In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients1. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records2–17 and using acute kidney injury—a common and potentially life-threatening condition18—as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests9. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment. A deep learning approach that predicts the risk of acute kidney injury may help to identify patients at risk of health deterioration within a time window that enables early treatment.

A clinically applicable approach to continuous prediction of future acute kidney injury

Elasticity is a fundamental property in cloud computing that has recently witnessed major developments This article reviews both classical and recent elasticity solutions and provides an overview of containerization, a new technological trend in lightweight virtualization It also discusses major issues and research challenges related to elasticity in cloud computing We comprehensively review and analyze the proposals developed in this field We provide a taxonomy of elasticity mechanisms according to the identified works and key properties Compared to other works in literature, this article presents a broader and detailed analysis of elasticity approaches and is considered as the first survey addressing the elasticity of containers

/pdf/elasticity-in-cloud-computing-state-of-the-art-and-research-32fme9yoqc.pdf

Elasticity in Cloud Computing: State of the Art and Research Challenges

Cloud computing affords lot of resources and computing facilities through Internet Cloud systems attract many users with its desirable features In spite of them, Cloud systems may experience severe security issues Thus, it is essential to create an Intrusion Detection System (IDS) to detect both insider and outsider attacks with high detection accuracy in cloud environment This work proposes an anomaly detection system at the hypervisor layer named Hypervisor Detector that uses a hybrid algorithm which is a mixture of Fuzzy C-Means clustering algorithm and Artificial Neural Network (FCM-ANN) to improve the accuracy of the detection system The proposed system is implemented and compared with Naive Bayes classifier and Classic ANN algorithm The DARPA's KDD cup dataset 1999 is used for experiments Based on extensive theoretical and performance analysis, it is evident that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate even for low frequent attacks thereby outperforming Naive Bayes classifier and Classic ANN

Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN

As an economic and scalable solution of providing interactive and adaptive media content across different devices, cloud-based media processing has recently attracted lots of attention from both academic and industry. Within a media cloud, a large number of media processing tasks will be dynamically spawn to run in parallel and then share resource with each other in the cloud. In such a dynamic and shared environment, how to appropriately provision cloud resource to different tasks with regards to both system efficiency and quality of service remains a challenging issue. Existing resource allocation algorithms consider generic task and only focus on maximizing system efficiency, but failed to meet the QoS requirement of media tasks. In this paper, we investigate the relationship between the properities of media tasks and their resource comsumption and accordingly propose a dynamic resource allocation solution. Our solution leverages machine learning technique to predict resource requirement and survival functions to prevent QoS degradation.

Dynamic resource allocation for cloud-based media processing

Cloud computing provides user-requested services that are reliable, dynamic, flexible and efficient. In order to offer such guaranteed services to cloud users, effective resource allocation strategies must be implemented. The methodology used should also confirm to the Service Level Agreement (SLA) drawn between the customer and the service provider. This work presents a study of such resource allocation strategies in cloud computing. The strategies include resource requirements prediction algorithms and resource allocation algorithms. This works studies the various resource allocation techniques utilized in cloud computing and makes a comparative study of the merits and demerits of each technique. This study aims to identify an efficient resource allocation strategy that utilizes resources effectively in the resource constrained environment of cloud computing.

A survey on resource allocation strategies in cloud computing

Coronavirus is a large family of viruses that affects humans and damages respiratory functions ranging from cold to more serious diseases such as ARDS and SARS. But the most recently discovered virus causes COVID-19. Isolation at home or hospital depends on one’s health history and conditions. The prevailing disease that might get instigated due to the existence of the virus might lead to deterioration in health. Therefore, there is a need for early detection of the virus. Recently, many works are found to be observed with the deployment of techniques for the detection based on chest X-rays. In this work, a solution has been proposed that consists of a sample prototype of an AI-based Flask-driven web application framework that predicts the six different diseases including ARDS, bacteria, COVID-19, SARS, Streptococcus, and virus. Here, each category of X-ray images was placed under scrutiny and conducted training and testing using deep learning algorithms such as CNN, ResNet (with and without dropout), VGG16, and AlexNet to detect the status of X-rays. Recent FPGA design tools are compatible with software models in deep learning methods. FPGAs are suitable for deep learning algorithms to make the design as flexible, innovative, and hardware acceleration perspective. High-performance FPGA hardware is advantageous over GPUs. Looking forward, the device can efficiently integrate with the deep learning modules. FPGAs act as a challenging substitute podium where it bridges the gap between the architectures and power-related designs. FPGA is a better option for the implementation of algorithms. The design attains 121µW power and 89 ms delay. This was implemented in the FPGA environment and observed that it attains a reduced number of gate counts and low power.

https://downloads.hindawi.com/journals/misy/2022/2110785.pdf

FPGA-Based Deep Learning Models for Analysing Corona Using Chest X-Ray Images

Blockchain in social networking

Over the last few decades, forest fires are increased due to deforestation and global warming. Many trees and animals in the forest are affected by forest fires. Technology can be efficiently utilized to solve this problem. Forest fire detection is inevitable for forest fire management. The purpose of this work is to propose deep learning techniques to predict forest fires, which would be cost-effective. The mixed learning technique is composed of YOLOv4 tiny and LiDAR techniques. Unmanned aerial vehicles (UAVs) are promising options to patrol the forest by making them fly over the region. The proposed model deployed on an onboard UAV has achieved 1.24 seconds of classification time with an accuracy of 91% and an F1 score of 0.91. The onboard CPU is able to make a 3D model of the forest fire region and can transmit the data in real time to the ground station. The proposed model is trained on both dense and rainforests in detecting and predicting the chances of fire. The proposed model outperforms the traditional methods such as Bayesian classifiers, random forest, and support vector machines.

/pdf/early-detection-of-forest-fire-using-mixed-learning-2mrcj5nr.pdf

Early Detection of Forest Fire Using Mixed Learning Techniques and UAV

Data is gathered from millions of patients across various healthcare organizations could be framed as electronic health record (EHR). It comprises of data in various forms that include laboratory results, diagnosis reports, medical images, demographic information about patient, and clinical notes. Transformation in the health care could be done with the help of deep learning techniques since these techniques outperform than the conventional machine learning methods. In addition, a huge and complex dataset gets generated day by day, and thus, it enables the need for the deployment of deep learning models. It is examined that the clinical decision in certain fields that vary from analysis of medical research issues and to suggest as well as prioritize the treatments in order to detect abnormalities, and in addition, the identification of genomic markers in tissue samples is done. Deep learning is applied in EHR system to derive patient representation to provide enhanced clinical predictions, and augments clinical decision systems. Moreover, it is applied in order to detect the disease in the earlier stages, determining the clinical risk, forecasting the future need of regular checkups and in addition prediction of hospitalization in the near future if required. According to statistical report, the deep learning software market is estimated to reach 180 million US dollars in size by 2020. The availability of a huge amount of clinical information particularly EHR has stimulated the growth of deep learning techniques which assist in rapid analysis of patient’s data. Nowadays, EHR is being incorporated with deep learning techniques and tools into EHR systems which provide deep insights for health outcomes. The market for EHR deep learning tools is predicted to exceed $34 billion by the mid of 2020s, motivated massively by an emerging desire to automate tasks and provide deeper insights into clinical issues. The global health IT market is anticipated to be worth a surprising $223.16 billion by 2023, driven in part by deep learning. EHR is a thriving research domain for deep learning researchers since it helps to achieve higher accuracies in medical conditions. Clinical information could be extracted by applying deep learning techniques. The extraction is of various types, namely retrieval of single idea, extracting the association, time-based extraction, and advancement of abbreviation. In EHR representation learning process, concept and patient representations are included to obtain detailed analysis and decisive predictive functionalities. Outcome prediction in deep EHR is categorized as static and temporal prediction to predict patient outcomes. The clinical deep learning is more interpretable under the category of maximum activation, constraints, qualitative clustering, and mimic learning in EHR analysis. The objective of this chapter is to provide an insight into the digital transformation of EHR through deep learning techniques. It discusses the deep learning framework and challenges that occur during the development of deep learning models for EHR. Moreover, it also focuses on the deep learning prediction techniques for various diseases and recent advancements on deep learning techniques in the aspect of providing precise medicine and future generation health care.

D. Sumathi

Papers

A survey on resource allocation strategies in cloud computing

FPGA-Based Deep Learning Models for Analysing Corona Using Chest X-Ray Images

Blockchain in social networking

Early Detection of Forest Fire Using Mixed Learning Techniques and UAV

Deep Learning Techniques for Electronic Health Record (EHR) Analysis