IEEE Systems, Man, and Cybernetics Magazine 

About: IEEE Systems, Man, and Cybernetics Magazine is an academic journal published by Institute of Electrical and Electronics Engineers. The journal publishes majorly in the area(s): Computer science & Cybernetics. It has an ISSN identifier of 2333-942X. Over the lifetime, 195 publications have been published receiving 1501 citations. The journal is also known as: IEEE systems, man, & cybernetics magazine & IEEE systems, man, and cybernetics magazine.

The Internet of Things in Manufacturing: Key Issues and Potential Applications

Abstract: With the globalization of the world's economy, manufacturing enterprises are facing severe competition from their worldwide counterparts in terms of product price, function, quality, cost, and lead time. They are also experiencing growing pressure to meet higher environmental standards due to enhanced producer responsibility [1]. Meanwhile, consumers have more diversified and demanding needs, e.g., customized products. These challenges have pushed the manufacturing industry to embrace new technologies to remain competitive and meet user demands. The Internet of Things (IoT), which has great potential in transforming the manufacturing sector [2], has attracted tremendous attention from both academia and industry.

Learning from Uncertainty for Big Data: Future Analytical Challenges and Strategies

Abstract: Big data refers to data sets that are so large that conventional database management and data analysis tools are insufficient to work with them [1]. Presently, we are in an era of big data, which exists in various fields, such as social media, telecom, finance, medicine, biinformatics, and power networks. Big data results mainly from the evolutionary development of data storage and data collection techniques in recent years. Big data has become a bigger-than-ever problem with the quick developments of data collection technologies. In fact, the word big is a fuzzy concept. So far, we do not have a mathematical definition of big data. But we can use several features or coordinates to describe it, for example, the well-known 5V characteristics.

Modern Machine-Learning Algorithms: For Classifying Cognitive and Affective States From Electroencephalography Signals

Abstract: Estimating cognitive or affective states from brain signals is a key but challenging step in creating passive brain-computer interface (BCI) applications. So far, estimating mental workloads or emotions from electroencephalogram (EEG) signals is only feasible with modest classification accuracies, which thus lead to unreliable neuroadaptive applications. However, recent machine-learning algorithms, notably Riemannian geometry-based classifiers (RGCs) and convolutional neural networks (CNNs), have shown promise for other BCI systems, e.g., motor imagery BCIs. However, they have not been formally studied and compared for cognitive or affective states classification.

The Internet of Things Meets Business Process Management: A Manifesto

Abstract: The Internet of Things (IoT) refers to a network of connected devices that collects and exchanges data through the Internet. These things can be artificial or natural and interact as autonomous agents that form a complex system. In turn, business process management (BPM) was established to analyze, discover, design, implement, execute, monitor, and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and in practice, we strongly believe that, on the one hand, the management of IoT applications will greatly benefit from BPM concepts, methods, and technologies. On the other hand, the IoT poses challenges that will require enhancements and extensions of the current state of the art in the BPM field. In this article, we question the extent to which these two paradigms can be combined, and we discuss emerging challenges and intersections from a research and practitioner's point of view in terms of complex software systems development.

Brain-Machine Interfaces: A Tale of Two Learners

Abstract: Brain-machine interface (BMI) technology has rapidly matured over the last two decades, mainly thanks to the introduction of artificial intelligence (AI) methods, in particular, machine-learning algorithms. Yet, the need for subjects to learn to modulate their brain activity is a key component of successful BMI control. Blending machine and subject learning, or mutual learning, is widely acknowledged in the BMI field. Nevertheless, we posit that current research trends are heavily biased toward the machine-learning side of BMI training. In this article, we take a critical view of the relevant literature, and our own previous work, to identify the key issues for more effective mutual-learning schemes in translational BMIs that are specifically tailored to promote subject learning. We identify the main caveats in the literature on subject learning in BMI, in particular, the lack of longitudinal studies involving end users and shortcomings in quantifying subject learning, and pinpoint critical improvements for future experimental designs.