A blockchain-based framework for data quality in edge-computing-enabled crowdsensing

With the rapid development of mobile technology and smart devices, crowdsensing has shown its large potential to collect massive data. Considering the limitation of calculation power, edge computing is introduced to release unnecessary data transmission. In edge-computing-enabled crowdsensing, massive data is required to be preliminary processed by edge computing devices (ECDs). Compared with the traditional central platform, these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically. ECDs involved in one task are required to cooperate to process the task data. The privacy of participants is important in crowdsensing, so blockchain is used due to its decentralization and tamper-resistance. In crowdsensing tasks, it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced. As mentioned before, ECDs can’t process task data comprehensively and they are required to cooperate quality assessment. Therefore, a blockchain-based framework for data quality in edge-computing-enabled crowdsensing (BFEC) is proposed in this paper. DPoR (Delegated Proof of Reputation), which is proposed in our previous work, is improved to be suitable in BFEC. Iteratively, the final result is calculated without revealing the privacy of participants. Experiments on the open datasets Adult, Blog, and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.     

Ruminations of an Editor-in-Chief

The Journal of Strategic Information Systems (JSIS) was first published December 1991 by Robert (Bob) D. Galliers, its Founding Editor-in-Chief (EiC). Early on Bob invited me to join the JSIS International Editorial Board¹ (the Board then being more operational, with members serving as quasi-AEs, an arrangement we’ve reinstituted more recently). I became Pacific Asia Region Editor in 2003, with JSIS shifting from region editors to Senior Editors in 2007, in which role I served the journal through the end of 2018. Mid-2018 Bob and Sirkka (Sirkka Jarvenpaa joined as co-EiC in 2000) jointly decided it was time; and announced they would both step down from their co-EiC roles at the end of that year. In September 2018, I was endorsed by Bob and Sirkka, the Senior Editors and Elsevier, to succeed Bob and Sirkka and assume the role of Editor-in-Chief of JSIS commencing 1 January 2019. I served as Editor-in-Chief through 1 July 2021 after which Yolande Chan and I served as co-Editors-in-Chief. Now the end of 2022, I am stepping down from operational involvement with the journal. In this my final editorial I focus on my time as EiC, also reflecting on my almost 3 decades with the journal. I much appreciate being invited to write these brief ruminations and am honoured to be included amongst the journal’s Emeritus, along with the esteemed Robert Galliers and Sirkka Jarvenpaa.     

Electrohydrodynamic‐Induced SERS Immunoassay for Extensive Multiplexed Biomarker Sensing

Cancer diagnosis and patient monitoring require sensitive and simultaneous measurement of multiple cancer biomarkers considering that single biomarker analysis present inadequate information on the underlying biological transformations. Thus, development of sensitive and selective assays for multiple biomarker detection might improve clinical diagnosis and expedite the treatment process. Herein, a microfluidic platform for the rapid, sensitive, and parallel detection of multiple cancer‐specific protein biomarkers from complex biological samples is presented. This approach utilizes alternating current electrohydrodynamic‐induced surface shear forces that provide exquisite control over fluid flow thereby enhancing target–sensor interactions and minimizing non‐specific binding. Further, the use of surface‐enhanced Raman scattering‐based spectral encoding with individual barcodes for different targets enables specific and simultaneous detection of captured protein biomarkers. Using this approach, the specific and sensitive detection of clinically relevant biomarkers including human epidermal growth factor receptor 2 (HER2); Mucin 1, cell surface associated (MUC1); epidermal growth factor receptor; and Mucin 16, cell surface associated (MUC16) at concentrations as low as 10 fg mL^?1 in patient serum is demonstrated. Successful target detection from patient samples further demonstrates the potential of this current approach for the clinical diagnosis, which envisages a clinical translation for a rapid and sensitive appraisal of clinical samples in cancer diagnostics.     

All‐Organic Dual Spin Valves with Well‐Resolved Four Resistive‐States

The formation of all‐organic dual spin valves (DSVs) with three organic spin‐selective layers, that is, spin‐injection, spin‐detection, and an additional spin‐filtering layer at the intermediate, is reported. As spin‐selective layers, manganese‐ and cobalt phthalocyanines, which are well‐known single‐molecule magnets, are used in their immobilized forms, so that all‐organic DSVs can be prefabricated for characterization. The three spin‐selective layers have provided four configurations with at most two spin‐flip interfaces enforcing spin‐flipping at the two nonmagnetic organic spacer layers, for which copper phthalocyanine is used. Since a couple of the four configurations have exhibited similar resistivities, the degeneracy in the resistive‐states is broken through asymmetric spin‐injection and spin‐detection layers and also through asymmetric thickness of the nonmagnetic spacer layers. When both the spin‐flip interfaces are made operative independently, a 2‐bit logic with four distinct resistive states can be achieved.     

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after five-fold cross validation.