New Proposed Robust, Scalable and Secure Network Cloud Computing Storage Architecture

Cloud computing describes highly scalable computing resources provided as an external service via the internet. Economically, the main feature of cloud computing is that customers only use what they need, and only pay for what they actually use. Resources are available to be accessed from the cloud at any time, and from any location via the internet. There’s no need to worry about how things are being maintained behind the scenes—you simply purchase the IT service you require. This new, web-based generation of computing utilizes remote servers for data storage and management. One of the challenging issues tackled in the cloud computing is the security of data stored in the service providers’ site. In this paper, we propose a new architecture for secure data storage in such a way that users’ data are encrypted and split into various cipher blocks and distributed among different service providers site rather than solely depend on single provider for data storage. This architecture ensures better reliability, availability, scalability and security.

Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

Eye health has become a global health concern and attracted broad attention.Over the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely.However,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model complexity.To alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular diseases.In MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer parameters.We conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 datasets.The results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and MixNets.Specifically,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy information.At present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of practicability.To this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model certification.The experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

Malware Attacks Detection in IoT Using Recurrent Neural Network(RNN)

IoT(Internet of Things)devices are being used more and more in a variety of businesses and for a variety of tasks,such as environmental data collection in both civilian and military situations.They are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing power.In this study,we investigate the possibility of detecting IoT malware using recurrent neural networks(RNNs).RNNis used in the proposed method to investigate the execution operation codes of ARM-based Internet of Things apps(OpCodes).To train our algorithms,we employ a dataset of IoT applications that includes 281 malicious and 270 benign pieces of software.The trained model is then put to the test using 100 brand-new IoT malware samples across three separate LSTM settings.Model exposure was not previously conducted on these samples.Detecting newly crafted malware samples with 2-layer neurons had the highest accuracy(98.18%)in the 10-fold cross validation experiment.A comparison of the LSTMtechnique to other machine learning classifiers shows that it yields the best results.

微尺度气体润滑的非平衡流效应分析

微尺度气体润滑中,引起微尺度气体润滑压力降低的原因在于润滑流的非平衡流效应。微尺度气体润滑的非平衡流效应不仅会引起壁面气流速度滑移,而且会引起润滑气流热通量的变化。本文首先给出了基于Boltzmann矩方程的微尺度气体润滑模型(矩方程润滑模型);其次,与FK润滑模型和直接模拟Monte Carlo方法的分析结果比较,验证基于矩方程润滑模型的有效性;再次,基于矩方程润滑模型,分析不同轴承数情形下微尺度气体润滑的非平衡流效应。研究表明,随着轴承数的增大,微尺度气体润滑的非平衡流效应增强,表现为壁面速度滑移及热通量均增大。与壁面速度滑移的变化相反,随着轴承数的增大,非平衡流效应引起气膜压力及承载力的变化较小。这一反常现象的原因在于气体的可压缩性。

基于协同神经网络的仿射不变性检索相似性度量方法(英文)

根据协同神经网络 ,提出了一种新的控制参数———序矢量 ,并着重研究了其在图像检索方面的相关性质 .在此基础上 ,针对商标图像检索 ,提出了一种高效的基于元素的仿射不变性度量方法 .实验表明 ,该检索算法抗噪、抗缺损能力强 ,同时对于平移、旋转、缩放具有不变性 .

浅析协同效应的理论模型与应用拓展

从基础物理化学分析入手,结合生物化学、生物无机化学、酶学和超分子化学等多学科实例,对协同效应的原理和应用进行系统性的全面阐述。以期对教学与相关科研有帮助。

Immersed Interface Finite Element Methods for Elasticity Interface Problems with Non-Homogeneous Jump Conditions

In this paper,a class of new immersed interface finite element methods (IIFEM) is developed to solve elasticity interface problems with homogeneous and non-homogeneous jump conditions in two dimensions.Simple non-body-fitted meshes are used.For homogeneous jump conditions,both non-conforming and conforming basis functions are constructed in such a way that they satisfy the natural jump conditions. For non-homogeneous jump conditions,a pair of functions that satisfy the same non-homogeneous jump conditions are constructed using a level-set representation of the interface.With such a pair of functions,the discontinuities across the interface in the solution and flux are removed;and an equivalent elasticity interface problem with homogeneous jump conditions is formulated.Numerical examples are presented to demonstrate that such methods have second order convergence.

Non-equilibrium turbulent phenomena in the ?ow over a backward-facing ramp

Non-equilibrium turbulence phenomena have raised great interests in recent years. Significant efforts have been devoted to non-equilibrium turbulence properties in canonical flows, e.g., grid turbulence, turbulent wakes, and homogeneous isotropic turbulence(HIT). The non-equilibrium turbulence in non-canonical flows, however, has rarely been studied due to the complexity of the flows. In the present contribution, a directnumerical simulation(DNS) database of a turbulent flow is analyzed over a backwardfacing ramp, the flow near the boundary is demonstrated, and the non-equilibrium turbulent properties of the flow in the wake of the ramp are presented by using the characteristic parameters such as the dissipation coefficient C and the skewness of longitudinal velocity gradient Sk, but with opposite underlying turbulent energy transfer properties. The equation of Lagrangian velocity gradient correlation is examined, and the results show that non-equilibrium turbulence is the result of phase de-coherence phenomena, which is not taken into account in the modeling of non-equilibrium turbulence. These findings are expected to inspire deeper investigation of different non-equilibrium turbulence phenomena in different flow conditions and the improvement of turbulence modeling.

Peptidomic analysis of whey protein hydrolysates and prediction of their antioxidant peptides

Enzymatic hydrolysis of proteins is a breakdown process of peptide bond in proteins,releasing some peptides with potential biological functions.Previous studies on enzymatic hydrolysis of whey proteins have not identified the complete peptide profiles after hydrolysis.In this study,we reconstructed a profile of peptides from whey hydrolysates with two enzymes and different processing conditions.We also developed an ensemble machine learning predictor to classify peptides obtained from whey hydrolysis.A total of 2572 peptides were identified over three process conditions with two enzymes in duplicate.499 peptides were classified and chosen as potential antioxidant peptides from whey proteins.The peptides classified as antioxidants in the hydrolysates had a proportion of 13.1%-24.5%regarding all peptides identified.These results facilitate the selection of promising peptides involved in the antioxidant properties during the enzymatic hydrolysis of whey proteins,aiding the discovery of novel antioxidant peptides.

Telebroadcasting of a Cat-like State via Local Operations

The quantum telebroadcasting of a cat-like state in combination with the quantum teleportation and the local copying of entanglement is presented. This gives a general way of distributing entanglement among distant parties. All of the operations of our scenario are local and within the reach of current technology.

Non-equilibrium turbulent phenomena in transitional flat plate boundary-layer flows

Many recent laboratory experiments and numerical simulations support a non-equilibrium dissipation scaling in decaying turbulence before it reaches an equilibrium state.By analyzing a direct numerical simulation(DNS)database of a transitional boundary-layer flow,we show that the transition region and the non-equilibrium turbulence region,which are located in different streamwise zones,present different non-equilibrium scalings.Moreover,in the wall-normal direction,the viscous sublayer,log layer,and outer layer show different non-equilibrium phenomena which differ from those in grid-generated turbulence and transitional channel flows.These findings are expected to shed light on the modelling of various types of non-equilibrium turbulent flows.

A Quantitative Security Metric Model for Security Controls:Secure Virtual Machine Migration Protocol as Target of Assessment

Quantitative security metrics are desirable for measuring the performance of information security controls. Security metrics help to make functional and business decisions for improving the performance and cost of the security controls. However, defining enterprise-level security metrics has already been listed as one of the hard problems in the Info Sec Research Council's hard problems list. Almost all the efforts in defining absolute security metrics for the enterprise security have not been proved fruitful. At the same time, with the maturity of the security industry, there has been a continuous emphasis from the regulatory bodies on establishing measurable security metrics. This paper addresses this need and proposes a relative security metric model that derives three quantitative security metrics named Attack Resiliency Measure(ARM), Performance Improvement Factor(PIF), and Cost/Benefit Measure(CBM) for measuring the performance of the security controls. For the effectiveness evaluation of the proposed security metrics, we took the secure virtual machine(VM) migration protocol as the target of assessment. The virtual-ization technologies are rapidly changing the landscape of the computing world. Devising security metrics for virtualized environment is even more challenging. As secure virtual machine migration is an evolving area and no standard protocol is available specifically for secure VM migration. This paper took the secure virtual machine migration protocol as the target of assessment and applied the proposed relative security metric model for measuring the Attack Resiliency Measure, Performance Improvement Factor, and Cost/Benefit Measure of the secure VM migration protocol.

Land cover classification in a mixed forest-grassland ecosystem using LResU-net and UAV imagery

Using an unmanned aerial vehicle (UAV) paired with image semantic segmentation to classify land cover within natural vegetation can promote the development of forest and grassland field. Semantic segmentation normally excels in medical and building classification, but its usefulness in mixed forest-grassland ecosystems in semi-arid to semi-humid climates is unknown. This study proposes a new semantic segmentation network of LResU-net in which residual convolution unit (RCU) and loop convolution unit (LCU) are added to the U-net framework to classify images of different land covers generated by UAV high resolution. The selected model enhanced classification accuracy by increasing gradient mapping via RCU and modifying the size of convolution layers via LCU as well as reducing convolution kernels. To achieve this objective, a group of orthophotos were taken at an altitude of 260 m for testing in a natural forest-grassland ecosystem of Keyouqianqi, Inner Mongolia, China, and compared the results with those of three other network models (U-net, ResU-net and LU-net). The results show that both the highest kappa coefficient (0.86) and the highest overall accuracy (93.7%) resulted from LResU-net, and the value of most land covers provided by the producer’s and user’s accuracy generated in LResU-net exceeded 0.85. The pixel-area ratio approach was used to calculate the real areas of 10 different land covers where grasslands were 67.3%. The analysis of the effect of RCU and LCU on the model training performance indicates that the time of each epoch was shortened from U-net (358 s) to LResU-net (282 s). In addition, in order to classify areas that are not distinguishable, unclassified areas were defined and their impact on classification. LResU-net generated significantly more accurate results than the other three models and was regarded as the most appropriate approach to classify land cover in mixed forest-grassland ecosystems.

Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart cities.IoV originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV implementation.Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements.Trust plays a vital role in ensuring security,especially during vehicle to vehicle communication.Vehicular networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust protocols.Current TM schemes are inflexible and static.Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks.The vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical messages.The vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security threats.Moreover,internet involvement makes it more vulnerable to cyberattacks.The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics.Machine learning is the best solution to utilize the big data produced by vehicle sensors.To handle the uncertainty Bayesian machine learning statistical model is used.The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available.The results indicated better performance than existing TM methods.Furthermore,for future work,a high-level machine learning model is proposed.

A Secure NDN Framework for Internet of Things Enabled Healthcare

Healthcare is a binding domain for the Internet of Things(IoT)to automate healthcare services for sharing and accumulation patient records at anytime from anywhere through the Internet.The current IP-based Internet architecture suffers from latency,mobility,location dependency,and security.The Named Data Networking(NDN)has been projected as a future internet architecture to cope with the limitations of IP-based Internet.However,the NDN infrastructure does not have a secure framework for IoT healthcare information.In this paper,we proposed a secure NDN framework for IoTenabled Healthcare(IoTEH).In the proposed work,we adopt the services of Identity-Based Signcryption(IBS)cryptography under the security hardness Hyperelliptic Curve Cryptosystem(HCC)to secure the IoTEH information in NDN.The HCC provides the corresponding level of security using minimal computational and communicational resources as compared to bilinear pairing and Elliptic Curve Cryptosystem(ECC).For the efficiency of the proposed scheme,we simulated the security of the proposed solution using Automated Validation of Internet Security Protocols and Applications(AVISPA).Besides,we deployed the proposed scheme on the IoTEH in NDN infrastructure and compared it with the recent IBS schemes in terms of computation and communication overheads.The simulation results showed the superiority and improvement of the proposed framework against contemporary related works.

Compression of Left Main Coronary Artery in Patients with Pulmonary Artery Aneurysm and Pulmonary Hypertension

Background: Pulmonary artery aneurysm (PAA) is an unusual finding and its association with left main coronary (LMCA) compression is even more infrequent. Cardiac CT evaluates of presence and size of PAA and the degree of LMCA compression. The aim of this study is to describe two cases of adults with compression of LMCA with PAA associated with PDA and pulmonary hypertension. Case presentation: The first case is a 27-year-old man with PAA (78 mm diameter) and LMCA compression of 70% between the aortic sinus and the PAA. He presented angina as a manifestation of the LMCA compression. During follow-up the patient died. The second case is a 28-year-old man with PAA (110 mm diameter) that compresses LMCA in 55%, he rejected surgical treatment, but he is in close follow-up with medical treatment. Conclusion: Cardiac computed tomography played an important role both in the diagnosis and identification of high-risk PAA patients.

Towards a Theoretical Framework of Autonomous Systems Underpinned by Intelligence and Systems Sciences

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems sciences.This paper explores the intelligent and mathematical foundations of autonomous systems.It focuses on structural and behavioral properties that constitute the intelligent power of autonomous systems.It explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive process.The properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems engineering.Emerging paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are described.Advances in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

Health Systems as Complex Systems

Health systems are paradigmatic examples of human organizations that blend a multitude of different professional and disciplinary features within a critically performance environment. Communication failure and defective processes in health systems have a tremendous impact in society, both in the financial and human aspects. Traditionally, health systems have been regarded as linear hierarchic structures. However, recent developments in the sciences of complexity point out to health systems as complex entities governed by non-linear interaction laws, self-organization and emergent phenomena. In this work we review some aspects of complexity behind health systems and how they can be applied to improve the performance of healthcare organizations.