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Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. We present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet. [ABSTRACT FROM AUTHOR]

The dual-scrambling scheme that combines position transformation and bit-plane transformation is a popular image encryption scheme. However, such schemes need more key information, and the encryption and decryption processes are complicated. In addition, the existing quantum image dual-scrambling schemes mainly deal with square images. In this paper, we propose a hybrid scrambling encryption scheme for multi-mode quantum image representation (MQIR) images based on random permutation, in which the H × W quantum image is represented in MQIR. A random number generator factor s uniquely associates one of the random permutations of integers from 1 to a positive integer, so as to hybrid scramble both the pixel position and the binarized position of each pixel value. Meanwhile, the quantum circuits and some examples of scrambling are given. Furthermore, various analyses of the performance of this scheme were conducted, including effectiveness, key space, and computational complexity. By modifying the random generation factor to construct multiple binary grayscale images, the simulated results on the IBM Quantum Cloud platform demonstrate that the proposed quantum image encryption scheme is effective. In comparison to existing quantum image dual scrambling schemes, it is both simple and effective, offering a large key space, lower computational complexity, and applicability to non-square quantum images. [ABSTRACT FROM AUTHOR]

Task scheduling is the core research content of cloud computing. It studies how to allocate tasks between computing nodes, so that tasks are evenly allocated or the execution cost of each task is minimized or the overall performance of the system is optimized. Different from the previous task slices that perform independent tasks sequentially in a model with processing time as the goal, we construct a mathematical model with the goal of optimizing response time, in which task slices are executed in parallel. Then, a solution method based on the improved adjustment entropy function is given and a new task scheduling algorithm is designed. Finally, we implement our proposed task scheduling algorithm and compare it with the game theory algorithm and the balanced scheduling algorithm. From the experimental results, by comparing the response time of the task with the size of the control system, the system load and the communication bandwidth, it is found that the task scheduling algorithm we proposed is superior to the game theory algorithm and the balanced scheduling algorithm in response time, and the algorithm we proposed is fair. [ABSTRACT FROM AUTHOR]

With rapid development and deployment of artificial intelligence methods, the Industrial Internet of Things (IIoT) has highly developed to fast tracing industrial communications and optimizing manufacturing processes. In Industry 5.0, hyper-automation process as one of technological trends navigates industrial entities to intelligent devices of the IIoT, cloud computing, smart robotics, smart agile software and embedded components by high complexity and reliability. By increasing data communication in the IIoT environments and cloud computing, the security and safety of hyper-automation process is also increasingly unstable and challengeable with respect to cyber-attacks, unstructured malwares and abnormal activities. With the diversification and unexpected behaviors of cyber-security threats, traditional cyber-attack detection systems have some critical problems with increasing massive data including unappropriated feature selection and extraction, high computation time in prediction and inaccurate classification models. Due to the above-mentioned challenges, this paper presents a new cloud-based cyber-attack detection architecture based on Ensemble Bagged Trees Detection (EBTD) algorithm for predicting malicious behaviors and types of cyber-attacks for hyper-automation process in the IIoT. The proposed architecture uses Analysis of Variance (ANOVA) and a priority-based feature selection and extraction model to find the optimal features with highly dependent on the network traffic, computation time, malicious behaviors, and types of attacks. Then, experimental results are conducted using the technical data sets UNSW-NB15 and NSL-KDD. The simulation results show that the proposed architecture performance performs better than other case studies and prediction models and effective on optimization of large-scale cyber-attack detection systems for critical hyper-automation process in the IIoT environment. [ABSTRACT FROM AUTHOR]

Cloud computing systems are widely used to deploy big data-based applications because of their high storage and computation capacity. The key component for storage in cloud computing environment is distributed file system which can store and process data produced by big data-based applications effectively. The users of such big data-based applications issue read requests more frequently when compared to write requests. So, most of these cloud-based applications demand optimal performance from the distributed file system, especially for read operations. Numerous caching and prefetching techniques have been proposed in the existing literature to enhance the performance of distributed file system. However, these techniques typically adopt a synchronous approach, focusing on either application data prefetching or user data prefetching, when the user application starts executing and this may result in an extended read access time. Furthermore, the data is prefetched either based on access frequency or reuse distance with out considering the access recency of data which may result in less cache hit ratio. In this paper, we have proposed application-specific and user-specific data prefetching algorithms for prefetching the data from the distributed file system and storing the same in the multi-level caches present in the distributed file system based on the combination of access frequency and recency ranking of file blocks that were previously accessed by client application programs. Additionally, we have divided the cache into two partitions namely user and application caches to store the prefetched data as per the popularity value calculated by considering user and application level accesses. We have also introduced a parallel read algorithm to read data simultaneously from the multiple caches present in the distributed file system environment. The simulation results demonstrate that, the proposed algorithms improved the distributed file systems performance by minimum of 8 to maximum of 92 percent in terms of average read access time when compared with different existing approaches. [ABSTRACT FROM AUTHOR]

For the global-scale multimedia live streaming services, both of the cost-efficiency at the service provider side and the Quality of Experience (QoE) satisfaction at the viewer side need to be achieved. This is a difficult challenge because the request patterns of global live-streaming services are highly dynamic. In this paper, we solve this issue by cloud-based adaptive resource allocation. We first present a cloud-based multi-tier architecture, called MaaS (Media as a Service), which consists of four types of modules. The main issue that we focus on is the deployment of properly dimensioned MaaS modules in proper geographical regions. We take the QoE of the Dynamic Adaptive Streaming over HTTP viewers into account for this decision. We propose a combination of deep-learning based demand prediction scheme and a dynamic-programming based heuristic to make a good tradeoff between viewers' QoE and the cloud resource cost. Extensive evaluation shows that the proposed scheme clearly outperforms the existing schemes. [ABSTRACT FROM AUTHOR]

Dynamic workloads with varying parameters are delivered to data centers to be scheduled on virtual machines (VMs). These unreliable, inconsistent, shockingly rising workloads with changing resource requisites may cause data center servers to become imbalanced. Consequently, the servers' resource utilization and QoS get degraded which further subsides the energy usage. To meet the requirement for variable on-demand resource provisioning, the current infrastructure must be virtualized to escalate its capability and capacity. Furthermore, because of the existence of competing balancing constraints, load balancing in cloud systems is NP-hard. An efficient load-balancing strategy in coalition with a scheduling technique based on the fusion of the Firefly (FF) and Honeybee algorithm (HA) is implemented to resolve the shortcomings of load balancing. This article proposes an efficient Firefly and Honeybee-based Load Balancing algorithm (FHLBA), to optimize average load, response time, and turnaround time. This methodology not only improvises resource utilization and throughput but also depreciates the makespan, degree of imbalance. It initially executes the FF algorithm to explore potential search space and identify the best feasible mapping of tasks to VMs. Further, the HA technique is applied to eliminate over-usage and under-usage of resources and optimize resource usage as per the workload. The efficacy of the proposed technique is appraised through simulation of the independent and non-preemptive tasks on Cloudsim. A comparison with different mechanisms such as RR, FCFS, SJF, GA, IPSO, IPSO-Firefly, Firefly, OLB, and JAYA is given. The simulation results show the least average load of 0.238 ms, minimum average response time of 13.56 ms, memory usage up to 93%, effective CPU utilization of 98%, a throughput of 73%, and a makespan time of 148 ms, degree of imbalance of 172.01 is achieved with a variable volume of tasks and VMs. [ABSTRACT FROM AUTHOR]

Reducing power consumption with tolerable performance degradation is a fundamental challenge in today's containerized High Performance Computing (HPC). Most Dynamic Power Management (DPM) approaches proposed for the HPC environment are based on profile-guided power-performance prediction techniques. However, the complexity of DPM approaches in a multi-tenant containerized HPC environment (cHPCe) increases significantly due to the varying demands of users and the contention of shared resources. Moreover, there is limited research into software-level monitoring of power consumption in the popular Docker container environment since it is not designed keeping HPC in mind. The proposed research in this paper aims to present a real-time hybrid power-performance prediction approach using Long Short-Term Memory (LSTM) machine learning model. Unlike state-of-the-art techniques, LSTM with the rolling update mechanism accurately predicts the relationships between the tail latencies in time-series power performance data. It updates the training sample sequences according to the currently predicted power consumption and duration to predict sporadic power surges. It also proposes a power-cap determination framework with resource contention awareness to fine-tune power consumption in real time at the thread level. The proposed containerized environment is designed from scratch keeping HPC requirements in mind. Hence, we name the proposed approach as power-aware cHPCe (pcHPCe) and is evaluated and compared with native BareMetal execution using the NAS Parallel Benchmark (NPB) and HPC Challenge benchmark (HPCC) applications. Our experimental results show that the power-performance prediction model achieves an accuracy of 91.39% on average in real-time with an overhead of 1.6% of the total computing power per node. Our resource contention-aware power-cap selection framework attains significant power saving up to 13.1%. [ABSTRACT FROM AUTHOR]

The convergence with the emerging digital twin technology has been envisaged as the looming of a smart cloud-based healthcare system. Precision clone simulation of the patients and medical service builds a bridge to the virtual medical world, enabling diagnosing, monitoring, and future prediction to offer enhanced medical services. Cloud services provider plays a crucial role in endowing a way to interlink the physical world to the virtual medical world through outsourced storage, a communication platform, reduced computational cost, and the relevance of hosting and other services over the internet offered by it. However, any alteration, deviation, or amendment of the physical patient's and its digital twin's stored data in the cloud storage has been one of the security liabilities. In this work, we propose a blockchain-assisted certificateless public auditing mechanism for the cloud-based digital twin healthcare networks to substantiate the data integrity in cloud storage. Blockchain in the proposed mechanism enables keeping the auditing process and outsourcing activities log. Meanwhile, it also enjoys the benefits of batch auditing and pairing-free operations. The proposed work is demonstrated to be provably secure and efficient in achieving security goals like storage correctness, privacy-preserving, and public verifiability. Furthermore, the computational overhead of the presented work is shown to be efficient with contrasted with some existing works. [ABSTRACT FROM AUTHOR]

Nowadays, cloud computing is widely used in various fields and is booming day by day with different services offered to users according to their needs and contracts. However, this has brought many challenges and constraints that organizations must to be aware of and address to fully harness its power. In practice, the most important issue that has gained significant influence in improving system performances is task scheduling. Unfortunately, it is commonly known that this problem is NP-hard and the use of both heuristics and metaheuristics is required to obtain near optimal solutions but in a reasonable amount of computation time. Despite the fact that several studies have been published in the literature, there are still interesting and relevant questions to be addressed. For instance, when it comes to the stagnation phenomenon of local solutions and the premature convergence of the search process, it is crucial to execute the exploration and exploitation stages carefully as improperly performed stages may result in inefficient task mapping solutions. Consequently, to overcome the limitations of existing techniques in terms of local optimality trap and immature convergence, a novel hybrid optimization algorithm is proposed to deal with multi-objective task scheduling in heterogeneous IaaS cloud environments. It is based on the combination of the pollination behavior of flowers with the search exploration capability of the grey wolf optimizer strategy. In addition, it makes use of the evolutionary algorithms crossover operators to strike a good balance between exploring new solutions and exploiting the already discovered ones. Based on the CloudSim framework, different test-bed scenarios and both synthetic and standard workload traces were considered to assess the performance of the proposed algorithm by evaluating its objective function in terms of four optimization criteria, namely time makespan, resource utilization, degree of imbalance and throughput. Our proposal was compared to the well-known optimization-based scheduling techniques in the literature, like TSMGWO, GGWO, LPGWO and FPA approach. The obtained results corroborate the merits of the new designed hybrid algorithm. [ABSTRACT FROM AUTHOR]

Users of computer networks may benefit from cloud computing, which is a fairly new abstraction that offers features like processing as well as the sharing and storing of data. As a result of the services it provides, cloud computing is drawing significant investments from across the world. Despite this, Cloud Computing Security continues to be one of the most important issues for businesses and consumers that use cloud computing systems. A few of the security flaws that are associated with cloud computing were passed down from earlier computer systems. In contrast, the other flaws were brought about by the distinctive qualities and design of cloud computing. The newly developed platform has measures that restrict data access to just those users who are authorized to do so. Using the user's identification and authentication/authorization information, a third-party service is responsible for managing access to the data. This service checks on all requests. Sensitive information and facts pertaining to users are encrypted both while in transit and while being stored. The platform was put into operation, analysed, and compared to other cloud platforms that were already in existence in terms of how effective it was in comparison to other platforms. When compared to the other security platforms, the findings demonstrated that this platform performed as anticipated in a relatively short amount of time and offered robust protection against the acts of an intruder. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) has become increasingly popular in various fields of activity. Cloud platforms have also grown in popularity, as they offer services that are more secure and accessible worldwide. In this context, cloud-based technologies emerged to support ML, giving rise to the machine learning as a service (MLaaS) concept. However, the clients accessing ML services in order to obtain classification results on private data may be reluctant to upload sensitive information to cloud. The model owners may also prefer not to outsource their models in order to prevent model inversion attacks and to protect intellectual property. The privacy-preserving evaluation of ML models is possible through multi-key homomorphic encryption (MKHE), that allows both the client data and the model to be encrypted under different keys. In this paper, we propose an MKHE evaluation method for decision trees and we extend the proposed method for random forests. Each decision tree is evaluated as a single lookup table, and voting is performed at the level of groups of decision trees in the random forest. We provide both theoretical and experimental evaluations for the proposed method. The aim is to minimize the performance degradation introduced by the encrypted model compared to a plaintext model while also obtaining practical classification times. In our experiments with the proposed MKHE random forest evaluation method, we obtained minimal (less than 0.6%) impact on the main ML performance metrics considered for each scenario, while also achieving reasonable classification times (of the order of seconds). [ABSTRACT FROM AUTHOR]

It is tempting to assume that because effectiveness metrics have free choice to assign scores to search engine result pages (SERPs) there must thus be a similar degree of freedom as to the relative order that SERP pairs can be put into. In fact that second freedom is, to a considerable degree, illusory. That is because if one SERP in a pair has been given a certain score by a metric, fundamental ordering constraints in many cases then dictate that the score for the second SERP must be either not less than, or not greater than, the score assigned to the first SERP. We refer to these fixed relationships as innate pairwise SERP orderings. Our first goal in this work is to describe and defend those pairwise SERP relationship constraints, and tabulate their relative occurrence via both exhaustive and empirical experimentation. We then consider how to employ such innate pairwise relationships in IR experiments, leading to a proposal for a new measurement paradigm. Specifically, we argue that tables of results in which many different metrics are listed for champion versus challenger system comparisons should be avoided; and that instead a single metric be argued for in principled terms, with any relationships identified by that metric then reinforced via an assessment of the innate relationship as to whether other metrics are likely to yield the same system‐versus‐system outcome. [ABSTRACT FROM AUTHOR]

Recently, baby healthcare monitoring systems have emerged as a key research area. However, numerous research studies have been conducted on babies and their state of either awakening or sleeping. But there is a lack of comprehensive and systematic review in this area of research. The existing research considers mostly the children's monitoring vital health parameters, in turn, giving relaxation to the parents in caring of their babies. Still, the fundamental practical issues related to daily life are missing. As a result, the safety of babies against suffocation and falling hazards for employed parents or mothers involved in the workforce has been overlooked. Only a few studies exist that cover the age range of 0 months to 4 years, with further focus on the age range of 6 months to 4 years. In response to these gaps in previous research and existing technologies, this review work proposes a vision for a baby safety system that utilizes various sensors and technologies like machine learning and image processing to ensure a safer environment for babies. [ABSTRACT FROM AUTHOR]

AbstractCloud computing has revolutionized technology and education by providing scalable virtual resources accessible via the internet. This study emphasizes the importance of usability in cloud services, discussing various scales and metrics while highlighting their strengths and weaknesses. It underscores the necessity for a comprehensive usability scale that integrates both subjective and objective measures to suit diverse user scenarios and populations. The article introduces the Cloud Applications/Services Usability Development Scale, comprising five usability dimensions: Capable, Personal, Reliable, Valuable, and Secure. To validate the instrument, factor analysis was performed on 28 items for each construct, utilizing Principal Component Analysis and Varimax Rotation. The Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test were applied to assess sampling adequacy. Construct validity and reliability were scrutinized using AMOS 21, a statistical analysis software for structural equation modeling (SEM). A confirmation factor analysis was employed to assess both converging and discriminating validity. A survey conducted among actual cloud application users, specifically Google Drive users in Lahore’s public libraries, demonstrated the strong construct validity and reliability of the developed instrument. The Cloud Applications/Services Usability Development Scale proved effective in evaluating the usability of cloud-based applications. This study not only contributes valuable insights to the development and validation of usability measurement tools for cloud applications but also addresses a significant gap in local and international literature. Furthermore, it serves as a model for creating and validating measurement tools for cloud services, offering practical implications for usability testing and methodological advancements in the field of cloud computing. [ABSTRACT FROM AUTHOR]

Effective management of data is a major issue in Distributed File System (DFS), like the cloud. This issue is handled by replicating files in an effective manner, which can minimize the time of data access and elevate the data availability. This paper devises a Fractional Social Optimization Algorithm (FSOA) for replica management along with balancing load in DFS in the cloud stage. Balancing the workload for DFS is the main objective. Here, the chunk creation is done by partitioning the file into a different number of chunks considering Deep Fuzzy Clustering (DFC) and then in the round-robin manner the Virtual machine (VM) is assigned. In that case for balancing the load considering certain objectives like resource use, energy consumption and migration cost thereby the load balancing is performed with the proposed FSOA. Here, the FSOA is formulated by uniting the Social optimization algorithm (SOA) and Fractional Calculus (FC). The replica management is done in DFS using the proposed FSOA by considering the various objectives. The FSOA has the smallest load of 0.299, smallest cost of 0.395, smallest energy consumption of 0.510, smallest overhead of 0.358, and smallest throughput of 0.537. [ABSTRACT FROM AUTHOR]

Massive smart water conservancy object (WCO) need to be connected for real-time monitoring and control, which produces massive data. Unfortunately, heterogeneous data structures and semantics lead to low interoperability between WCO and management systems. To address this challenge, we propose a novel interoperability structure for a smart water conservancy system based on the Internet of Things (IoT), and the key design includes a smart WCO terminal, interoperability network, special interoperability protocol, WCO information model, and cloud platform. Universal terminal and network are the base of interoperability hardware, and special interoperability protocol and information model for interconnection of WCO are designed for smart water conservancy management system. WCO can be connected to a water conservancy Big Data processing cloud platform for interoperability applications. The application results demonstrate that our proposed WCO's interoperability structure has obvious advantages than the general IoT at WCO interoperability. The interoperability protocol is reliable, the information model can ease interoperability and security, and the semantic dictionary is very rich and covers all semantic services of WCO. [ABSTRACT FROM AUTHOR]

Background: Due to a multitude of factors, the onset of the COVID-19 pandemic resulted in a significant increase in mental health issues within society, including depression and anxiety. Due to the increased trend of mental health disorders in society, digital mental health therapies are more useful than ever. With the emergence of programs utilizing Internet Cognitive Behavioral Therapy (iCBT), mental health resources are easily accessible and can be widely implemented to those in need. The aim of this study was to identify predictors for interest to participate in SilverCloud digital mental health therapy among individuals with mild to severe anxiety and/or depression based on preliminary findings from the COVIDsmart study. Methods: COVIDSmart participants who had moderate to severe anxiety and/or depression based on the PHQ-9 and GAD-7 scores, and who consented to be contacted for future studies, were invited to complete a needs assessment survey via Research Electronic Data Capture (REDCap). This assessment used self-reported measures including medical diagnoses, mental health services received, reasons for anxiety and/or depression, the use of coping strategies, suicidal ideology using the Ask Suicide Questions tool, and whether they would be interested in receiving free digital mental therapy. Descriptive statistics were used to report participants' demographics and a logistic regression was used to find predictors for interest in participation in SilverCloud. SAS 9.4 was used and p values < 0.05 were considered significant. Results: Out of the original 782 COVIDsmart participants, 634 consented to be contacted for future studies, 280 were subsequently invited to complete the SilverCloud needs assessment, and 120 individuals completed it. The largest demographic among these participants were females (70.83%) who identified as White (80.83%). The mean age was 48.74 years (SD = 14.66). Results revealed that having a mental health comorbidity significantly predicted the likelihood of interest in participating in the SilverCloud digital mental health program (p = 0.027). Conclusions: In this study, mental illness comorbidities predicted the interest to participate in digital mental therapy. Fragmented healthcare and perceptions of unmet care needs are likely contributor factors. Further research with a diverse sample of participants is necessary for generalizability. Findings may have important implications for healthcare best practices. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to address the challenges of self-adaptive privacy in cloud computing environments (CCE). Under the Cloud-InSPiRe project, the aim is to provide an interdisciplinary framework and a beta-version tool for self-adaptive privacy design, effectively focusing on the integration of technical measures with social needs. To address that, a pilot taxonomy that aligns technical, infrastructural, and social requirements is proposed after two supplementary surveys that have been conducted, focusing on users' privacy needs and developers' perspectives on self-adaptive privacy. Through the integration of users' social identity-based practices and developers' insights, the taxonomy aims to provide clear guidance for developers, ensuring compliance with regulatory standards and fostering a user-centric approach to self-adaptive privacy design tailored to diverse user groups, ultimately enhancing satisfaction and confidence in cloud services. [ABSTRACT FROM AUTHOR]

A rugged handheld sensor for rapid in-field classification of cannabis samples based on their THC content using ultra-compact near-infrared spectrometer technology is presented. The device is designed for use by the Austrian authorities to discriminate between legal and illegal cannabis samples directly at the place of intervention. Hence, the sensor allows direct measurement through commonly encountered transparent plastic packaging made from polypropylene or polyethylene without any sample preparation. The measurement time is below 20 s. Measured spectral data are evaluated using partial least squares discriminant analysis directly on the device's hardware, eliminating the need for internet connectivity for cloud computing. The classification result is visually indicated directly on the sensor via a colored LED. Validation of the sensor is performed on an independent data set acquired by non-expert users after a short introduction. Despite the challenging setting, the achieved classification accuracy is higher than 80%. Therefore, the handheld sensor has the potential to reduce the number of unnecessarily confiscated legal cannabis samples, which would lead to significant monetary savings for the authorities. [ABSTRACT FROM AUTHOR]

When resource demand increases and decreases rapidly, container clusters in the cloud environment need to respond to the number of containers in a timely manner to ensure service quality. Resource load prediction is a prominent challenge issue with the widespread adoption of cloud computing. A novel cloud computing load prediction method has been proposed, the Double-channel residual Self-attention Temporal convolutional Network with Weight adaptive updating (DSTNW), in order to make the response of the container cluster more rapid and accurate. A Double-channel Temporal Convolution Network model (DTN) has been developed to capture long-term sequence dependencies and enhance feature extraction capabilities when the model handles long load sequences. Double-channel dilated causal convolution has been adopted to replace the single-channel dilated causal convolution in the DTN. A residual temporal self-attention mechanism (SM) has been proposed to improve the performance of the network and focus on features with significant contributions from the DTN. DTN and SM jointly constitute a dual-channel residual self-attention temporal convolutional network (DSTN). In addition, by evaluating the accuracy aspects of single and stacked DSTNs, an adaptive weight strategy has been proposed to assign corresponding weights for the single and stacked DSTNs, respectively. The experimental results highlight that the developed method has outstanding prediction performance for cloud computing in comparison with some state-of-the-art methods. The proposed method achieved an average improvement of 24.16% and 30.48% on the Container dataset and Google dataset, respectively. [ABSTRACT FROM AUTHOR]

The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation's susceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests (Pinus halepensis Mill. and Pinus pinaster Aiton) to wildfires, analyzing two major forest fires that occurred in Yeste (Spain) in 1994 and 2017, affecting over 14,000 and 3200 hectares, respectively. Four recovery regions were identified based on fire severity—calculated using the delta Normalized Burn Ratio (dNBR) index—and recurrence: areas with high severity in 2017 but not in 1994 (UB94-HS17), areas with high severity in 1994 but not in 2017 (HS94-UB17), areas with high severity in both fires (HS94-HS17), and areas unaffected by either fire (UB94-UB17). The analysis focused on examining the recovery patterns of three spectral indices—the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), and Normalized Burn Ratio (NBR)—using the Google Earth Engine platform from 1990 to 2023. Additionally, the Relative Recovery Indicator (RRI), the Ratio of Eighty Percent (R80P), and the Year-on-Year average (YrYr) metrics were computed to assess the spectral recovery rates by region. These three spectral indices showed similar dynamic responses to fire. However, the Mann–Kendall and unit root statistical tests revealed that the NDVI and NDMI exhibited distinct trends, particularly in areas with recurrence (HS94-HS17). The NDVI outperformed the NBR and NDMI in distinguishing variations among regions. These results suggest accelerated vegetation spectral regrowth in the short term. The Vegetation Recovery Capacity After Fire (VRAF) index showed values from low to moderate, while the Vulnerability to Fire (V2FIRE) index exhibited values from medium to high across all recovery regions. These findings enhance our understanding of how vegetation recovers from fire and how vulnerable it is to fire. [ABSTRACT FROM AUTHOR]

In cloud computing environment, the workflow allocation mainly focuses on task assignment to satisfy the quality-of-service (QoS) standards and workload balancing by the best use of the demanded resources as a service on pay per use basis to optimize objectives from service level agreements. In secured workflow allocation (SWA), the intermediate data transfers in sensitive workflow tasks demand security requirements during the execution across the virtual machines. Security-aware workflow execution challenges result in a significant share of security overhead which affects the QoS performance. The aim of this work is to facilitate the researcher's selection of suitable SWA approaches from the existing approaches in the literature. To achieve this, we have comprehensively reviewed the current state-of-the-art approaches of the SWA models in cloud computing environments. Also, a secured workflow allocation challenges taxonomy by describing the security overhead model and QoS objectives and constraints is presented. Finally, it determines and discusses the existing open issues, challenges, and future research direction in the SWA model based on the study's literature review. The study will help researchers in the domain with current security challenges for SWA research trends. [ABSTRACT FROM AUTHOR]

Research on resource management focuses on optimizing system performance and energy efficiency by distributing shared resources like processor cores, caches, and main memory among competing applications. This research spans a wide range of applications, including those from high-performance computing, machine learning, and mobile computing. Existing research frameworks often simplify research by concentrating on specific characteristics, such as the architecture of the computing nodes, resource monitoring, and representative workloads. For instance, this is typically the case with cloud systems, which introduce additional complexity regarding hardware and software requirements. To avoid this complexity during research, experimental frameworks are being developed. Nevertheless, proposed frameworks often fail regarding the types of nodes included, virtualization support, and management of critical shared resources. This paper presents Stratus, an experimental framework that overcomes these limitations. Stratus includes different types of nodes, a comprehensive virtualization stack, and the ability to partition the major shared resources of the system. Even though Stratus was originally conceived to perform cloud research, its modular design allows Stratus to be extended, broadening its research use on different computing domains and platforms, matching the complexity of modern cloud environments, as shown in the case studies presented in this paper. [ABSTRACT FROM AUTHOR]

Function-as-a-service has reduced the user burden by allowing cloud service providers to overtake operational activities such as resource allocation, service deployment, auto-scaling, and load-balancing, to name a few. The users are only responsible for developing the business logic through event-triggered functions catering to an application. Although FaaS brings about multiple user benefits, a typical challenge in this context is the time incurred in the environmental setup of the containers on which the functions execute, often referred to as the cold-start time leading to delayed execution and quality-of-service violations. This paper presents an efficient scheduling strategy FASE that uses a finite-sized warm pool to facilitate the instantaneous execution of functions on pre-warmed containers. Test-bed evaluations over AWS Lambda confirm that FASE achieves a 40% reduction in the average cold-start time and 1.29 × speedup compared to the baselines. [ABSTRACT FROM AUTHOR]

An auction mechanism is an effective way to allocate resources through market behavior. However, in existing studies, most auction mechanisms are designed based on the maximization of social welfare, and there are few studies on potential revenue. Based on cloud computing resource allocation, this paper studies an auction mechanism with revenue limits under single-dimensional and multidimensional resource allocation. That is, the resource provider proposes the lowest revenue limit B. The mechanism aims to maximize the total social welfare while conforming to the lowest revenue limit of the provider. Specifically, we design a new price-raising auction mechanism based on resource similarity and the user cost-effectiveness value, which unifies the two stages of resource allocation and payment pricing, overcoming the problem of low revenue caused by overallocated resources and the lowest winning price. This mechanism can also achieve truthfulness, individual rationality and computational efficiency. In the experimental section, the proposed mechanism is compared with the optimal VCG mechanism and the monotonic mechanism with critical values in terms of revenue, social welfare, resource utilization, etc., and the results demonstrate the good effects of the mechanism designed in this article. [ABSTRACT FROM AUTHOR]

During disasters, swiftly and efficiently evacuating populations in hazardous situations is crucial to minimize losses. This study proposes a novel framework to address dynamic population evacuation (DPE) problems, which includes planning and online evacuation management phases facilitated by vehicular communication. In the planning phase, a shelter allocation problem (SAP) is solved dynamically for destination choice and a dynamic traffic assignment (DTA) for path choice toward the chosen destinations to obtain an initial evacuation plan. The initial plan is then enhanced by employing a vehicular ad hoc network (VANET) within the vehicular edge computing (VEC) architecture. This enhancement enables communication among evacuees, allowing them to revise their vehicle's route choice and planned destination. These revisions take into account the changing risk and traffic conditions. The proposed online DPE framework is applied to the real evacuation scenario of Mill Valley City, CA. The proposed model is evaluated with different VANET architectures, including vehicular cloud computing (VCC) and VEC. The results show that the VEC framework outperforms other configurations and improves the evacuation process compared to the scenario with an initial plan by more than 30% in network clearance time. Additionally, a performance analysis is carried out for evacuation scenarios with different penetration rates of connected vehicles in VANET. [ABSTRACT FROM AUTHOR]

As the globe enters a new era, web applications will become indispensable to managing business. Businesses can easily grow, become simpler, and accomplish their objective much faster by employing web applications. Creating a web application in cloud computing allows for the more affordable leveraging of cloud-based services. This makes it easier to avoid setting up and maintaining several servers. To get around cloud computing’s built-in restrictions such as scalability, security, and bandwidth limitations, the future smart world of cloud computing will be coupled with LiFi connectivity. Beyond creating the web application, it is important to promote this web application among the network of users as quickly and effectively as possible. This manuscript proposes a strategy to address these challenges. There are two primary components to this MCDM technique. The first step is to model the problem as a graph and weigh the edges by employing the Hamacher aggregation operator. The second step involves using a fresh iteration of Kruskal’s technique in conjunction with this approach to discover a Minimum Spanning Tree as a resolution. This manuscript adds to the literature by solving real-world Minimum Spanning Tree problems by combining existing algorithms with MCDM techniques. This technique is demonstrated for marketing a web application(created via cloud service) in a future smart world using LiFi technology. [ABSTRACT FROM AUTHOR]

Public clouds favor sharing of storage resources, in which many tenants acquire bandwidth and storage capacity from a shared storage pool. To provide high availability, data are often encoded to provide fault tolerance with low storage costs. Regarding this, efficiently organizing an encoded storage system for shared I/Os is critical for application performance. This is usually hard to achieve as different applications have different stripe configurations and fault tolerance levels. In this paper, we first study the block trace from the Alibaba cloud, and find that I/O patterns of modern applications prefer the resource sharing scheme. Based on this, we propose a globally shared resource paradigm for encoded storage system in the public cloud. The globally shared resource paradigm can provide balanced load and fault tolerance for numerous disk pool sizes and arbitrary application stripe configurations. Furthermore, we demonstrate with two case studies that our theory can help address the device-specific problems of HDD and SSD RAID arrays with slight modifications: comparing the existing resource partition and resource sharing methods, our theory can promote the rebuild speed of the HDD RAID arrays by 2.5 ×, and reduce the P99 tail latency of the SSD arrays by up to two orders of magnitude. [ABSTRACT FROM AUTHOR]

Researchers have created cryptography algorithms that encrypt data using a public or private key to secure it from intruders. It is insufficient to protect the data by using such a key. No research article has identified an algorithm capable of protecting both the data and the associated key, nor has any mechanism been developed to determine whether access to the data is permissible or impermissible based on the authentication of the key. This paper presents a WEDEx-Kerberotic Framework for data protection, in which a user-defined key is firstly converted to a cipher key using the "Secure Words on Joining Key (SWJK)" algorithm. Subsequently, a WEDEx-Kerberotic encryption mechanism is created to protect the data by encrypting it with the cipher key. The first reason for making the WEDEx-Kerberotic Framework is to convert the user-defined key into a key that has nothing to do with the original key, and the length of the cipher key is much shorter than the original key. The second reason is that each ciphertext and key value are interlinked. When an intruder utilizes the snatching mechanism to obtain data, the attacker obtains data or a key unrelated to the original data. No matter how efficient the algorithm is, an attacker cannot access the data when these methods and algorithms are used to protect it. Finally, the proposed algorithm is compared to the previous approaches to determine the uniqueness of the algorithm and assess its superiority to the previous algorithms. [ABSTRACT FROM AUTHOR]

The need for speeding up data analytics increases inevitably due to the need for extracting valuable information from social media, data generated by smart devices with sensors, patterns of people's communications over the web, items viewed and bought by global-scale customers, cloud applications, etc., all of which take part in the "Big Data." Such kind of interaction data is very well represented as sparse graphs to enable the graph analytics, which requires efficient underlying kernels. The breadth-first search (BFS)-based traversal is a commonly used kernel in graph algorithms such as the betweenness centrality algorithm for centrality analysis. In this work, we focus on parallel BFS operations and propose hypergraph-based combinatorial models that aim at reducing cache misses and hence exploiting data locality during the parallel BFS operations. Our models are based on finding new vertex visit orders so that locality in accessing the data associated with vertices is exploited. Experiments on graphs arising in a wide range of applications show that our proposed models achieve on average 9% performance improvement in the CPU-based Ligra data analytics framework. [ABSTRACT FROM AUTHOR]

Cloud service providers improve resource utilization by co-locating latency-critical (LC) workloads with best-effort batch (BE) jobs in datacenters. However, they usually treat multi-component LCs as monolithic applications and treat BEs as "second-class citizens" when allocating resources to them. Neglecting the inconsistent interference tolerance abilities of LC components and the inconsistent preemption loss of BE workloads can result in missed co-location opportunities for higher throughput. We present Rhythm, a co-location controller that deploys workloads and reclaims resources rhythmically for maximizing the system throughput while guaranteeing LC service's tail latency requirement. The key idea is to differentiate the BE throughput launched with each LC component, that is, components with higher interference tolerance can be deployed together with more BE jobs. It also assigns different reclamation priority values to BEs by evaluating their preemption losses into a multi-level reclamation queue. We implement and evaluate Rhythm using workloads in the form of containerized processes and microservices. Experimental results show that it can improve the system throughput by 47.3%, CPU utilization by 38.6%, and memory bandwidth utilization by 45.4% while guaranteeing the tail latency requirement. [ABSTRACT FROM AUTHOR]

Cloud detection technology is crucial in remote sensing image processing. While cloud detection is a mature research field, challenges persist in detecting clouds on reflective surfaces like ice, snow, and sand. Particularly, the detection of cloud shadows remains a significant area of concern within cloud detection technology. To address the above problems, a convolutional self-attention mechanism feature fusion network model based on a U-shaped structure is proposed. The model employs an encoder–decoder structure based on UNet. The encoder performs down-sampling to extract deep features, while the decoder uses up-sampling to reconstruct the feature map. To capture the key features of the image, Channel Spatial Attention Module (CSAM) is introduced in this work. This module incorporates an attention mechanism for adaptive field-of-view adjustments. In the up-sampling process, different channels are selected to obtain rich information. Contextual information is integrated to improve the extraction of edge details. Feature fusion at the same layer between up-sampling and down-sampling is carried out. The Feature Fusion Module (FFM) facilitates the positional distribution of the image on a pixel-by-pixel basis. A clear boundary is distinguished using an innovative loss function. Finally, the experimental results on the dataset GF1_WHU show that the segmentation results of this method are better than the existing methods. Hence, our model is of great significance for practical cloud shadow segmentation. [ABSTRACT FROM AUTHOR]

Cloud computing has become a major component of the modern IT ecosystem. A key contributor to this has been the development of Infrastructure as a Service (IaaS) architecture, in which users' virtual machines (VMs) are run on the service provider's physical infrastructure, making it possible to become independent of the need to purchase one's own physical machines (PMs). One of the main aspects to consider when designing such systems is achieving the optimal utilization of individual resources, such as processor, RAM, disk, and available bandwidth. In response to these challenges, the authors developed an analytical model (the ARU method) to determine the average utilization levels of the aforementioned resources. The effectiveness of the proposed analytical model was evaluated by comparing the results obtained by utilizing the model with those obtained by conducting a digital simulation of the operation of a cloud system according to the IaaS paradigm. The results show the effectiveness of the model regardless of the structure of the emerging requests, the variability of the capacity of individual resources, and the number of physical machines in the system. This translates into the applicability of the model in the design process of cloud systems. [ABSTRACT FROM AUTHOR]

Aims: To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain. Design: Bibliometric analysis. Methods: The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer. Results: The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field. Conclusions: The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field. Clinical Relevance: This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science. [ABSTRACT FROM AUTHOR]

Recently, several private and political cloud initiatives emerged in Europe. This paper demonstrates how the sociotechnical imaginaries of three European cloud projects reveal a performative coupling of innovation and political ideas of control, territoriality and sovereignty. I ascertain three elements of the concept of sociotechnical imaginaries (innovation, boundary making and material properties) guiding the empirical analysis. Taking technology in the making and its role in (geo)politics seriously, this paper shows how imaginaries shape and interact with current geostrategic and political developments in Europe. The analysis of Microsoft's cloud, Bundescloud and GAIA-X reveals that rising privacy and data security issues have been integrated into cloud imaginaries that traditionally highlight progress and innovation. More specifically, state actors and cloud providers link and sometimes merge allegedly opposing technological aspects of innovation and politicised ideas of control such as digital sovereignty. This shift constitutes a move towards erecting political borders and localising IT within a global infrastructure. [ABSTRACT FROM AUTHOR]

Cloud computing has revolutionized the way IT industries work. Most modern‐day companies rely on cloud services to accomplish their day‐to‐day tasks. From hosting websites to developing platforms and storing resources, cloud computing has tremendous use in the modern information technology industry. Although an emerging technique, it has many security challenges. In structured query language injection attacks, the attacker modifies some parts of the user query to still sensitive user information. This type of attack is challenging to detect and prevent. In this article, we have reviewed 65 research articles that address the issue of its prevention and detection in cloud and Traditional Networks, of which 11 research articles are related to general cloud attacks, and the rest of the 54 research articles are specifically on web security. Our result shows that Random Forest has an accuracy of 99.8% and a Precision rate of 99.9%, and the worst‐performing model is Multi‐Layer Perceptron (MLP) in the SQLIA Model. For recall value, Random Forest performs best while TensorFlow Linear Classifier performs worst. F1 score is best in Random Forest, while MLP is the most diminutive performer. [ABSTRACT FROM AUTHOR]

Several real-world scientific and industrial workflow applications adopt elastic and cost-efficient cloud services to fulfill their requirement. There are two stakeholders in the system, namely the user and the provider each of which tries to maximize its profit and at the same time minimize their possible overall costs. Since task scheduling algorithms for workflow executions determine which task should be possibly executed on what resources, they have a drastic impact on both the user's quality of experiences and on the underlying resource utilization. Indeed, an efficient task scheduling algorithm can meet service-level agreement (SLA) for both sides. For the sake of the importance of the issue, this survey presents a subjective taxonomy on the task scheduling schemes in the literature for workflow executions in cloud computing environments to be a guideline for future improvement. It classifies the literature based on the proposed algorithms in the literature, objectives, stakeholders' requirements, and evaluation metrics. This survey highlights research trends, challenges, research gaps, potential solutions, and future direction. It can also pave the way for further processing, improvement and strengthening of existing approaches or devising novel ones for interested researchers in the field of task scheduling problems. [ABSTRACT FROM AUTHOR]

To investigate how the petroleum industry evaluates and selects powerful cloud service providers, first, an evaluation index system including 25 indices such as scalability and private data protection is built. This index system can systematically examine the comprehensive strengths of cloud service providers. Aiming to solve the problems that the traditional expert evaluation method has high requirements on expert experience and is easily affected by subjective factors, a novel artificial intelligence evaluation model named TentISSA-BPNN is proposed. The objective evaluation ability of this model can be effectively used in evaluation research on cloud service providers for petroleum enterprises. In this model, the SSA algorithm is optimized by Tent chaotic mapping and adaptive inertia weight; an algorithm, TentISSA, that has good stability and fast convergence speed is designed and proposed; and the BPNN is improved with TentISSA to obtain more accurate evaluation results. To evaluate the performance of the TentISSA algorithm, nine unimodal and multimodal functions are selected in this paper to test the convergence accuracy. Then, seven models are selected as the control groups to validate the effectiveness and performance of the TentISSA-BPNN evaluation model proposed in this paper. Finally, the preprocessed data of the candidate cloud service providers are input into the trained neural network model proposed in this paper for evaluation. Based on the ranking of the evaluation scores, the comprehensive strengths of the cloud service providers are obtained to provide a decision-making reference for managers of petroleum enterprises in the process of choosing cloud service providers. [ABSTRACT FROM AUTHOR]

Air pollution is a growing concern in today's urbanized world, necessitating efficient and accurate methods for air quality monitoring. The proliferation of IoT devices has led to a surge in the generation of time-series data. With its high volume and complexity, this surge in time-series data necessitates cloud-based solutions for handling and analyzing this data effectively. However, existing methods for air quality monitoring face challenges in capturing the complex patterns and dynamics of air pollution, which often exhibit both linear and nonlinear characteristics. Air pollution data often exhibit both linear and nonlinear characteristics. Linearity and nonlinearity refer to the nature of the relationships within the data. Some aspects of air quality, such as pollutant concentrations, may follow linear patterns, while other factors, like the interaction of multiple pollutants and environmental conditions, exhibit nonlinear relationships. This complexity arises from the multifaceted nature of air quality dynamics, which various factors and interactions can influence. To address these challenges, this study introduces a novel hybrid time-series approach that combines the proven strengths of two well-established techniques: traditional time-series autoregressive integrated moving average (ARIMA) and soft computing adaptive neuro-fuzzy inference system (ANFIS). The hybrid model is designed to provide a comprehensive solution that accommodates the diverse characteristics of air quality time-series data. To assess the efficacy of our proposed model, we conducted extensive experiments using real-world air pollution datasets obtained from the Ministry of Environment, Forest and Climate Change of India, covering the period from January 2015 to July 2020. Our evaluation includes a range of performance metrics such as root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared logarithmic error (MSLE). Specifically, our model demonstrates exceptional accuracy, with notably low error values for key metrics such as air quality index (AQI) and PM2.5. Furthermore, we subjected our innovative hybrid model to rigorous statistical testing using the Diebold-Mariano test, establishing the significance and superiority of our approach. This research advances our understanding of air quality prediction and offers a valuable solution for mitigating the detrimental effects of air pollution on public health and the environment. [ABSTRACT FROM AUTHOR]