Recognizing human body motions opens possibilities for real-time observation of users' daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capab...
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Recognizing human body motions opens possibilities for real-time observation of users' daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capabilities in detecting movements, no prior work could detect full-body motions with wireless devices. Here, we introduce a soft electronic textile-integrated system, including nanomaterials and flexible sensors, which enables real-time detection of various full-body movements using the combination of a wireless sensor suit and deep-learning-based cloud computing. This system includes an array of a nanomembrane, laser-induced graphene strain sensors, and flexible electronics integrated with textiles for wireless detection of different body motions and workouts. With multiple human subjects, we demonstrate the system's performance in real-time prediction of eight different activities, including resting, walking, running, squatting, walking upstairs, walking downstairs, push-ups, and jump roping, with an accuracy of 95.3%. The class of technologies, integrated as full body-worn textile electronics and interactive pairing with smartwatches and portable devices, can be used in real-world applications such as ambulatory health monitoring via conjunction with smartwatches and feedback-enabled customized rehabilitation workouts.
The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resource...
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The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.
Pervasive computing requires dynamic, energy-efficient cloud architecture to deploy services in VMs to distributed computing nodes. This mapping must ensure the service level agreement (SLA) runs VMs without disruptio...
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Pervasive computing requires dynamic, energy-efficient cloud architecture to deploy services in VMs to distributed computing nodes. This mapping must ensure the service level agreement (SLA) runs VMs without disruption. This paper presents an energy-efficient metaheuristic Rock Hyrax algorithm-based cloud VM migration architecture. The male Rock Hyraxes find food and ensure the colony's safety. His behavior has been mimicked in our proposed algorithm for finding energy-efficient compute nodes. A multi-objective VM migration function considers job submission deadlines. The proposed method reduces SLA violations and energy utilization while optimizing resource utilization. When evaluating the project, makespan, energy efficiency, and SLA violations were considered. The proposed algorithm is simulated on the cloudSim simulator considering both resources and jobs dynamic in nature. The suggested strategy outperforms ant colony optimization, particle swarm optimization, cuckoo optimization, and modified gray wolf optimization. The migration technique improves resource utilization by 18%, makespan time by 5%, SLA violation by 13%, and energy usage by 15%.
Cross-modal retrieval can break through the limitations of modalities and carry out information retrieval across data of different modalities to meet the needs of users in obtaining multi-modal correlation retrieval. ...
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Cross-modal retrieval can break through the limitations of modalities and carry out information retrieval across data of different modalities to meet the needs of users in obtaining multi-modal correlation retrieval. cloud computing has the advantages of high efficiency and low cost, but data security hinders its development. While cloud computing offers high efficiency and cost-effectiveness, concerns surrounding data security impede its full potential. Privacy-preserving cross-modal retrieval emerges as a viable solution, catering to users' demands for efficient retrieval while safeguarding data confidentiality. However, a major challenge still exists in this field: how to bridge the inherent semantic gap within heterogeneous and chaotic information. To address this challenge, this paper proposes dual-branch networks for privacy-preserving cross-modal retrieval in cloud computing. Firstly, a dual-branch feature extraction network of encrypted image-text is constructed, enhancing the extraction of meaningful features from encrypted data. Secondly, a cross-modal alignment method is designed to eliminate the heterogeneous gap between different modalities through the alignment within and between modalities. Finally, to fully exploit the storage and computing advantages of cloud computing, both encrypted data and the cross-modal feature extractor are deployed to the cloud. Leveraging the dynamic update capabilities of cloud-stored encrypted data enables continuous model refinement, enhancing retrieval accuracy while reducing the storage and computational burdens on data owners. Extensive experiments conducted on the publicly available benchmark image-text dataset Wikipedia indicate that, compared to existing methods, our approach achieves improvements of 5.4%, 1%, 1.6%, and 20.1% in the four metrics of image-to-text (i2t), text-to-image (t2i), image-to-all (i2all), and text-to-all (t2all), respectively.
With great agility, availability, scalability, and resilience, cloud computing has recently become one of the most popular platforms for offering compute, storage, and analytics services to businesses and end users on...
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With great agility, availability, scalability, and resilience, cloud computing has recently become one of the most popular platforms for offering compute, storage, and analytics services to businesses and end users on a pay-per-use basis. This eliminates the need to set up a high-performance computing platform by giving people and organizations access to a vast pool of high processing resources. Task scheduling in cloud computing has become a highly valued resource for researchers in recent years. Nonetheless, task scheduling is generally considered an NP-hard problem. In this paper a hybrid algorithm IVPTS designed for dependable cloud computing task scheduling and VM placement. We address the optimization of task execution time and resource balance concurrently by integrating an improved particle swarm optimization algorithm along with fuzzy framework. We then conduct a new algorithm for VM placement problem, with using this strategy along with PSO-Fuzzy scheduling we can improve the cloud computing infrastructure. The results demonstrate that compared to the ELBA and ERA algorithm, IVPTS reduces makespan by 11% and enhances energy consumption by 15%. Additionally, compared to GWO and PSO algorithms, IVPTS achieves 13%, 5% reduction in makespan, and improves energy consumption by 12%, 5%. Moreover, the simulations indicate that the proposed method can improve the cloud environment's reliability. Furthermore, compared to other methods, IVPTS exhibits a better degree of imbalance and makespan results.
Identity-based encryption with equality test (IBEET) is proposed to check whether the underlying messages of ciphertexts, even those encrypted with different public keys, are the same or not without decryption. Since ...
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Identity-based encryption with equality test (IBEET) is proposed to check whether the underlying messages of ciphertexts, even those encrypted with different public keys, are the same or not without decryption. Since people prefer to encrypt before outsourcing their data for privacy protection nowadays, the research of IBEET on cloud computing applications naturally attracts attention. However, we claim that the existing IBEET schemes suffer from the illegal trapdoor sharing problem caused by the inherited key escrow problem of the Identity-Based Encryption (IBE) mechanism. In traditional IBEET, the private key generator (PKG) with the master secret key generates trapdoors for all authorized cloud servers. Considering the reality in practice, the PKG is usually not fully trusted. In this case, the Private-Key Generator (PKG) may generate, share, or even sell any trapdoor without any risk of being caught, or not being held accountable, which may lead to serious consequences such as the illegal sharing of a gene bank's trapdoors. In this paper, to relieve the illegal trapdoor sharing problem in IBEET, we present a new notion, called IBEET Supporting Accountable Authorization (IBEET-AA). In IBEET-AA, if there is a disputed trapdoor, the generator will be distinguished among the PKG and suspected testers by an additional tracing algorithm. For the additional tracing function, except for the traditional indistinguishability (IND) and one-way (OW) security models in IBEET, we define three more security models to protect the tracing security against dishonest authorizers, PKG, and testers, respectively. Based on Gentry's IBE scheme, we instantiate IBEET-AA and give a specific construction along with a formalized security proof with random oracles.
cloud computing has achieved great commercial success to date, but its associated risks may impede firms from adopting it more effectively. This paper addresses the issue from the perspective of firms that use cloud c...
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cloud computing has achieved great commercial success to date, but its associated risks may impede firms from adopting it more effectively. This paper addresses the issue from the perspective of firms that use cloud computing and argues that the modularization of firms' internal IT systems play an important role in enabling their adoption of cloud services. We performed detailed empirical analyses employing a dataset containing 457 firms classified as small and medium-sized enterprises (SMEs) as well as large enterprises. Our empirical results mainly suggest that internal IT modularity aimed for organization-wide, top-down strategic business transformation can help firms better adopt application-level cloud services regardless of their firm size. Bottom-up non-strategic internal IT modularity siloed in individual projects can foster the use of server-level cloud services for SMEs, but not for large enterprises. Our findings reveal support for the general prior understanding of (a) the negative effect of internal IT expenditure and cloud-based software's quality and risk concerns on application-level cloud adoption, and (b) the positive effect of perceived benefits of cloud-based software on application-level cloud adoption for both SMEs and large enterprises. Finally, when SMEs develop more custom Web services in house, they tend to adopt fewer server-level cloud services externally. The theoretical development and empirical analysis of the effect of IT modularity and firm size on cloud adoption contribute to our understanding of how firms can be more internally ready to use cloud computing effectively.
Currently, cloud computing has become one of the key tools in the field of information technology. Their popularity is due to their high flexibility and scalability, as well as cost-effectiveness. However, cloud compu...
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Currently, cloud computing has become one of the key tools in the field of information technology. Their popularity is due to their high flexibility and scalability, as well as cost-effectiveness. However, cloud computing also poses significant data security challenges. The main method to protect data in cloud computing is encryption, which, while ensuring data confidentiality, complicates data processing. Moreover, traditional encryption methods do not allow operations with encrypted data to be performed without decrypting them, which may be impractical or even impossible in the case of processing large amounts of data, which suggests the expediency of using fully homomorphic encryption *** this paper, information security threats in the cloud infrastructure are identified, ways to prevent the described threats are analyzed, and it is proved that homomorphic encryption can comprehensively solve security problems in cloud environments. The architecture and algorithm of functioning of a software tool that ensures the security and confidentiality of data transmitted and processed in a cloud environment are described, quantitative characteristics of its performance are obtained, and advantages over existing solutions are shown.
The application of cloud computing technology has had a significant impact on both the traditional IT industry and enterprise network marketing. This paper takes medium-sized enterprises in Southwest China as the rese...
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The application of cloud computing technology has had a significant impact on both the traditional IT industry and enterprise network marketing. This paper takes medium-sized enterprises in Southwest China as the research object, collects data through questionnaires, uses statistical analysis methods, and explores the impact of cloud computing technology on enterprise network marketing and its influencing factors. The results of the study show that enterprises have deficiencies in channel selection and effect evaluation of network marketing, are not satisfied with the effect of cloud computing technology, or fail to give full play to the advantages of cloud computing technology, and have a high level of concern and vigilance about the difficulty and uncertainty of cloud computing technology. Therefore, this paper puts forward some suggestions for the problems found. The first is to strengthen the access of cloud computing, choose the appropriate cloud service mode and deployment mode, and improve the availability and reliability of cloud computing;the second is to strengthen the training of cloud computing, improve the cognition and skills of enterprises and employees of cloud computing, and reduce the barriers to the use of cloud computing;the third is to strengthen the management of cloud computing, and formulate a reasonable strategy and specification of cloud computing, to ensure the security and efficiency of cloud computing;the fourth is to strengthen the innovation of cloud computing, to using the functions and characteristics of cloud computing, and develop and implement more diversified and personalized network marketing methods to enhance the competitive advantage of enterprises.
Data security during transmission over public networks has become a key concern in an era of rapid digitization. Image data is especially vulnerable since it can be stored or transferred using public cloud services, m...
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Data security during transmission over public networks has become a key concern in an era of rapid digitization. Image data is especially vulnerable since it can be stored or transferred using public cloud services, making it open to illegal access, breaches, and eavesdropping. This work suggests a novel way to integrate blockchain technology with a Chaotic Tent map encryption scheme in order to overcome these issues. The outcome is a Blockchain driven Chaotic Tent Map Encryption Scheme (BCTMES) for secure picture transactions. The idea behind this strategy is to ensure an extra degree of security by fusing the distributed and immutable properties of blockchain technology with the intricate encryption offered by chaotic maps. To ensure that the image is transformed into a cipher form that is resistant to several types of attacks, the proposed BCTMES first encrypts it using the Chaotic Tent map encryption technique. The accompanying signed document is safely kept on the blockchain, and this encrypted image is subsequently uploaded to the cloud. The integrity and authenticity of the image are confirmed upon retrieval by utilizing blockchain's consensus mechanism, adding another layer of security against manipulation. Comprehensive performance evaluations show that BCTMES provides notable enhancements in important security parameters, such as entropy, correlation coefficient, key sensitivity, peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), and number of pixels change rate (NPCR). In addition to providing good defense against brute-force attacks, the high key size of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${10}<^>{72}$$\end{document} further strengthens the system's resilience. To sum up, the BCTMES effectively addresses a number of prevalent risks to pi
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