Trusted execution environment (TEE) promises strong security guarantee with hardware extensions for security-sensitive tasks. Due to its numerous benefits, TEE has gained widespread adoption, and extended from CPU-onl...
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ISBN:
(纸本)9798350326598;9798350326581
Trusted execution environment (TEE) promises strong security guarantee with hardware extensions for security-sensitive tasks. Due to its numerous benefits, TEE has gained widespread adoption, and extended from CPU-only TEEs to FPGA and GPU TEE systems. However, existing TEE systems exhibit inadequate and inefficient support for an emerging (and significant) processing unit, NPU. For instance, commercial TEE systems resort to coarse-grained and static protection approaches for NPUs, resulting in notable performance degradation (10%-20%), limited (or no) multitasking capabilities, and suboptimal resource utilization. In this paper, we present a secure NPU architecture, known as sNPU, which aims to mitigate vulnerabilities inherent to the design of NPU architectures. First, sNPU proposes NPU Guarder to enhance the NPU's access control. Second, sNPU defines new attack surfaces leveraging in-NPU structures like scratchpad and NoC, and designs NPU Isolator to guarantee the isolation of scratchpad and NoC routing. Third, our system introduces a trusted software module called NPU Monitor to minimize the software TCB. Our prototype, evaluated on FPGA, demonstrates that sNPU significantly mitigates the runtime costs associated with security checking (from upto 20% to 0%) while incurring less than 1% resource costs.
Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep ...
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ISBN:
(纸本)9798400712456
Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks (DNNs) to directly model the patient's health status. Existing DNNs training protocols, including Impute-then-Regress Procedure and Jointly Optimizing of Impute-n-Regress Procedure, require the additional imputation models to reconstruction missing values. However, Impute-then-Regress Procedure introduces the risk of injecting imputed, non-real data into downstream clinical prediction tasks, resulting in power loss, biased estimation, and poorly performing models, while Jointly Optimizing of Impute-n-Regress Procedure is also difficult to generalize due to the complex optimization space and demanding data requirements. Inspired by the recent advanced literature of learnable prompt in the fields of NLP and CV, in this work, we rethought the necessity of the imputation model in downstream clinical tasks, and proposed Learnable Prompt as Pseudo-Imputation (PAI) as a new training protocol to assist EHR analysis. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all state-of-the-arts EHR analysis models on four real-world datasets across two clinical prediction tasks. Further experimental analysis indicates that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, as a plug-and-play protocol, PAI can be easily integrated into any existing or even imperceptible future EHR analysis models. The code of this work is deployed publicly available at https://***/MrBlankness/PAI to help the research community reproduce the results and assist the EHR analysis tasks.
With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal *** the promising performance of the heuristic algorithm on the route networ...
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With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal *** the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain *** this paper,the wind farm layout optimization problem is ***,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is *** provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be *** multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple *** proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization *** experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same *** ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.
The growth of service ecosystems, which include diversified services such as cloud and edge services, has led to a thriving service transaction market. However, service pricing remains a major obstacle to further prog...
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Accurate sales forecasting serves as a critical foundation for informed business decision-making and numerous methodologies have been advanced by scholars predominantly rely on complete datasets, however, in practice,...
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The outbreak of Covid-19 pandemic has caused millions of people infected and dead, resulting in global economy depression. Lessons learned to minimize the damage in an emerging pandemic is that timely tracking and rea...
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Dear editor,Estimations of nonlinear autoregressive(AR) models in the literature typically involve ergodic series. Based on this assumption,the asymptotic theory has been established accordingly(see [1–3]). However,t...
Dear editor,Estimations of nonlinear autoregressive(AR) models in the literature typically involve ergodic series. Based on this assumption,the asymptotic theory has been established accordingly(see [1–3]). However,this good property is not always true [4]. For example,
Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers’ program comprehension activities. Despite being studied for a long time, a bottleneck for existing...
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In the real world, the presence of missing values brings a challenge to the classification, and missing value imputation is often inseparably involved in classification for incomplete data. In this paper, we propose a...
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In the real world, the presence of missing values brings a challenge to the classification, and missing value imputation is often inseparably involved in classification for incomplete data. In this paper, we propose a tracking-removed neural network with graph information for solving the classification problem of incomplete data. Specifically, we redesign the hidden layer neuron structure of the autoencoder to improve the network's ability to mine associations among attributes. Graph information, which is used to analyze the similarity among samples, is introduced into the above tracking-removed neural network to further improve the network's imputation performance for missing values. On the basis of appropriate imputation, the output layer neurons of the proposed network are reorganized to achieve the mapping of incomplete data to classification. Moreover, we present a learning algorithm that regards the missing values as variables and co-trains them with the network parameters for the designed model. The proposed strategy enables all the existing attribute information in incomplete datasets to participate in network training, which promotes the network to match the classification and regression structure of incomplete data, thereby improving the classification performance of the model for incomplete data. The experiments on 6 public datasets verify the effectiveness of the proposed method.
Trinidad and Tobago’s public health sector currently uses paper records, posing challenges for both patients and healthcare providers. Transitioning to an electronic health record system with a patient portal is vita...
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Trinidad and Tobago’s public health sector currently uses paper records, posing challenges for both patients and healthcare providers. Transitioning to an electronic health record system with a patient portal is vital. Identifying and highlighting user requirements is crucial for this improvement, promising better healthcare services and data accessibility. With that being said, this study aims to identify key functional user requirements for a patient portal in Trinidad and Tobago, assess potential adoption barriers, and define credibility attributes for the credibility evaluation framework. The research question is focused on understanding user expectations and factors that may hinder adoption. The research methodology consisted of three main steps. First, a comprehensive review of existing standards, guidelines, and journals on patient portals was conducted to gather insights into best practices and key software credibility attributes. Second, an online survey was designed to collect data from 390 citizens of Trinidad and Tobago aged 18 years and over. The survey focused on gathering user preferences, expectations, and concerns regarding the implementation of patient portal adoption. Lastly, the survey responses were analyzed to identify key functional user requirements for the system. The results of the study revealed 16 key functional user requirements for the electronic health records system, providing insights into the features and functionalities expected by users. Among the identified adoption barriers, resistance to change and inadequate cybersecurity laws were found to be significant factors that may hinder the successful implementation of the system. Additionally, 15 credibility attributes were selected based on the use’r requirements to establish the system’s reliability and trustworthiness. Understanding user requirements and addressing adoption barriers are crucial for developing an effective patient portal system in Trinidad and Tobago’s public healt
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