Trusted computing technology, as a new type of information security technology, has been applied in various fields. In order to ensure the safe and stable operation of industrial and power automation systems, it is pr...
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This discussion explores the evolution of serverless computing, highlighting its origins, rise in importance, and the challenges it presents. Serverless computing represents a significant shift from traditional, hardw...
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This discussion explores the evolution of serverless computing, highlighting its origins, rise in importance, and the challenges it presents. Serverless computing represents a significant shift from traditional, hardware-dependent environments to cloud-based, code-centric architectures. The concept of serverless emerged with the introduction of platforms like Google App Engine and AWS Lambda, freeing developers from server management concerns and enabling them to focus solely on code development. The integration of serverless computing with DevOps practices is examined, emphasizing the role of Infrastructure as Code (IaC), version control, continuous integration (CI), and continuous deployment (CD). These practices are essential for streamlining the development and deployment of serverless applications, enhancing consistency, and promoting collaboration between development and operations teams. Furthermore, the discussion addresses the specific challenge of cold starts in serverless computing and how DevOps practices can help mitigate their impact. Strategies such as proactive monitoring, scaling policies, scheduled warm-up, provisioned concurrency, performance testing, automation, and continuous optimization are presented as solutions to minimize cold start delays. Lastly, the importance of Testing automation within the CI/CD pipeline is highlighted. Through rapid feedback, comprehensive testing, consistency, regression testing, and scalability, Testing automation ensures the quality and reliability of software in a fast-paced development environment, aligning with the principles of DevOps.
With the rapid development of the cutting edge cloud computing technology, millions of vulnerabilities have been identified, there is a growing concern that organizations should devote plenty of time and lots of resou...
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ISBN:
(纸本)9798350381993;9798350382006
With the rapid development of the cutting edge cloud computing technology, millions of vulnerabilities have been identified, there is a growing concern that organizations should devote plenty of time and lots of resources to secure. The overarching objective of remediation is to prioritize the vulnerabilities. Hence, define the severity and the urgency of the vulnerabilities and remediate them automatically is very important. Although the recognized Common Vulnerability Scoring System (CVSS) 4.0 method addresses this issues partly, they are difficult to be implemented in practices on the cloud because of the complication and lack of risk based factors. To this end, we propose a Cost-effective Massive automation Method of Vulnerability Analysis and Remediation Based on Cloud Native Framework. Specifically, considering that the current CVSS is more like a severity of vulnerabilities, we design a novel formula to define the urgency of vulnerabilities. The formula takes the advantaged of the capabilities of modern cloud-based infrastructure and simplifies the CVSS. Besides, we propose an algorithm of risk reduction by leveraging the cloud native security capabilities, which cut down unnecessary patching time and workload. Particularly, in order to remediation the risk on the cloud, we implement an automatic scheme to harden the vulnerabilities by invoking the cloud native APIs based on the Security Orchestration, automation and Response (SOAR) platform. Finally, we conduct comprehensive experiments to evaluate our system. Experimental results demonstrate the effectiveness of ours approach has a high ratio of urgency risk recognition of 99.24%. Meanwhile, ours approach shows a maximum risk reduction by downgrade the fixable vulnerability with a average of 79% risk reduction rate in application level and 99% of risk reduction rate in operating system level respectively. As a result, our approach lightens the workload of patching greatly in the real cloud computing environ
The identification and characterization of sitting postures are crucial for various applications, particularly in addressing issues related to postural abnormalities, and musculoskeletal symptoms. This study presents ...
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ISBN:
(纸本)9798350370058;9798350370164
The identification and characterization of sitting postures are crucial for various applications, particularly in addressing issues related to postural abnormalities, and musculoskeletal symptoms. This study presents a novel seat-mounted sitting posture detection system comprising 15 Pressure Sensitive Conductive (PSC) sensors. These sensors are strategically positioned within a seat, that interfaces with an edge computing system to run a convolutional neural network (CNN) model and predict one among 8 different sitting postures. A dataset consisting of all 15 sensory time-series signals was captured for each of the 8 postures, for training the model. The trained CNN model demonstrated superior accuracy compared to SVM, KNN, ANN, and CNN+LSTM models, leveraging spatio-temporal features for precise posture identification. High classification accuracy was achieved when applied to an unseen dataset. This cameraless system offers real-time sitting posture prediction without compromising user privacy. Its adaptability and scalability make it an innovative solution for medical, domestic, and clinical applications, aiding in posture-related concerns. The dataset, CNN model, and source-codes are made freely available for easy adoption and further usage by the designers and scientific community.
The word InsurTech has been around for a decade now however, it has recently gained a much crucial position in the insurance industry, with the development ofnew-age Technological advancements such as Artificial Intel...
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The word InsurTech has been around for a decade now however, it has recently gained a much crucial position in the insurance industry, with the development ofnew-age Technological advancements such as Artificial Intelligence, Blockchain, Internet of Things (IoT), Big data, Augmented and Virtual reality (AR and VR) and Robotics Process automation (RPA). Insurtech is proving to be a digital change that modernizes the Traditional insurance sector *** research focuses on drawing a comparison between the state of InsurTech in Developed and developing countries using claims processing, Underwriting automation, and the recent changes that have come into play. Data from four InsurTechsfrom the US have also been taken Namely - Haven life, Ethos life, Fabric by Gerber life, and Bestow.
Taking the development of public computing service systems as the background requirement, combined with Digital Twin related technologies, the design of computing units is carried out. Research is carried out from the...
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In the paper a problem related to the field of edge computing is considered. We discuss a situation where an autonomous energy charging station is used to perform the process of charging a fleet of electric vehicles. ...
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ISBN:
(纸本)9798350311075
In the paper a problem related to the field of edge computing is considered. We discuss a situation where an autonomous energy charging station is used to perform the process of charging a fleet of electric vehicles. The station consists of two modules: a computing and an executive one. The computing module is equipped with a variable speed processor, and is responsible to find a schedule of the charging jobs. We discuss the model of the system and the resulting scheduling problem, as well and we propose a few basic approaches for solving it. Some conclusions and directions for future research are given.
This paper presents a fully autonomous assembly line for custom cubes with two sub-block options (plastic and aluminium). Users can design their cubes through an Android app or website interface. The system retrieves ...
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ISBN:
(纸本)9798350385939;9798350385922
This paper presents a fully autonomous assembly line for custom cubes with two sub-block options (plastic and aluminium). Users can design their cubes through an Android app or website interface. The system retrieves orders, processes them based on deadlines, and dispenses the required sub-blocks. A quality inspection station with computer vision and machine learning ensures that defect-free parts are used. Cobots handle sub-block transportation and manipulation throughout the assembly process, with a dedicated station for inserting pins when necessary. A digital Shadow model facilitates performance analysis and optimization. Cloud-based platforms manage data, order processing, and remote access. Finally, an Automated Storage and Retrieval System (ASRS) manages cube storage and delivery. This research demonstrates the potential of combining automation, machine learning, and real-time monitoring for efficient custom product assembly. The approach offers applications in various industries and lays the groundwork for future research on expanding customization options and production flow optimization.
Traditional power automation sensitive data classification algorithms are prone to generalization errors, resulting in low geometric mean of data classification. Therefore, it is necessary to design a sensitive data c...
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The proceedings contain 190 papers. The topics discussed include: survey on disaster management using VANET;diabetes diagnosis using different data pre processing techniques;a review on possibilities of hearing loss a...
ISBN:
(纸本)9781538669471
The proceedings contain 190 papers. The topics discussed include: survey on disaster management using VANET;diabetes diagnosis using different data pre processing techniques;a review on possibilities of hearing loss and implantable hearing devices for teenagers;SDN-Assisted IoT architecture: a review;conformal microstrip filter design using complementary split ring resonator;revisiting cloud security threats: replay attack;fake news detection using a deep neural network;design patterns in GIS application design;application of machine learning in disease prediction;and prediction of climate variable using multiple linear regression.
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