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Internet of Things (IoT) architectures generally focus on providing consistent performance and reliable communications. The convergence of IoT, edge, fog, and cloud aims to improve the quality of service of applications, which does not typically emphasize energy efficiency. Considering energy in IoT architectures would reduce the energy impact from billions of IoT devices. The research presented in this paper proposes an optimization framework that considers energy consumption of nodes when selecting a node for processing an IoT request in edge-fog-cloud layered architecture. The IoT use cases considered in this paper include smart grid, autonomous vehicles, and eHealth. The proposed framework is evaluated using CPLEX simulations. The results provide insights into mechanisms that can be used to select nodes energy-efficiently whilst meeting the application requirements and other network constraints in multi-layered IoT architectures. [ABSTRACT FROM AUTHOR]

Abstract Despite the increasingly digital nature of society there are some areas of research that remain firmly rooted in the past; in this case the laboratory notebook, the last remaining paper component of an experiment. Countless electronic laboratory notebooks (ELNs) have been created in an attempt to digitise record keeping processes in the lab, but none of them have become a ‘key player’ in the ELN market, due to the many adoption barriers that have been identified in previous research and further explored in the user studies presented here. The main issues identified are the cost of the current available ELNs, their ease of use (or lack of it) and their accessibility issues across different devices and operating systems. Evidence suggests that whilst scientists willingly make use of generic notebooking software, spreadsheets and other general office and scientific tools to aid their work, current ELNs are lacking in the required functionality to meet the needs of the researchers. In this paper we present our extensive research and user study results to propose an ELN built upon a pre-existing cloud notebook platform that makes use of accessible popular scientific software and semantic web technologies to help overcome the identified barriers to adoption.

Cloud technologies have a huge impact in the IT sphere. With their appearance, they made a significant contribution in various fields. They also provided a quick, easy and cost-effective access to services that significantly improved the availability of IT resources for companies and organizations. This was especially evident during the period of the Covid- 19 pandemic. During this period, a large number of organizations experienced the benefits offered by the cloud. Many of them have migrated their applications to the cloud in order to cope with the increasing number of requests and insufficient resources of their servers. This paper presents a review for the usage of cloud technologies in different areas during the Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

The need of keeping medical information safe and secure stems from the fact that doctors rely on it to make accurate diagnoses. If this information is altered in any way, no matter how minor, there is a risk of an inaccurate diagnosis, which could result in severe medical issues and death. The transition from paper to electronic health records (EHRs) has considerably improved patient care quality and efficiency. However, for many healthcare service providers, it has extended the attack surface. Because of the value of a patient's medical information, this has posed a threat to both patients and healthcare providers. When security is not taken into account in healthcare systems, patients' privacy is jeopardized. The intended solution to this challenge is to create a modified AES algorithm to secure patient medical information. Although, the AES algorithm is secure, however, there is always a need for improvement on any cryptographic algorithms in terms of computational cost. This study implements AES and modified the last round of the AES and their performance has been measured by scrambling input datasets of various contents and volumes. The experimental results show that modified AES outperforms AES algorithms in terms of Encryption time while AES outperform modified AES in terms of decryption time. Also, the Avalanche effect results revealed that modified AES has a higher avalanche effect for small-size files while a smaller avalanche effect for larger file sizes. This signifies that modified AES security is stronger for a small size file while conventional AES has higher security for larger file sizes. The average encryption time of the AES algorithm for text files is 1513.3ms while the modified AES average encryption time gives 1293.837ms. The average decryption time for conventional AES is 1289.627ms while the average decryption time for modified AES give 1400.136ms. Modified AES uses lesser time complexity during the encryption of all categories of data files while conventional AES uses lesser time complexity during the decryption of all categories of data files. [ABSTRACT FROM AUTHOR]

New satellite-based techniques open up new horizons to researchers and local communities. Concurrently, however, requirements and expectations with regard to satel-lite-based remote sensing products are increasingly higher. By relying on satellite-derived information, environmental observations can cover areas of a few to several metres resolution. Here we are dealing with free-of-charge and generally available sources of satellite-based information. The Piaśnica River mouth area was selected as an observation site representing a highly dynamic morphological transect. The paper compares products of cloud cover detection, supplied with data and available in the Copernicus database for a local area in the coastal zone of the Baltic Sea. The absolute difference did not exceed 5%, which confirms a high efficiency of the solutions offered. More than 96% of the clouded area determined for the Sentinel-2/MSI (Multispectral Instrument) was correctly identified when compared with supervised observations. The rate was lower (92%) for the Sentinel-3/OLCI (Ocean and Land Colour Instrument). It was eventually concluded that, at the local level, successful observations can be conducted using the cloud cover map supplied with the satellite data. At the same time, the analyses presented do not rule out further efforts to, e.g., increase the accuracy and speed of the analyses. [ABSTRACT FROM AUTHOR]

The management of large medical files poses a critical challenge in the health sector, with conventional systems facing deficiencies in security, scalability, and efficiency. Blockchain ensures the immutability and traceability of medical records, while the cloud allows scalable and efficient storage. Together, they can transform the data management of electronic health record applications. The method used was the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to choose and select the relevant studies that contribute to this research, with special emphasis set on maintaining the integrity and security of the blockchain while tackling the potential and efficiency of cloud infrastructures. The study's focus is to provide a comprehensive and insightful examination of the modern landscape concerning the integration of blockchain and cloud advances, highlighting the current challenges and building a solid foundation for future development. Furthermore, it is very important to increase the integration of blockchain security with the dynamic potential of cloud computing while guaranteeing information integrity and security remain uncompromised. In conclusion, this paper serves as an important resource for analysts, specialists, and partners looking to delve into and develop the integration of blockchain and cloud innovations. [ABSTRACT FROM AUTHOR]

Intelligent transport systems (ITS) provide various cooperative edge cloud services for roadside vehicular applications. These applications offer additional diversity, including ticket validation across transport modes and vehicle and object detection to prevent road collisions. Offloading among cooperative edge and cloud networks plays a key role when these resources constrain devices (e.g., vehicles and mobile) to offload their workloads for execution. ITS used different machine learning and deep learning methods for decision automation. However, the self-autonomous decision-making processes of these techniques require significantly more time and higher accuracy for the aforementioned applications on the road-unit side. Thus, this paper presents the new offloading ITS for IoT vehicles in cooperative edge cloud networks. We present the augmented convolutional neural network (ACNN) that trains the workloads on different edge nodes. The ACNN allows users and machine learning methods to work together, making decisions for offloading and scheduling workload execution. This paper presents an augmented federated learning scheduling scheme (AFLSS). An algorithmic method called AFLSS comprises different sub-schemes that work together in the ITS paradigm for IoT applications in transportation. These sub-schemes include ACNN, offloading, scheduling, and security. Simulation results demonstrate that, in terms of accuracy and total time for the considered problem, the AFLSS outperforms all existing methods. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic starkly revealed the vulnerability of global food systems, prompting a pressing call for sustainable and resilient practices. This conceptual paper forms an integral part of a comprehensive study aimed at investigating the pivotal role of technological and sustainable solutions in surmounting pandemic-induced challenges and attaining vital food security objectives. Specifically, this conceptual paper delves into the possibilities inherent in cloud-based management, robotics, and circular economy principles, focusing on their potential to bolster food security. To this end, the paper proposes the implementation of an 'IoT based Farm robot' to enhance aquaculture resilience in Yogyakarta, Indonesia - an apt locale renowned for its vibrant community and agricultural traditions. Leveraging cloud technology, the paper explores the remote management and monitoring of fishponds, thereby optimizing resource utilization and elevating overall productivity. Through the integration of robotics, the automation and precision introduced to fish farming processes serve to lessen reliance on manual labor. Furthermore, the incorporation of circular economy principles underpins sustainable practices, minimizing waste and maximizing the efficiency of resources. By harnessing the synergy of cloud-based management, robotics, and circular economy principles, this conceptual paper introduces a solution that empowers local communities to both navigate the current pandemic crisis and foster enduring aquaculture resilience. The discourse presented within this paper holds valuable implications for policymakers, researchers, and practitioners invested in aquaculture and food security. Moreover, it contributes to the wider conversation concerning sustainable food systems, offering a pathway to cultivating a more secure and resilient future for aquaculture in Yogyakarta and beyond, even amidst pandemics and other disruptive occurrences. [ABSTRACT FROM AUTHOR]

Aerosol–cloud interactions play a crucial role in shaping Earth's climate and hydrological cycle. Observing these interactions with high precision and accuracy is of the utmost importance for improving climate models and predicting Earth's climate. Over the past few decades, lidar techniques have emerged as powerful tools for investigating aerosol–cloud interactions due to their ability to provide detailed vertical profiles of aerosol particles and clouds with high spatial and temporal resolutions. This review paper provides an overview of recent advancements in the study of ACI using lidar techniques. The paper begins with a description of the different cloud microphysical processes that are affected by the presence of aerosol, and with an outline of lidar remote sensing application in characterizing aerosol particles and clouds. The subsequent sections delve into the key findings and insights gained from lidar-based studies of aerosol–cloud interactions. This includes investigations into the role of aerosol particles in cloud formation, evolution, and microphysical properties. Finally, the review concludes with an outlook on future research. By reporting the latest findings and methodologies, this review aims to provide valuable insights for researchers engaged in climate science and atmospheric research. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper analyzes the timing performance of a persistent storage designed for distributed container-based architectures in industrial control applications. The timing performance analysis is conducted using an in-house simulator, which mirrors our testbed specifications. The storage ensures data availability and consistency even in presence of faults. The analysis considers four aspects: 1. placement strategy, 2. design options, 3. data size, and 4. evaluation under faulty conditions. Experimental results considering the timing constraints in industrial applications indicate that the storage solution can meet critical deadlines, particularly under specific failure patterns. Comparison results also reveal that, while the method may underperform current centralized solutions in fault-free conditions, it outperforms the centralized solutions in failure scenario. Moreover, the used evaluation method is applicable for assessing other container-based critical applications with timing constraints that require persistent storage. [ABSTRACT FROM AUTHOR]

Over the last decade, data has exploded on cloud storage, and outsourcing data to cloud storage has become an appealing trend, which is not a fully reliable service. Data growth has made cloud storage management difficult. To provide more efficient and secure data storage on the cloud, cloud service providers employed a deduplication technique. Cloud data deduplication has developed as a popular research subject to boost the efficiency of cloud storage and minimize network communication traffic. It is not easy to provide security while increasing the huge volume of data on the cloud. Users are more concerned about the security of their data, as the data on the cloud is not secure and safe even after encryption. The various encryption and decryption available for providing the security to data. Conventional encryptions cannot be employed in data deduplication. To maintain the confidentiality and integrity of data, Convergent encryption and proof of ownership are applied. Various other approaches like proof of retrievability, Dekey, DupLESS, Identity-based encryption, Message-locked encryption, Attribute-based encryption, provable data possession, proof of storage with Deduplication, etc. are the security research topics these days. This paper presents a review of the literature on several proposed methodologies for safe deduplication techniques in cloud storage and current research trends. The primary contribution of the paper is to offer a complete understanding of the issues and solutions associated with safe data deduplication in cloud storage systems. The study dives into the various encryption solutions, security concerns, and potential challenges with data deduplication in the cloud. [ABSTRACT FROM AUTHOR]

This paper advocates for a proactive approach to traffic safety by introducing a collaborative Misbehaviour Response System (MBR) designed to preemptively address hazardous driving behaviours such as wrong-way driving and distracted driving. The system integrates with electric vehicles (EVs), leveraging advanced technologies like ADAS, edge computing, and cloud services to enhance road safety. Upon detection of misbehaviour, the MBR system utilizes data from interconnected parking facilities to identify the nearest safe location and provides navigation guidance to authorities and nearby vehicles. The paper presents a prototype of the MBR system, demonstrating its efficiency in detecting misbehaviours and coordinating swift responses. It also discusses the system's limitations and societal implications, outlining future research directions, including integration with autonomous vehicle systems and variable speed limit technologies, to further improve road safety through proactive and context-aware response mechanisms. [ABSTRACT FROM AUTHOR]

This paper analyses the component of the Integrated Interlocking System which forms the central logical and functional unit implementing all logical and computational functions necessary for railway traffic control in the Railway 4.0 concept. The main principle is that this approach centralizes the technology of station interlocking system, track line interlocking systems, level crossing interlocking systems and the functions of train interlocking system - the line part of ETCS L2/3 radio block control panel (RBC). The operation control is centralised to the controlling dispatcher centres. The paper discusses the concept of integrated interlocking system including safety issues addressed from the perspective of the requirements of CENELEC standards EN 50126, EN 50128 and EN 50129. These requirements are addressed from the perspective of the authors of the paper, who also work as independent assessors of the safety of railway control and command systems. [ABSTRACT FROM AUTHOR]

Hidayetoğlu et al. propose a novel memory-centric algorithm to reconstruct X-ray CT images in the SC19 article entitled “MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization”. They formulate the reconstruction with several SpMVs, and propose two memory-centric optimizations to improve cache locality for better memory bandwidth utilization, i.e., a two-level pseudo-Hilbert ordering and a multi-stage input buffering. In this article, we present our results on reproducing that article to show its effectiveness and generality, as part of the SC20 Student Cluster Competition Reproducibility Challenge. We reproduce the execution time and memory bandwidth tests in that article on various architectures, including Intel CPUs, AMD CPUs, and NVIDIA GPUs. We further analyze the bottleneck on different architectures by comparing the achieved memory bandwidth with the peak bandwidth on those architectures. We then reproduce the strong scaling test on CPU and GPU clusters with different scales, and use the proposed algorithm to reconstruct three new X-ray computed tomograms. [ABSTRACT FROM AUTHOR]

Summary: The resource orchestrators in the cloud provide different QoS classes. Existing studies have mainly focused on resource guarantee for high‐priority class jobs, but yet to consider delicate QoS control of low‐priority jobs in the cloud. In this paper, we propose the differentiated shares that provide weighted CPU scheduling for low‐priority batch jobs in container‐based cloud. To implement the differentiated shares, we extend resource reservation interfaces provided by Kubernetes and suggest an algorithm that maps the resource management attributes of Kubernetes into those of Linux. The proposed differentiated shares can avoid worsening interference with high‐priority containers by exploiting the hierarchical resource sharing of task groups in Linux. In addition, we suggest a node scoring policy to provide efficient inter‐node job scheduling with the consideration of the differentiated shares. The performance measurement results show that the differentiated shares can provide weighted CPU scheduling for best‐effort batch containers without interference with high‐priority containers (less than 3% with respect to application‐level performance). [ABSTRACT FROM AUTHOR]

Thanks to advancements in artificial intelligence and the Internet of Things (IoT), eHealth is becoming an increasingly attractive area for researchers. However, different challenges arise when sensor-generated information is stored and analyzed using cloud computing. Latency, response time, and security are critical concerns that require attention. Fog and Edge Computing technologies have emerged in response to the requirement for resources near the network edge where data is collected, to minimize cloud challenges. This paper aims to assess the effectiveness of Machine Learning (ML) and Deep Learning (DL) techniques when executed in Edge or Fog nodes within the eHealth data. We compared the most efficient baseline techniques from the state-of-the-art on three eHealth datasets: Human Activity Recognition (HAR), University of Milano Bicocca Smartphone-based Human Activity Recognition (UniMiB SHAR), and MIT-BIH Arrhythmia. The experiment showed that for the HAR dataset, the Support Vector Machines (SVM) model was the best performer among the ML techniques, with low processing time and an accuracy of 96%. In comparison, the K-Nearest Neighbors (KNN) performed 94.43, and 96%, respectively, for SHAR and MIT-BIH datasets. Among the DL techniques, the Convolutional Neural Network with Fourier (CNNF) model performed the best, with accuracies of 94.49% and 98.72% for HAR and MIT-BIH. In comparison, CNN achieved 96.90% for the SHAR dataset. [ABSTRACT FROM AUTHOR]

The advent of the Internet of Things (IoT) has revolutionised our interaction with the environment, facilitating seamless connections among sensors, actuators, and humans. Efficient task scheduling stands as a cornerstone in maximising resource utilisation and ensuring timely task execution in IoT systems. The implementation of efficient task scheduling methodologies can yield substantial enhancements in productivity and cost-effectiveness for IoT infrastructures. To that end, this paper presents the IoT-mist bat-inspired algorithm (IMBA), designed specifically to optimise resource allocation in IoT environments. IMBA's efficacy lies in its ability to elevate user service quality through enhancements in task completion rates, load distribution, network utilisation, processing time, and power efficiency. Through comparative analysis, IMBA demonstrates superiority over traditional methods, such as fuzzy logic and round-robin algorithms, across all performance metrics. [ABSTRACT FROM AUTHOR]

Clouds are key to understanding the atmosphere and climate, and a long series of satellite observations provide invaluable information to study their properties. EUMETSAT has published Release 1 of the Optimal Cloud Analysis (OCA) Climate Data Record (CDR), which provides a homogeneous time series of cloud properties of up to two overlapping layers, together with uncertainties. The OCA product is derived using the 15 min Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements onboard Meteosat Second Generation (MSG) in geostationary orbit and covers the period from 19 January 2004 until 31 August 2019. This paper presents the validation of the OCA cloud-top pressure (CTP) against independent lidar-based estimates and the quality assessment of the cloud optical thickness (COT) and cloud particle effective radius (CRE) against a combination of products from satellite-based active and passive instruments. The OCA CTP is in good agreement with the CTP sensed by lidar for low thick liquid clouds and substantially below in the case of high ice clouds, in agreement with previous studies. The retrievals of COT and CRE are more reliable when constrained by solar channels and are consistent with other retrievals from passive imagers. The resulting cloud properties are stable and homogeneous over the whole period when compared against similar CDRs from passive instruments. For CTP, the OCA CDR and the near-real-time OCA products are consistent, allowing for the use of OCA near-real time products to extend the CDR beyond August 2019. [ABSTRACT FROM AUTHOR]

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model's performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~ 7.3 × 10 − 4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6 × 10 − 4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products. [ABSTRACT FROM AUTHOR]

In satellite imagery, clouds obstruct the ground information, directly impacting various downstream applications. Thus, cloud annotation/cloud detection serves as the initial preprocessing step in remote sensing image analysis. Recently, deep learning methods have significantly improved in the field of cloud detection, but training these methods necessitates abundant annotated data, which requires experts with professional domain knowledge. Moreover, the influx of remote sensing data from new satellites has further led to an increase in the cost of cloud annotation. To address the dependence on labeled datasets and professional domain knowledge, this paper proposes an automatic cloud annotation method for satellite remote sensing images, CloudAUE. Unlike traditional approaches, CloudAUE does not rely on labeled training datasets and can be operated by users without domain expertise. To handle the irregular shapes of clouds, CloudAUE firstly employs a convex hull algorithm for selecting cloud and non-cloud regions by polygons. When selecting convex hulls, the cloud region is first selected, and points at the edges of the cloud region are sequentially selected as polygon vertices to form a polygon that includes the cloud region. Then, the same selection is performed on non-cloud regions. Subsequently, the fast KD-Tree algorithm is used for pixel classification. Finally, an uncertainty method is proposed to evaluate the quality of annotation. When the confidence value of the image exceeds a preset threshold, the annotation process terminates and achieves satisfactory results. When the value falls below the threshold, the image needs to undergo a subsequent round of annotation. Through experiments on two labeled datasets, HRC and Landsat 8, CloudAUE demonstrates comparable or superior accuracy to deep learning algorithms, and requires only one to two annotations to obtain ideal results. An unlabeled self-built Google Earth dataset is utilized to validate the effectiveness and generalizability of CloudAUE. To show the extension capabilities in various fields, CloudAUE also achieves desirable results on a forest fire dataset. Finally, some suggestions are provided to improve annotation performance and reduce the number of annotations. [ABSTRACT FROM AUTHOR]

Computerized advances affected individuals' lives. Most of these computerized gadgets depend on cloud capacity to meet their memory needs. A huge number of pictures, recordings, and sound documents are being moved to distributed storage. Large number of individuals all throughout the planet access these media consistently. Unapproved admittance to these media should be stayed away from. One of the flimsy parts for information breaks is the client end encryption. This paper proposes a procedure for further developing cloud information security by consolidating the AES and blowfish encryption and decoding calculations. AES-256 is utilized as the main layer, followed by blowfish as the subsequent layer, in the crossover arrangement. The yield of the principal layer is contribution to the second layer and the eventual outcome is examined. The proposed strategy likewise talks about other consolidated approaches, for example, AES with other customary calculations however the proposed strategy gives huge results contrasted with different methodologies. [ABSTRACT FROM AUTHOR]

The adoption of cloud computing for providing resource and delivering services is an irreversible trend. For most IT companies, the quality of provided services is strongly dependent on reliability and performance of the underlying cloud technologies. A widely studied problem that can greatly affect the user experience is runtime software aging, whose main countermeasure is a proactive maintenance action known as rejuvenation. This article reviews the effort conducted so far by the software aging and rejuvenation (SAR) community in the cloud domain. A set of 105 papers has been examined from three source digital libraries in order to have a clear view of the state of the art. The paper characterizes the cloud-related SAR literature according to four dimensions: the publication trends, the aging analysis methods and metrics, the rejuvenation solutions, the validation approach. Results witness an increasing interest in this area (with 58% of the studies published in the last 5 years), an equivalent role of measurement-based and model-based solutions for aging analysis (42 and 40 studies, respectively) and a prevalent interest for rejuvenation (76/105 of the studies deals with rejuvenation). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Entrepreneurship, Management & Innovation is the property of Wyzsza Szkola Biznesu-National Louis University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In cloud services and Internet-of-Things (IoT) applications, the cybersecurity in video transmission technologies has drawn much attention in recent researches due to the rapid growth of cyber-risks on both individuals and institutions. Unfortunately, the spoofing attack, a kind of cyber-risk, has increased the number of cyber-criminals in data transfer applications without being detected, especially in smart cities. Several applications based on online video communications, such as online testing and video conferences, are involved in smart cities. The video displays various variations of a person, which makes face recognition an important concept in security implementation. The face spoofing attacks are mainly based on the person's face replication by replaying a video or by printed photos. Therefore, video forgery detection and related spoof attack detection have become a new topic in cybersecurity research. From this perspective, this paper presents a deep learning approach for video face forensic detection with a cyber facial spoofing attack using two methodologies. The first methodology is based on a convolutional neural network (CNN) to extract features from the input video frames. The model has five convolutional layers followed by five pooling layers. The second methodology is based on convolutional long short-term memory (ConvLSTM). This model comprises two pooling layers, two convolutional layers, and a convolutional LSTM layer. Each methodology includes a fully-connected layer to interconnect between the feature map resulting from the feature extraction process and the classification layer. A SoftMax layer performs the classification task in each method. This paper aims to achieve an optimum modality for face forensic detection to overcome spoofing attacks. Simulation results reveal that the ConvLSTM with CNN methodology achieves better classification results as the extracted features are more comprehensive than those of other conventional approaches. Also, it achieves an accuracy of 99%, and the works presented in the literature achieve an accuracy levels up to 95%. [ABSTRACT FROM AUTHOR]

The artificial intelligence (AI) industry is increasingly integrating with diverse sectors such as smart logistics, FinTech, entertainment, and cloud computing. This expansion has led to the coexistence of heterogeneous applications within multi-tenant systems, presenting significant scheduling challenges. This paper addresses these challenges by exploring the scheduling of various machine learning workloads in large-scale, multi-tenant cloud systems that utilize heterogeneous GPUs. Traditional scheduling strategies often struggle to achieve satisfactory results due to low GPU utilization in these complex environments. To address this issue, we propose a novel scheduling approach that employs a genetic optimization technique, implemented within a process-oriented discrete-event simulation framework, to effectively orchestrate various machine learning tasks. We evaluate our approach using workload traces from Alibaba's MLaaS cluster with over 6000 heterogeneous GPUs. The results show that our scheduling improves GPU utilization by 12.8% compared to Round-Robin scheduling, demonstrating the effectiveness of the solution in optimizing cloud-based GPU scheduling. [ABSTRACT FROM AUTHOR]

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems. [ABSTRACT FROM AUTHOR]

The proper use of protective equipment is very important to avoid fatalities. One sector in which this has a great impact is that of construction sites, where a large number of workers die each year. In this sector as in others, employers are responsible for providing their employees with this equipment. In addition, employers must monitor and ensure its correct use. These tasks are usually performed using manual procedures. Existing tools to automate this process are unreliable and present scalability issues. In this paper, we research the benefits of using a cloud-edge computing architecture to automate the monitoring of protective equipment. The solution we propose successfully addresses all the problems that appear in hostile and unstructured work environments such as construction sites. Although these sites are used as a use case, the approach presented can also be deployed in other sectors with similar characteristics and restrictions. [ABSTRACT FROM AUTHOR]

Cloud computing and the increasing popularity of 5G have greatly increased the application of images on Internet of Things (IoT) devices. The storage of images on an untrusted cloud has high security and privacy risks. Several lightweight cryptosystems have been proposed in the literature as appropriate for resource-constrained IoT devices. These existing lightweight cryptosystems are, however, not only at the risk of compromising the integrity and security of the data but also, due to the use of substitution boxes (S-boxes), require more memory space for their implementation. In this paper, a secure lightweight cryptography algorithm, that eliminates the use of an S-box, has been proposed. An algorithm termed Enc, that accepts a block of size n divides the block into L n R bits of equal length and outputs the encrypted block as follows: E = L ⨂ R ⨁ R , where ⨂ and ⨁ are exclusive-or and concatenation operators, respectively, was created. A hash result, h a s R = S H A 256 P ⨁ K , was obtained, where SHA256, P, and K are the Secure Hash Algorithm (SHA−256), the encryption key, and plain image, respectively. A seed, S, generated from e n c h a s h = E n c h a s h e n c , K , where hashenc is the first n bits of hasR, was used to generate a random image, Rim. An intermediate image, i n t i m a g e = R i m ⨂ P , and cipher image, C = E n c i n t i m a g e , K , were obtained. The proposed scheme was evaluated for encryption quality, decryption quality, system sensitivity, and statistical analyses using various security metrics. The results of the evaluation showed that the proposed scheme has excellent encryption and decryption qualities that are very sensitive to changes in both key and plain images, and resistance to various statistical attacks alongside other security attacks. Based on the result of the security evaluation of the proposed cryptosystem termed Hash XOR Permutation (HXP), the study concluded that the security of the cryptography algorithm can still be maintained without the use of a substitution box. [ABSTRACT FROM AUTHOR]

The current vision of internet of things aims at connecting anything with everything. It is estimated that there will be 18 billion connected devices in 2022. Applications like utility meters, robotics, smart street lighting, process automation, solar and wind farms are expected to grow. High end requirements for automated driving, industrial automation and e-health exist. Cellular IOT is expected to bring new use cases to address latest requirements in the market. There is a need to provide large coverage in a power efficient manner while providing a high battery life. There is a need to have a kit which connects seamlessly and has a small form factor. The requirements on latency and throughput are relaxed in some cases while stringent in others. Stringent requirements make use of more radio resources. There is increased demand for system capacity and network availability. In this paper we make use of nRF9160 kit a low-cost device where a reduction in the cost and complexity has been achieved. The performance objectives of coverage, throughput, latency, capacity, power efficiency and complexity are met. This kit provides a reliable and future proof solution in the long term. The kit is built for the global market and allows roaming over multiple networks. [ABSTRACT FROM AUTHOR]

Cloud computing is a computing paradigm that provides various services and computing resources to users through Cloud Service Providers over the Internet. However, storing data in an unsecure cloud may lead to security issues such as privacy issues and data leakage. Therefore, it is necessary for encryption schemes to be implemented in clouds to provide a secure environment for the users. One of the cryptographic schemes is the Attribute Based Encryption (ABE), which provides privacy and access control in cloud and can be implemented in a Trusted Real-Time Execution Environment to achieve stronger security. This paper first outlines the various existing encryption techniques, which can be categorized into symmetric and asymmetric algorithms. It then comprehensively explore the key policy and the access policy attribute based encryption. Furthermore, this paper examines the various schemes of ABE and compares the schemes based on its access structure, advantages and disadvantages. Lastly, this paper discusses the applications of key policy and ciphertext policy ABE. [ABSTRACT FROM AUTHOR]

In a previous work (Hernández‐Bernal et al., 2021, https://doi.org/10.1029/2020je006517) we performed an observational analysis of the Arsia Mons Elongated Cloud (AMEC), which stands out due to its impressive size and shape, quick dynamics, and the fact that it happens during the Martian dusty season. Observations show that its morphology can be split in a head, on the western slope of the volcano of around 120 km in diameter; and a tail, that expands to the west reaching more than 1,000 km in length, making the AMEC the longest orographic cloud observed so far in the solar system. In this work we run the Laboratoire de Météorologie Dynamique Mesoscale Model to gain insight into the physics of the AMEC. We note that it is coincident in terms of local time and seasonality with the fastest winds on the summit of Arsia Mons. A downslope windstorm on the western slope is followed by a hydraulic‐like jump triggering a strong vertical updraft that propagates upwards in the atmosphere, causing a drop in temperatures of down to 30 K at 40–50 km in altitude, spatially and temporarily coincident with the observed head of the AMEC. However the model does not reproduce the microphysics of this cloud: the optical depth is too low and the expansion of the tail does not happen in the model. The observed diurnal cycle is correctly captured by the model for the head of the cloud. This work raises new questions that will guide future observations of the AMEC. Plain Language Summary: This is the second paper of our research on the Arsia Mons Elongated Cloud (AMEC), which is a visually impressive cloud on Mars. It appears on the western flank of the Arsia Mons volcano during a specific season right at sunrise. For 3 hr it grows, developing a thin elongated tail that has been observed to be as long as 1,800 km. In our previous work we described available observations. In this work we run a high resolution atmospheric model that captures the effect of the Arsia Mons volcano on the atmosphere. This model shows that due to the presence of the volcano and its effect on the wind, air is forced upwards next to the volcano, leading to a drop in temperatures of 30°C, which causes the formation of the cloud under extreme conditions of humidity. This is a success of the model that provides a new understanding of this outstanding cloud, however, the accurate physics behind the extreme expansion of the AMEC are not fully understood yet. This work solves some questions and raises many new ones, which will be an aid in the planning of new observations. Key Points: We performed mesoscale model dynamic simulations of the Arsia Mons Elongated Cloud observed in the Martian southern solsticeTopography‐induced circulation causes temperatures to drop by about 30 K at the observed origin location and local time of the cloudThe cloud tail is much more elongated in the observations than in the model, which challenges our understanding of winds and microphysics [ABSTRACT FROM AUTHOR]

Efficient resource scheduling is one of the most critical issues for big data centers in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models. [ABSTRACT FROM AUTHOR]

This special issue is the eleventh in an important tradition in the computer architecture community: IEEE Micro's Top Picks from the Computer Architecture Conferences. This tradition provides a means for sharing a sample of the best papers published in computer architecture in 2013 with the IEEE Micro readership and researchers in the computer architecture community. [ABSTRACT FROM AUTHOR]

Like many industries, the capital markets industry relies on reliable access to trustworthy data. For this industry in particular, it is becoming critical for success, if not survival. This type of data access, however, requires modern data infrastructures, and not many organisations are prepared to furnish such infrastructures. This paper introduces ways to modernise the data infrastructure without ripping out and replacing expensive hardware. It covers key capabilities of modern data infrastructures and compares and contrasts emerging technologies such as cloud data warehouses, data lakes, data virtualisation, logical data fabric and data mesh. The paper provides a business case for complementing investments in centralised data management tools, such as cloud data warehouses or data lakes, with a logical data management approach. [ABSTRACT FROM AUTHOR]

This paper discusses the design of a novel hybrid bioinspired model for task-and-VM-dependency and deadline aware scheduling via dual service level agreements. The model uses a combination of grey wolf optimization with the league championship algorithm, to perform efficient scheduling operations. These optimization techniques model a fitness function that incorporates task make-span, task deadline, mutual dependencies with other tasks, the capacity of VMs, and energy needed for scheduling operations. This assists in improving its scheduling performance for multiple use cases. To perform these tasks, the model initially deploys a task-based service level agreement (SLA) method, which assists in enhancing task and requesting-user diversity. This is followed by the design of a VM-based SLA model, which reconfigures the VM's internal characteristics to incorporate multiple task types. The model also integrates deadline awareness along with task-level and VM-level dependency awareness, which assists in improving its scheduling performance under real-time task and cloud scenarios. The proposed model is able to improve cloud utilization by 8.5%, increase task diversity by 8.3%, reduce the delay needed for resource provisioning by 16.5%, and reduce energy consumption by 9.1%, making for a wide variety of real-time cloud deployments. [ABSTRACT FROM AUTHOR]