QUANTUM COMPUTING AND COMMUNICATIONS

Today, like never before, we are witnessing a pervasive diffusion of ultra-broadband fixed-mobile connectivity, the deployment of Cloud-native 5G network and service platforms, and the wide adoption of Artificial Intelligence. It has the so-called Digital Transformation of our Society: as a matter of fact, the transformative role of Telecommunications and Information Communication Technologies (ICT) has long been witnessed as a precursor of scientific progress and economic growth in the modern world. Nevertheless, this transformation is still laying its foundations on Electronics and the impending end of Moore’s Law: therefore, a rethinking of the long-term ways of doing computation and communications has been already started. Among these different ways, quantum technologies might trigger the next innovation breakthrough in the medium long-term. In this direction, the paper provides an overview of the state of the art, challenges, and opportunities posed by an expected second wave of quantum technologies and services.

VIRTUAL DESKTOP INFRASTRUCTURE IN HIGHER EDUCATION INSTITUTION: AN APPLICATION OF HOME AND MOBILE COMPUTING ENVIRONMENT

Currently, universities have rising demands to apply the incredible recent developments in computer technology that support students to achieve skills necessary for developing applied critical thinking in the contexts of online society. Medical and engineering subjects’ practical learning and education scenarios are crucial to attain a set of competencies and applied skills. These recent developments allow sharing and resource allocation, which brings savings and maximize use, and therefore offer centralized management, increased security, and scalability. This paper describes the implantation of Virtual Desktop Infrastructure (VDI) to access the virtual laboratories that bring efficient use of resources as one of Al Balqa Applied University’s (BAU) Private Cloud services. The concept of desktop virtualization implements the sharing of capabilities utilizing legacy machines, which reduces the cost of infrastructure and introduces increased security, mobility, scalability, agility, and high availability. Al Balqa Applied University uses the service extensively to facilitate in/off-campus learning, teaching, and administrative activities and continue performing their work and education functions remotely to cope with the COVID-19 pandemic.

OBJECT RECOGNITION FOR AUGMENTED REALITY APPLICATIONS

Developments in the field of neural networks, deep learning, and increases in computing systems’ capacity have allowed for a significant performance boost in scene semantic information extraction algorithms and their respective mechanisms. The work presented in this paper investigates the performance of various object classification- recognition frameworks and proposes a novel framework, which incorporates Super-Resolution as a preprocessing method, along with YOLO/Retina as the deep neural network component. The resulting scene analysis framework was fine-tuned and benchmarked using the COCO dataset, with the results being encouraging. The presented framework can potentially be utilized, not only in still image recognition scenarios but also in video processing.

SENTIMENT ANALYSIS OF CUSTOMER REVIEWS

Sentiment analysis is a process in which we classify text data as positive, negative, or neutral or into some other category, which helps understand the sentiment behind the data. Mainly machine learning and natural language processing methods are combined in this process. One can find customer sentiment in reviews, tweets, comments, etc. A company needs to evaluate the sentiment behind the reviews of its product. Customer sentiment can be a valuable asset to the company. This ultimately helps the company make better decisions regarding its product marketing and improving product quality. This paper focuses on the sentiment analysis of customer reviews from Amazon. The reviews contain textual feedback along with a rating system. The aim is to build a supervised machine learning model to classify the review as positive or negative. As reviews are in the text format, there is a need to vectorize the text to numerical format for the computer to process the data. To do this, we use the Bag-of-words model and the TF-IDF (Term Frequency-Inverse Document Frequency) model. These two models are related to each other, and the aim is to find which model performs better in our case. The problem in our case is a binary classification problem; the logistic regression algorithm is used. Finally, the performance of the model is calculated using a metric called the F1 score.

DEFINING PARAMETERS FOR THE OSCILLATION MODEL OF LOAD FLOW OF GLOBAL ACTIVITIES IN A FULLY DISTRIBUTED EXASCALE SYSTEM

Distributed exascale computing systems are the idea of the HPC systems, that capable to perform one exaflop operations per second in dynamic and interactive nature without central managers. In such environment, each node should manage its own load itself and it should be found the basic rules of load distribution for all nodes because of being able to optimize the load distribution without central managers. In this paper proposed oscillation model for load distribution in fully distributed exascale systems and defined some parameters for this model and mentioned about feature works.

PEDESTRIAN GENDER RECOGNITION WITH HANDCRAFTED FEATURE ENSEMBLES

Human gender recognition is one the most challenging task in computer vision, especially in pedestrians, due to so much variation in human poses, video acquisition, illumination, occlusion, and human clothes, etc. In this article, we have considered gender recognition which is very important to be considered in video surveillance. To make the system automated to recognize the gender, we have provided a novel technique based on the extraction of features through different methodologies. Our technique consists of 4 steps a) preprocessing, b) feature extraction, c) feature fusion, d) classification. The exciting area is separated in the first step, which is the full body from the images. After that, images are divided into two halves on the ratio of 2:3 to acquire sets of upper body and lower body. In the second step, three handcrafted feature extractors, HOG, Gabor, and granulometry, extract the feature vectors using different score values. These feature vectors are fused to create one strong feature vector on which results are evaluated. Experiments are performed on full-body datasets to make the best configuration of features. The features are extracted through different feature extractors in different numbers to generate their feature vectors. Those features are fused to create a strong feature vector. This feature vector is then utilized for classification. For classification, SVM and KNN classifiers are used. Results are evaluated on five performance measures: Accuracy, Precision, Sensitivity, Specificity, and Area under the curve. The best results that have been acquired are on the upper body, which is 88.7% accuracy and 0.96 AUC. The results are compared with the existing methodologies, and hence it is concluded that the proposed method has significantly achieved higher results.

PROVISIONING LARGE-SCALED DATA WITH PARAMETERIZED QUERY PLANS: A CASE STUDY

In a general scenario, the approaches linked to the innovation of large-scaled data seem ordinary; the informational measures of such aspects can differ based on the applications as these are associated with different attributes that may support high data volumes high data quality. Accordingly, the challenges can be identified with an assurance of high-level protection and data transformation with enhanced operation quality. Based on large-scale data applications in different virtual servers, it is clear that the information can be measured by enlisting the sources linked to sensors networked and provisioned by the analysts. Therefore, it is very much essential to track the relevance and issues with enormous information. While aiming towards knowledge extraction, applying large-scaled data may involve the analytical aspects to predict future events. Accordingly, the soft computing approach can be implemented in such cases to carry out the analysis. During the analysis of large-scale data, it is essential to abide by the rules associated with security measures because preserving sensitive information is the biggest challenge while dealing with large-scale data. As high risk is observed in such data analysis, security measures can be enhanced by having provisioned with authentication and authorization. Indeed, the major obstacles linked to the techniques while analyzing the data are prohibited during security and scalability. The integral methods towards application on data possess a better impact on scalability. It is observed that the faster scaling factor of data on the processor embeds some processing elements to the system. Therefore, it is required to address the challenges linked to processors correlating with process visualization and scalability.

A QUANTUM ALGORITHM BASED ON ENTANGLEMENT MEASURE FOR CLASSIFYING BOOLEAN MULTIVARIATE FUNCTION INTO NOVEL HIDDEN CLASSES REVISITED

The algorithm that solves a generalized form of the Deutsch- Jozsa problem was proposed. This algorithm uses the degree of entanglement computing model to classify an arbitrary Oracle Uf to one of the 2n classes. In this paper, we will analyze this algorithm based on the degree of entanglement.

TRUST BASE JOB SCHEDULING IN CLOUD COMPUTING

Cloud computing is a new technology recently being developed seriously. Scheduling is an essential issue in the area of cloud computing. There is an extensive literature concerning scheduling in the area of distributed systems. Some of them are applicable for cloud computing. Traditional scheduling methods are unable to provide scheduling in cloud environments. According to a simple classification, scheduling algorithms in the cloud environment are divided into two main groups: batch mode and online heuristics scheduling. This paper focuses on the trust of cloud-based scheduling algorithms. According to the literature, the existing algorithm examinee latest algorithm is related to an algorithm trying to optimize scheduling using the Trust method. The existing algorithm has some drawbacks, including the additional overhead and inaccessibility to the past transaction data. This paper is an improvement of the trust-based algorithm to reduce the drawbacks of the existing algorithms. Experimental results indicate that the proposed method can execute better than the previous method. The efficiency of this method depends on the number of nods and tasks. The more trust in the number of nods and tasks, the more the performance improves when the time cost increases

IMPLEMENTATION OF NOVEL APPROACHES IN BIDIRECTIONAL MODEL TRANSFORMATION: A SYSTEMATIC LITERATURE REVIEW

In the past few years, software development has seen rapid growth, and developers have adopted different methods to provide efficient procedures in software development, thus reducing the overall bug counts and time delay. Bidirectional model transformation is one such technique that encompasses the development of the object code in both directions enabling an abstract view of the software to the developer; over the year’s researchers, have been able to produce many approaches in bidirectional model transformations (bx), but the cost and best fir for effective model transformation, in particular, a quantities survey will be designed which will discuss the best possible apron in the bx. The methodology for this survey shall be made through SLR to identify around 20 different approaches proposed for bidirectional model transformation; these studies range from the year 2010 till date and are thus, rendered latest in the respective fields of our survey. The gathered results have been calculated on the specific set of parameters that are cost and time of usage time are the main aspects of these approaches, and that is the predicament that has made us produce a systematic literature review (SLR) on this very topic. Thus, this paper investigates different approaches based on their implementation cost and time delay and discusses their limitations, and the approach is implemented. Those approaches have been selected, which culminate in both of these respective parameters. The main objective of this SLR is to provide an insight into the different approaches and establish a well-balanced approach that can be used in bidirectional model transformation in software development.