantinos Demertzis, Nikos Tziritas, Panayiotis Kikiras, Salvador Llopis Sanchez and Lazaros Iliadis
Big Data Cogn. Comput. 2019, 3(1), 6; https://doi.org/10.3390/bdcc3010006 - 10 Jan 2019
Cited by 27 | Viewed by 8018
Abstract
A Security Operations Center (SOC) is a central technical level unit responsible for monitoring, analyzing, assessing, and defending an organization’s security posture on an ongoing basis. The SOC staff works closely with incident response teams, security analysts, network engineers and organization managers using [...] Read more.
A Security Operations Center (SOC) is a central technical level unit responsible for monitoring, analyzing, assessing, and defending an organization’s security posture on an ongoing basis. The SOC staff works closely with incident response teams, security analysts, network engineers and organization managers using sophisticated data processing technologies such as security analytics, threat intelligence, and asset criticality to ensure security issues are detected, analyzed and finally addressed quickly. Those techniques are part of a reactive security strategy because they rely on the human factor, experience and the judgment of security experts, using supplementary technology to evaluate the risk impact and minimize the attack surface. This study suggests an active security strategy that adopts a vigorous method including ingenuity, data analysis, processing and decision-making support to face various cyber hazards. Specifically, the paper introduces a novel intelligence driven cognitive computing SOC that is based exclusively on progressive fully automatic procedures. The proposed λ-Architecture Network Flow Forensics Framework (λ-ΝF3) is an efficient cybersecurity defense framework against adversarial attacks. It implements the Lambda machine learning architecture that can analyze a mixture of batch and streaming data, using two accurate novel computational intelligence algorithms. Specifically, it uses an Extreme Learning Machine neural network with Gaussian Radial Basis Function kernel (ELM/GRBFk) for the batch data analysis and a Self-Adjusting Memory k-Nearest Neighbors classifier (SAM/k-NN) to examine patterns from real-time streams. It is a forensics tool for big data that can enhance the automate defense strategies of SOCs to effectively respond to the threats their environments face. Full article
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13 pages, 1541 KiB  
Article
Beneficial Artificial Intelligence Coordination by Means of a Value Sensitive Design Approach
by Steven Umbrello
Big Data Cogn. Comput. 2019, 3(1), 5; https://doi.org/10.3390/bdcc3010005 - 6 Jan 2019
Cited by 31 | Viewed by 11714
Abstract
This paper argues that the Value Sensitive Design (VSD) methodology provides a principled approach to embedding common values into AI systems both early and throughout the design process. To do so, it draws on an important case study: the evidence and final report [...] Read more.
This paper argues that the Value Sensitive Design (VSD) methodology provides a principled approach to embedding common values into AI systems both early and throughout the design process. To do so, it draws on an important case study: the evidence and final report of the UK Select Committee on Artificial Intelligence. This empirical investigation shows that the different and often disparate stakeholder groups that are implicated in AI design and use share some common values that can be used to further strengthen design coordination efforts. VSD is shown to be both able to distill these common values as well as provide a framework for stakeholder coordination. Full article
(This article belongs to the Special Issue Artificial Superintelligence: Coordination & Strategy)
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21 pages, 1160 KiB  
Review
Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review
by Vinh Phuc Tran, Adel Ali Al-Jumaily and Syed Mohammed Shamsul Islam
Big Data Cogn. Comput. 2019, 3(1), 3; https://doi.org/10.3390/bdcc3010003 - 1 Jan 2019
Cited by 67 | Viewed by 10595
Abstract
Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and [...] Read more.
Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage. Full article
(This article belongs to the Special Issue Health Assessment in the Big Data Era)
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