The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keep...
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The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret
Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and t...
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Metamorphic testing is a testing method for problems without test oracles. Integration testing allows for detecting errors in complex systems that may not be found during the testing of their components. In this paper...
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The article the efficiency of the Microgrid network when transitioning to a transactive power system that uses control algorithms called to optimize the distribution of power between sources of distributed generation ...
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Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperabi...
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Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs)—in our case, distributed generators (DGs)-in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on nonsensitive data, the transfer of sensitive information between stakeholders is prevented. Consequently, even if reverse engineering is applied to these ML models, no sensitive data can be extracted. This allows
Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS ...
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Processors for terrestrial applications are protected by Earth's atmosphere and magnetic field, so the probability of single event effects (SEE) is significantly lower. Shrinking of the technology nodes allows hig...
Processors for terrestrial applications are protected by Earth's atmosphere and magnetic field, so the probability of single event effects (SEE) is significantly lower. Shrinking of the technology nodes allows higher operational frequency and performance, but their susceptibility to SEE is increasing. Nowadays satellites and safety critical automotive applications require more and more computational power. The hardening by design has been implemented mainly by redundancy of the whole cores. The consequences are lower frequency, larger area, and higher power consumption. We propose a novel hardening technique, which enables high frequency operation and doesn't cause large power consumption and area overhead. The protection is based redundancy and separation of the pipeline into two main sections. It provides a fast detection of faults, simple recovery by flush of the pipeline, and allows a large prediction unit to be unprotected. The whole protection scheme can be implemented at the Register Transfer Level. We present the protection scheme at the core with RISC-V instruction set. Simulations confirm the protection can handle the injected faults. Synthesis shows, the protection lowers the maximum frequency by only about 4.7%. The area increase is only about 118% and can be even lower.
In this paper, we propose a novel hardening technique against Single Event Effects, which enables high-frequency operation and does not cause large power consumption and area overhead. The protection is based on redun...
In this paper, we propose a novel hardening technique against Single Event Effects, which enables high-frequency operation and does not cause large power consumption and area overhead. The protection is based on redundancy, separation of the pipeline into two main sections, and is fully applicable at Register Transfer Level. It provides fast detection of faults, simple recovery by a flush of the pipeline, and allows a large prediction unit to be unprotected. We present the protection scheme at the core with the RISC-V instruction set. Simulations confirm that the protection can handle the injected faults. Synthesis shows that protection lowers the maximum frequency by only 3.9%. The area increase is only 108% and can be even lower. Total power consumption increased by 119%.
Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological ***,we present Microsnoop,a novel deep learning–based representation tool trained ...
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Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological ***,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy images using masked self-supervised *** can process various complex and heterogeneous images,and we classified images into three categories:single-cell,full-field,and batch-experiment *** benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy image representation ability,surpassing existing generalist and even several custom *** can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal ***,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing *** will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.
Parkinson’s disease (PD) is a progressive disorder of the nervous system that affects movement. Early prediction of PD can increase the chances of earlier intervention and delay the onset of the disease. Vocal impair...
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