1,721,352 research outputs found
The Security of Medical Data on Internet Based on Differential Privacy Technology
No full textThe study aims at discussing the security of medical data in the Internet era. By using k-anonymity (K-A) and differential privacy (DP), an algorithm model combining K-A and DP was proposed, which was simulated through the experiments. In the Magic and EIA datasets, the algorithm constructed was compared with K-A and the L-diversity model to verify the performance of the model. The model constructed based on DP had the lowest privacy-leakage risks, which increased with the number of identifiers in the Magic and EIA datasets, and the information disclosure was the least. In addition, in its usability analysis, it was found that its value was the most obviously improved and its operation efficiency was the highest. The K-A-DP algorithm can effectively reduce the risk of privacy leakage and information loss, and has achieved excellent results. Despite the deficiencies in the process of the experiment, the study still provides a reference for solving the problem of medical data security
Technical Committees: Recent Activities of the DEIS Technical Committee on 'HVDC Cable Systems': Two New Study Groups for Novel IEEE Standards on Space Charge Measurement
No full textThe IEEE DEIS TC 'HVDC cable systems (cables, joints and terminations)' has been quite active since its formation in 2012 (for a summary, see the TC website [1]) and has issued the following two IEEE standards: (1) IEEE 1732-2017, 'Recommended Practice for Space Charge Measurements in HVDC Extruded Cables for Rated Voltages up to 550 kV' [2] and (2) IEEE 2862-2020, 'Recommended Practice for Partial Discharge Measurements under AC Voltage with VHF/UHF Sensors During Routine Tests on Factory and Pre-moulded Joints of HVDC Extruded Cable Systems up to 800 kV' [3]
Evolution of b-value during the seismic cycle. Insights from laboratory experiments on simulated faults
No full textWe investigate the evolution of the frequency-magnitude b-value during stable and unstable frictional sliding experiments. Using a biaxial shear configuration, we record broadband acoustic emissions (AE) while shearing layers of simulated granular fault gouge under normal stresses of 2–8 MPa and shearing velocity of 11 μm/s. AE event amplitude ranges over 3–4 orders of magnitude and we find an inverse correlation between b and shear stress. The reduction of b occurs systematically as shear stress rises prior to stick–slip failure and indicates a greater proportion of large events when faults are more highly stressed. For quasi-periodic stick–slip events, the temporal evolution of b has a characteristic saw-tooth pattern: it slowly drops as shear stress increases and quickly jumps back up at the time of failure. The rate of decrease during the inter-seismic period is independent of normal stress but the average value of b decreases systematically with normal stress. For stable sliding, b is roughly constant during shear, however it exhibits large variability. During irregular stick–slip, we see a mix of both behaviors: b decreases during the interseismic period between events and then remains constant when shear stress stabilizes, until the next event where a co-seismic increase is observed. Our results will help improve seismic hazard assessment and, ultimately, could aid earthquake prediction efforts by providing a process-based understanding of temporal changes in b-value during the seismic cycle
Advanced Machine-Learning Methods for Brain-Computer Interfacing
No full textThe brain-computer interface (BCI) connects the brain and the external world through an information transmission channel by interpreting the physiological information of the brain during thinking activities. The effective classification of electroencephalogram (EEG) signals is the key to improving the performance of the system. To improve the classification accuracy of EEG signals in the BCI system, the transfer learning algorithm and the improved Common Spatial Pattern (CSP) algorithm are combined to construct a data classification model. Finally, the effectiveness of the proposed algorithm is verified. The results show that in actual and imagined movements, the accuracy of the left- and right-hand movements at different speeds is higher than when the speeds are the same. The proposed Adaptive Composite Common Spatial Pattern (ACCSP) and Self Adaptive Common Spatial Pattern (SACSP) algorithms have good classification effects on 5 subjects, with an average classification accuracy rate of 83.58 percent, which is an increase of 6.96 percent compared with traditional algorithms. When the training sample size is 10, the classification accuracy of the ACCSP algorithm is higher than that of the traditional CSP algorithm. The improved CSP algorithm combined with transfer learning embodies a good classification effect in both ACCSP and SACSP. Especially, the performance of SACSP mode is better. Combining the improved CSP algorithm proposed with the CSP-based transfer learning algorithm can improve the classification accuracy of the BCI classifier
Towards smarter cities: Learning from Internet of Multimedia Things-generated big data
Full text linkIn today’s technological era, smart devices connected through the IoT and giant IoT infrastructures are playing a vital role in making daily life easier and simpler than it ever was. Numerous sensors including IoT-interconnected multimedia sensors communicating with each other generate a huge amount of data. In particular, IoT multimedia sensors play a vital role for green cities, providing secure and efficient analytics to monitor routine activities. Big data generated by these sensors contain dense information that needs to be processed for various applications such as summarization,
security, and privacy. The heterogeneity and complexity of video data is the biggest hurdle and a pretty number of techniques are already developed for the efficient processing of big video data. IoT big data processing is an emerging field and many researchers are enthusiastic to contribute in making the cities smarter. Among all these methods, deep learning-based techniques are dominant over existing traditional multimedia data processing algorithms with convincing results emerged recently. This special issue targets the current problems in smart cities development and provides future challenges in this domain and invite researchers working in IoT domain to make cities smarter. It also focuses on some related technologies comprising Internet of Multimedia Things (IoMTs) and machine learning for big data. Furthermore, it covers deep learning-based
solutions for real-time data processing, learning from big data, distributed learning paradigms with embedded processing, and efficient inference
Deep learning-based quality prediction for multi-stage sequential hot rolling processes in heavy rail manufacturing
No full textTraditional quality control practices in heavy rail manufacturing primarily rely on experiential knowledge and numerical simulations. However, these methods come with significant drawbacks, including high costs, safety concerns, and limited accuracy due to the inadequate consideration of various influencing factors. Despite the accumulation of extensive data by some heavy rail manufacturers in recent years, achieving data-driven quality modeling remains a formidable challenge, mainly due to the complexity of the multi-stage sequential production process. This study presents the Temporal-Spatial Mapping approach to transform isochronous sampling data into spatially aligned data. Additionally, it introduces a multi-objective prediction model, Temporal-Fusion-LSTM (TFL), designed for evaluating the quality of heavy rails. The proposed model presents the Double-Layer LSTM with Time-Steps Fusion (DLL) architecture. The first layer of the DLL architecture targets the temporal transfer regularity within a single stage, while the second layer addresses the transition dynamics across multiple rolling stages in the heavy rail rolling process. Following this, the Local Process Correlation Matrix (LPCM) based on the Maximal Information Coefficient (MIC) is employed to empower the model to comprehend the intricate interactions among parameters in each stage of the rolling process. Finally, the Multi-Gate based Shared-Bottom architecture is employed to accentuate manufacturing processes highly correlated with the target quality indicators during multi-objective prediction. Real production data from a heavy rail factory is utilized to validate the effectiveness of the method. The experimental results unequivocally demonstrate that the proposed method accurately predicts heavy rail quality and facilitates cross-stage prediction, thus establishing the groundwork for data-driven quality diagnosis and pre-control in heavy rail production
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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