IEEE Transactions on Multi-Scale Computing Systems

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From the October-December 2017 issue

Keep the Stress Away with SoDA: Stress Detection and Alleviation System

By Ayten Ozge Akmandor and Niraj K. Jha

Featured article thumbnail image Long-term exposure to stress may lead to serious health problems such as those related to the immune, cardiovascular, and endocrine systems. Once having arisen, these problems require a considerable investment of time and money to recover from. With early detection and treatment, however, these health problems may be nipped in the bud, thus improving quality of life. We present an automatic stress detection and alleviation system, called SoDA, to address this issue. SoDA takes advantage of emerging wearable medical sensors (WMSs), specifically, electrocardiogram (ECG), galvanic skin response (GSR), respiration rate, blood pressure, and blood oximeter, to continuously monitor human stress levels and mitigate stress as it arises. It performs stress detection and alleviation in a user-transparent manner, i.e., without the need for user intervention. When it detects stress, SoDA employs a stress alleviation technique in an adaptive manner based on the stress response of the user. We establish the effectiveness of the proposed system through a detailed analysis of data collected from 32 participants. A total of four stressors and three stress reduction techniques are employed. In the stress detection stage, SoDA achieves 95.8 percent accuracy with a distinct combination of supervised feature selection and unsupervised dimensionality reduction. In the stress alleviation stage, we compare SoDA with the ‘no alleviation’ baseline and validate its efficacy in responding to and alleviating stress.

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Editorials and Announcements

Announcements

  • The winner of the 2017 Best TMSCS Paper Award is:
    "Enabling New Computation Paradigms with HyperFET - An Emerging Device"
    by Wei-Yu Tsai, Xueqing Li, Matthew Jerry, Baihua Xie, Nikhil Shukla, Huichu Liu, Nandhini Chandramoorthy, Matthew Cotter, Arijit Raychowdhury, Donald M Chiarulli, Steven P Levitan, Suman Datta, John Sampson, Nagarajan Ranganathan, Vijaykrishnan Narayanan
    IEEE Transactions on Multi-Scale Computing Systems, Vol. 2, Iss. 1, pp. 30-48, 2016.
  • We're pleased to announce that Partha Pratim Pande, professor at Washington State University, has accepted the position of inaugural Editor-in-Chief.

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Special Issue on Advances in High-Performance Interconnects

Submission deadline: 1 Jan. 2018. View PDF.

High-performance interconnects have had a major role in high-performance scientific computing for several decades. They are also gaining traction in cloud computing and massive traditional data centers. The intersection of high-performance interconnects between these two areas spans networks of all scales, from small on-chip interconnects to whole system interconnects with hundreds of thousands of links. Innovation of new network architectures both in terms of software and hardware systems is a key component in next generation systems. As the number of cores in a single node increases, the bandwidth and message rates required from a single network interface controller (NIC) has continued to rise rapidly. Improvements to NIC architectures are required in order to keep up with increasing core counts on CPUs. In addition, improvements in network topologies have driven down costs by reducing the number of links and switches required to provide very high performance. These changes have also introduced interesting challenges as innovation in network topologies requires adopting adaptive routing and advanced congestion avoidance mechanisms. This special issue concentrates on the latest cutting edge developments in interconnects from both the cloud/data center and scientific computing communities.

This special issue invites original research papers and extended papers from the 25th Annual Symposium on High-Performance Interconnects that showcase cutting-edge research on hardware and software architectures and implementations for interconnection networks of all scales.

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