Reviewerly/exHarmony huggingface dataset:
Resource Request Handling Mechanisms for Effective VM Placements in Cloud Environment

In a cloud computing environment, effective resource request handling and their dynamic management can optimize the VM placement process. An efficient resource request handling can resolve the problem of increased waiting time as well as load imbalance rate during the course of running the VM resources. Load balancing techniques only helps to reschedule the VMs in the appropriate PMs based on the dynamic changes in the load. To make use of the resources in an effective way, an optimized resource scheduling and load balancing model is necessary along with the proper model to manage the resource request made by the cloud users. A rule based method utilizing the M/M/C queue model for effective management of resource request handling is presented in this work. The RB-M/M/C queue model can ensure the availability of cloud datacenter services by managing user requests. This further enriches the cloud service by increasing the physical server utilization rate and minimizing the waiting time and number of servers used to fulfill the requirements of the users. The resource request queue management algorithm is designed by utilizing some suitable modesties of queuing theory. Simulation results for performance analysis shows that the proposed model decreases the waiting time and at the same time increases the utilization rate.
Self-adaptive Hadoop Cluster for Data Analysis Applications

With continuous improvements of information and technology, the world is witnessing an exponential growth in the generation and processing of data. The handling of big data has become a challenge. Hadoop clusters have emerged as a solution to tackle this problem by sharing the processing power between each node in the cluster, hence boosting the processing speed of data analysis applications. In order to improve the Hadoop cluster and help in improving the throughput and with balanced CPU utilization in all the slave nodes, the Hadoop cluster could be programmed to be self-adaptive. Such that, before executing tasks on Hadoop, depending on the dataset file size, the cluster will dynamically shrink or expand. This will improve the overall performance of the system by allocating an appropriate number of slave nodes based on the size of the input file and then will proceed with MapReduce processing.
Sustainable Cloud Computing in the Supply Chains

Contrary to the popular belief that cloud-only helps save costs, it can help drive end-to-end real-time visibility, intelligent automation, scalability, agility, and speed. The Supply chain industry is prime for cloud computing because of the large number of internal and external stakeholders, including suppliers and partners collaborate to facilitate the seamless delivery of products and services to consumers. The profitability and survival of an organization in the long term are closely associated with sustainability measures, but unfortunately, digitalization models in the supply chain often ignore these aspects. The cloud-based self-thinking supply chains and workloads are more sustainable, elastic, and scalable than resources used in traditional data centers. The paper aims to bridge the gap between supply chain practitioners and academic researchers by seeking to understand how sustainable cloud computing will help to address key supply chain opportunities and challenges.
Evaluation of Underwater Images Using Genetic Algorithm-Monitored Preprocessing and Morphological Segmentation

In a variety of domains, image processing-supported Regions of Interest (RoIs) are widely used to evaluate specific areas of digital images. In order to achieve a better result, a combination of image evaluation procedures is combined together to evaluate the ROI in complex and blurred images. In the proposed work, a scheme will be developed for obtaining an accurate ROI in underwater RGB scale pictures. There are four phases to the proposed approach; (i) Image collection and resizing, (ii) Kapur’s image thresholding based on Genetic Algorithms (GAs) integrated with firefly and particle swarm optimization, (iii) Identifying the ROI and comparing it with the binary mask, and (iv) evaluating and validating its performance. Recently, GA has been applied to a new application known as Mayfly Algorithm (MA) which incorporates the best features of other successful heuristic methods along with GA to produce the Mayfly Algorithm (MA). In this study, a suitable scheme for analyzing underwater images with GA based on the optimally assigned gene and chromosome will be developed. With the proposed approach, the proposed work is tested and evaluated using the UFO-120 benchmark database, which results in better Jaccard, Dice, and accuracy values.
IGA: An Improved Genetic Algorithm for Real-Optimization Problem

The selection of crossover and mutation procedures greatly influences the searchability, convergence effectiveness, and precision of genetic algorithms. The dimensional crossover and mutation operations of the straightforward genetic algorithm are refined in this chapter to create the improved genetic algorithm called IGA. IGA is tested using eight real-optimization applications of nonlinear equation systems and the CEC 2018 test functions. The qualitative results demonstrate that IGA is compared to different optimization algorithms in the literature; the algorithm can enhance global search capability, convergence efficiency, and precision and increase the success rate of convergence to the optimal value under the same experimental circumstances. The quantifiable outcomes demonstrate the algorithm’s higher performance in the major evaluated functions. The algorithm has a considerable advantage at 95% confidence intervals, as shown by the Wilcoxon rank-sum and Friedman rank tests used for statistical analysis.
Verify De-Duplication Using Blockchain on Data with Smart Contract Techniques for Detecting Errors on Cloud

Recently, cloud computing has becoming increasingly popular. To store an excessive amount of knowledge there, many consumers choose cloud services that provide knowledge outsourcing. The demand for blockchain innovation, as well as the significance of its application, has fuelled continuous study in a wide range of scientific and practical disciplines. Despite its infancy, the blockchain is seen as a forward-thinking solution to present technical difficulties such as decentralisation, identification, trust, data ownership, and information-driven decisions. Many applications rely on blockchain technology, including supply chain, Internet of Things, multimedia, healthcare, and cloud computing. Blockchain is now one of the most sophisticated technologies for ensuring the protection of sensitive or secret data. Existing systems have drawbacks, including decrease in availability, knowledge Misfortune and corruption, breach of privacy, Lock-in of the merchandiser Leaving DEPSKY overcomes these restrictions, but it has significant computing costs and no error warning system. As per research one-of-a-kind affordable on a trustworthy encrypted network, de-duplication data contracting out the cloud with speedy recovery to overcome this issue. Main objective is to push for the elimination of persistent files stored online, so after each upload by a user file, the quick recovery technique recovers files more quickly than existing methods after checking for deduplication, followed by the three error detection methods that determine whether or not files shadows have been successfully compromised. This research surpasses previous systems and meets all basic security requirements.
Adoption of Network Function Virtualization Technology in Education

Network function virtualization is a method of cost reduction trigger services of cloud deployment for network operations. It separates the functionalities like a firewall or encryption from a dedicated source or hardware and moving to virtual servers. The network function virtualization has seen keen attention and grasping from both industries and academic institutions as a transformation in telecommunications. This technology allows compactible provisioning, deployable and provides a centralized system of virtual private network functions. On developing a Web-based application or game-based software, we have to see to that the application is safer and secure with all encryption standards. That safety can be provided by network function virtualization standards and its concepts. At the same point, there are benefits and significant challenges and appropriate solutions of network function virtualization; henceforth, it pays the way for future research in these domains.
Literature Survey on Empirical Analysis on Efficient Secured Data Communication in Cloudcomputing Environment

Cloud computing (CC) is an accessibility of computer resource for the storage of data. CC comprises remote servers for storing the information. CC gives users for storing the information within third-party data. CC security protects cloud environment, data, information and applications against the unauthorized access. Storage security secures the data storage systems. User authentication is establishing the user identity in fast access network resource by using authorizing transfer into verified user authenticity. Many researchers carried out their research on protected data within cloud. But, the data confidentiality rate was not enhanced as well as the time was not minimized during secured communication in cloud. The major contribution of the study is used for reviewing different data methods within cloud environment.
Blockchain for Supply Chain for Perishable Goods

Maintaining transparency in the perishable goods supply chain is critical to ensure that the items are not tampered with and that customers have faith in the products. This work aims to solve this problem utilizing blockchain technology, which is well-known for its decentralization, immutability, and trustless nature. The research goal was to develop an Android application that is both cost-free, easily deployable, and easy to use for recording product data and the supply chain. Before purchasing a product, a consumer might use this app to scan the QR (Quick Response) code to retrieve all of the product’s details and supply chain to ensure that the product has not been tampered with and is still within its shelf life.
Genetic Algorithm Applications for Challenging Real-World Problems: Some Recent Advances and Future Trends

Originated from the work of J. Holland in the 70s, genetic algorithms have become one of the most popular and widely used computational developments for optimization and global search tasks. As a result, genetic algorithms have been intensively used in many scientific and engineering problems during the last few decades. Also, several variants of the original genetic algorithm approach have been defined in the literature, mostly tailored to cope with specialized real-world optimization problems in industry and other computer-assisted areas. In this context, this chapter offers a gentle description of the fundamentals and background of genetic algorithms. Then, it explores some of the most exciting recent advances regarding the application of genetic algorithms to challenging real-world problems in nonstandard fields, such as reverse engineering for manufacturing (curve and surface reconstruction from point clouds), medicine and bioinformatics (cancer prediction, detection, and diagnosis; cancer classification and treatment; and COVID-19), computer animation and video games (behavioral animation of NPCs for video games), and robotics (robot path planning). The chapter discusses how genetic algorithms can be successfully applied to address these problems, including several variants and/or hybridizations with other approaches for better performance. Some future trends toward the application of genetic algorithms in other potential fields and some current challenges for future work in the field are also outlined in this chapter.
Technological Choice and Development

Economic developmentEconomic development is directly related to the ability of an economic actor to produce competitive products.
Window State or Action Modeling? An Explainable AI Approach in Offices

Window operation significantly impacts energy use and indoor environmental quality in buildings. Individuals behave differently, making it difficult for models trained on a specific dataset to encompass the entire spectrum of these actions. As a result, a generalizable model that is capable of predicting the behavior of diverse occupants is necessary. Hence, this paper introduces a systematic approach for capturing its diversity in offices and building a model towards generalizability. The approach involves state and action modeling through a Random Forest algorithm on the ASHRAE Global Occupant Behavior Database. The data pre-processing, hyperparameter tuning, and evaluation are deeply described and applied to imbalanced window datasets. Our results demonstrate that including metadata in a state model and applying a G-means threshold moving technique can result in an F1-score of 0.74. This score slightly outperformed the state model, which was trained only on its own dataset and achieved an F1-score of 0.73. However, both models had similar accuracies of 77%. The action model did not fare as well as the state models, with an F1-score and accuracy score of just 0.42 and 49%, respectively. In contrast, the action model shows more explainable results for domain experts than state models.
Checkpoint Snapshot Placement in the Cloud Data Center Using Fuzzy Inference System

Various services and scalable computing resources are made available via the internet by cloud computing. Cloud reliability has recently grown significantly as a result of features that enable various tenants to share and multiplex computer resources. The stability of cloud services like those based on virtual servers (VS) must be improved in a cloud data center (CDC), though. To guarantee the dependability of virtual servers (VSs) in the (Infrastructure as a Service (IaaS) cloud model, this study suggests two fuzzy inference systems (FIS), referred to as Physical Server FIS1 (PSFIS1) and PSFIS2 for fault tolerance of VSs in CDC. Checkpointing serves as a fault tolerance (FT) process for VSs in the suggested inference systems. These systems assist in choosing the best physical servers (PSs) to use for saving the snapshot of tasks running on the virtual servers (VSs). To compare the suggested FISs in terms of complication, elasticity, and improved PS selection, the projected FIS is implemented in MATLAB. Though the PSFIS1 is less sophisticated than PSFIS2, the PSFIS2 is more elastic and produces an improved selection of PS than PSFIS1, according to simulation results.
An Augmented Reality Framework as a Solution to Enhance the Experience of Visiting a Museum

The technological advances converge with digital transformation in order to improve our everyday life. Cultural heritage is one of the application areas that may take advantage of this progress, for example, museums, bringing several new experiences to their visitors. However, several digital solutions return static and simple data and contents that are often uninteresting to the visitors nor promote their integration with a museum visit. This paper proposes an augmented reality framework, as a multimedia content and information transmission solution, in the indoor environment of a museum, focusing on a dynamic approach, which increases the immersion of the visitor into the visit storyline. The framework architecture was developed as a solution to a real problem, stated at the Foz Côa museum, in Portugal. An augmented reality contextualization was carried out on that museum, as well as a comparative study between two augmented reality tools, in order to determine the most appropriate to use. Also, the accuracy of the developed framework was tested, under different conditions, in the real museum environment, as an experimental result of this study
Bridging the Analogue–digital Divide in Stakeholder Engagement

Visualisation plays an increasingly important role in the planning and design of natural and urban environments, where it now contributes to advancements in landscape representation, critical assessment, and decision-making through stakeholder participation. Visualisations are a core component of communication and dissemination within various formats of project representation, publications, workshops, and stakeholder involvement. In this project, we utilised the latest augmented reality systems to explore how it can be used to bridge the analogue–digital divide. We explored the utility of augmenting workflows for stakeholder participation workshops. We developed a suite of apps, for both tablet-based and headset-based augmented reality, to allow multiuser interactions, blending data from analogue sources, such as maps, sketches, models, and the environment, with digital data, such as GIS layers, flood simulations, and digital 3D models. To advance this aim, we developed novel occlusion methods for enriching physical models with digital twins, created novel environmental-driven augmented reality frameworks, and presented the adaptive visualisation framework for augmented reality application development.
Untangling Explainable AI in Applicative Domains: Taxonomy, Tools, and Open Challenges

Recently, a paradigm shift is observed toward Industry 5.0, where tasks (processes) are automated at massive scales. This shift has initiated modern developments in artificial intelligence (AI) to support a plethora of applications like manufacturing, health care, vehicular net- works, and others. However, owing to the black-box nature of AI models, the research has shifted toward the proposal of novel techniques that aim toward the explainability and validity of these AI models. Thus, explainable AI (XAI) has become a norm in modern applicative domains, and the study of its frameworks and tools has become the buzzword among researchers. Thus, the paper intends to present the key concepts of XAI and aims at improving the model transparency. The survey systematically untangles the key concepts of XAI and presents a solution taxonomy in different applications. Modern XAI techniques are classified as self-explanatory, visual-based-model-agnostic, global surrogate, and local surrogate-model-agnostic. We also cover the tools and frameworks of XAI and discuss the open issues and challenges in practical realization. Thus, the survey intends to arm AI practitioners to design optimal solutions to realize XAI in practical use-case setups.
A Comparative Review on Image Interpolation-Based Reversible Data Hiding

Reversible data hiding (RDH) is one of the most crucial issues in the field of information security in the modern world, as it enables us for securing communication by hiding secret data in a cover media. With the help of this, it also lets us extract the secret data without losing any original data at the receiver side, i.e., called as lossless recovery. RDH is very suitable for medical images for example MRI scans, CT scans, and in military applications. In the past few years, several RDH approaches have been developed, for example, difference expansion, histogram shifting, prediction error expansion, and interpolation. This review paper focuses on different reversible data-hiding strategies that are basically based on interpolation techniques in order to get a good visual image quality and embedding capacity, which have been attempted by many researchers to improve it at the same time.
Automatic SMS Spam Filtering for Disaster Response Using Classification Algorithms

Short Message Service (SMS) acts as one of the media of communication for the mobile device system due to rapid increase of mobile users. During disasters, the government provides rescue agencies for the people to deal with emergencies. Since there exist critical positions during the time of disasters, the rescue agencies cannot categorize the messages based on emergency. So, a hybrid system of the SMS spam filtering method is used to classify messages like spam, ham, alert1, alert2 and alert3 using machine learning algorithms such as Naive Bayesian Algorithm and Decision Tree Algorithm. Since it is a hybrid system significant messages are considered effectively by the rescue agencies and help the victims.
Deraining of Image Using UNet-Based Conditional Generative Adversarial Network

Adverse weather conditions like rain, fog and storm cause degradation in the quality of an image. Computer vision operations such as detection, classification and various monitoring of objects activities are adversely affected due to image degradation. Hence, image enhancement is an important pre-processing step. We propose a method for the elimination of rain streaks from rain-affected images by making use of conditional generative adversarial networks. We have used structural similarity index measure and peak signal-to-noise ratio as evaluation metrics evaluate the model. We have tested our model on one synthetic and four real-world datasets and compared the performance with other state-of-the-art methods. We have obtained a generated image that has a close resemblance with the ground truth images.
Managing Database Trigger Design

How to systematically check constraints in databases other than the standard constraints, e.g., (primary) keys and foreign keys? To solve this important problem in practice, one can use triggers, i.e., special database procedures that automatically start (‘fire’) upon attempts to modify data in a table. In practice, designing, writing, and maintaining triggers consistently is subtle, difficult, and error-prone, for many reasons: E.g., when multiple triggers are activated ‘at the same time’ or different trigger-types are mixed, the behaviour can become quite subtle or unpredictable, e.g., regarding their execution-order. Or when triggers invoke external actions, the triggering transaction might not be committed after all. Moreover, keeping triggers consistent when they must be changed is also very error-prone. So, it is very desirable to be able to (re)generate such triggers in a disciplined way. Therefore, our research question is how to manage the proper execution of checking constraints in database practice. To tackle these problems, we systematically assemble the triggers that should be activated ‘at the same time’ into one trigger. In that way, we keep control over what happens, and when. It keeps the trigger-order platform-independent as well. Using standard templates, we describe a generic way for generating triggers from a declarative specification. We intensively used and evaluated this approach in practice. Constraints between and within tables can be treated in this way. Using triggers, we also propose a straightforward implementation of SQL’s assertions. To develop triggers consistently, several design-decisions must be made. E.g., should the trigger stop checking after detecting the first violation or find and mention all violations? And what to do when there is a violation? E.g., a rollback or some ‘repair’, e.g., a cascading delete? We discuss and answer all these questions.