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The evolution of Service-Oriented Computing (SOC) provides more efficient software development methods for building and engineering new value-added service-based applications. SOC is a computing paradigm that relies on Web services as fundamental elements. Research and technical advancements in Web services composition have been considered as an effective opportunity to develop new service-based applications satisfying complex requirements rapidly and efficiently. In this paper, we present a novel approach enhancing the composition of semantic Web services. The novelty of our approach, as compared to others reported in the literature, rests on: i) mapping user's/organization's requirements with Business Process Modeling Notation (BPMN) and semantic descriptions using ontologies, ii) considering functional requirements and also different types of non-functional requirements, such as quality of service (QoS), quality of experience (QoE), and quality of business (QoBiz), iii) using Formal Concept Analysis (FCA) technique to select the optimal set of Web services, iv) considering composability levels between sequential Web services using Relational Concept Analysis (RCA) technique to decrease the required adaptation efforts, and finally, v) validating the obtained service-based applications by performing an analytical technique, which is the monitoring. The approach experimented on an extended version of the OWLS-TC dataset, which includes more than 10830 Web services descriptions from various domains. The obtained results demonstrate that our approach allows to successfully and effectively compose Web services satisfying different types of user's functional and non-functional requirements.

The Kingdom of Saudi Arabia has suffered from COVID-19 disease as part of the global pandemic due to severe acute respiratory syndrome coronavirus 2. The economy of Saudi Arabia also suffered a heavy impact. Several measures were taken to help mitigate its impact and stimulate the economy. In this context, we present a safe and secure WiFi-sensing-based COVID-19 monitoring system exploiting commercially available low-cost wireless devices that can be deployed in different indoor settings within Saudi Arabia. We extracted different activities of daily living and respiratory rates from ubiquitous WiFi signals in terms of channel state information (CSI) and secured them from unauthorized access through permutation and diffusion with multiple substitution boxes using chaos theory. The experiments were performed on healthy participants. We used the variances of the amplitude information of the CSI data and evaluated their security using several security parameters such as the correlation coefficient, mean-squared error (MSE), peak-signal-to-noise ratio (PSNR), entropy, number of pixel change rate (NPCR), and unified average change intensity (UACI). These security metrics, for example, lower correlation and higher entropy, indicate stronger security of the proposed encryption method. Moreover, the NPCR and UACI values were higher than 99% and 30, respectively, which also confirmed the security strength of the encrypted information.

The Internet of Things (IoT) is a vital component of many future industries. By intelligent integration of sensors, wireless communications, computing techniques, and data analytics, IoT can increase productivity and efficiency of industries. Reliability of data transmission is key to realize several applications offered by IoT. In this paper, we present an overview of future IoT applications, and their major communication requirements. We provide a brief survey of recent work in four major areas of reliable IoT including resource allocation, latency management, security, and reliability metrics. Finally, we highlight some of the important challenges for reliable IoT related to machine learning techniques, 6G communications and blockchain based security that need further investigation and discuss related future directions.

Time-series remote sensing data offer a rich source of information that can be used in a wide range of applications, from monitoring changes in land cover to surveillance of crops, coastal changes, flood risk assessment, and urban sprawl. In this paper, time-series satellite images are used to predict urban expansion. As the ground truth is not available in time-series satellite images, an unsupervised image segmentation method based on deep learning is used to generate the ground truth for training and validation. The automated annotated images are then manually validated using Google Maps to generate the ground truth. The remaining data were then manually annotated. Prediction of urban expansion is achieved by using a ConvLSTM network, which can learn the global spatio-temporal information without shrinking the size of spatial feature maps. The ConvLSTM based model is applied on the time-series satellite images and the results of prediction are compared with Pix2pix and Dual GAN networks. In this paper, experimental results are conducted using several multi-date satellite images representing the three largest cities in Saudi Arabia, namely: Riyadh, Jeddah, and Dammam. The evaluation results show that the proposed ConvLSTM based model produced better prediction results in terms of Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy as compared to Pix2pix and Dual GAN. Moreover, the training time of the proposed architecture is less than the Dual GAN architecture.

Religious tourism is getting popularity and more and more people wants to visit the religious places. Al-Masjid Al-Nabawi holds a historical priority for 1.5 billion visitors with different languages, cultures, and ethnicity. As part of the Saudi leadership to govern the affairs of Al-Masjid Al-Nabawi, many expansions have been planned\performed to provide better services for Saudi Arabia’s guests and visitors around the years. Large and ongoing expansions of the holy sites in Saudi Arabia may impact the ease of mobility for own guests especially in the time of high seasons (i.e., Haij and Omrah times). Virtual Reality technology started to play a critical role in the tourism industry by virtually exposing users to a certain place. The primary purpose of this paper is to visualize the Al-Masjid Al-Nabawi site in an interactive style and ease of use for the guests. The interaction with the 3D environment is ensured via the Leap motion Controller in a very simple way without need to special learning. We designed and implemented a virtual reality-based touring guide prototype, and an initial user validation was conducted. The results are promising for future investigation.

Globally, COVID-19 already emerged in around 170 million confirmed cases of infected people and, as of May 31, 2021, affected more than 3.54 million deaths. This pandemic has given rise to numerous public health and socioeconomic issues, emphasizing the significance of unraveling the epidemic’s history and forecasting the disease’s potential dynamics. A variety of mathematical models have been proposed to obtain a deeper understanding of disease transmission mechanisms. Machine Learning (ML) models have been used in the last decade to identify patterns and enhance prediction efficiency in healthcare applications. This paper proposes a model to predict COVID-19 patients admission to the intensive care unit (ICU). The model is built upon robust known classification algorithms, including classic Machine Learning Classifiers (MLCs), an Artificial Neural Network (ANN) and ensemble learning. This model’s strength in predicting COVID-19 infected patients is shown by performance analysis of various MLCs and error metrics. Among other used ML models, the ANN model resulted in the highest accuracy, 97.9% over other models. Mean Squared Error showed that the ANN method had the lowest error (0.0809). In conclusion, this paper could be beneficial to ICU staff to predict ICU admission based on COVID-19 patients’ clinical characteristics. © 2021. All Rights Reserved.

Software developers agree that software portability is a desirable attribute for their software quality. Software portability is mostly acquired by ad-hoc techniques when trying to port existing products. There is a lack of unified measuring approach of software portability in most computing platforms. This paper presents preliminary results of a systematic literature review, conducted to collect evidence on measuring software portability. The evidence was gathered from selected studies and based on a set of meaningful and focused questions. 49 studies of these were selected for data extraction performed against the research questions. We provide an overview of used\proposed measurement metrics of software portability. Our results suggested that there are scattered efforts to understand measurement of software portability, and no census has been achieved.

Training and planning for neurosurgeries necessitate many requirements from junior neurosurgeons, including perceptual capacities. An effective method of deliberate training is to replicate the required procedures using neurosurgical simulation tools and visualizing a three-dimensional (3D) workspace. However, Augmented Reality (AR) neurosurgical simulators become obsolete for a variety of reasons, including users' distance underestimation. Few investigations have been conducted for improving users' depth perception in AR systems with perceptual motion cues through neurosurgical simulation tools for planning aid purposes. In this poster, we are reporting a user study about whether head motion and perceptual motion cues have any an influence on users' depth perception.

The rapid growth of the smartphone market and its increasing revenue has motivated developers to target multiple platforms. Market leaders, such as Apple, Google, and Microsoft, develop their smartphone applications complying with their platform specifications. The specification of each platform makes a platform-dedicated application incompatible with other platforms due to the diversity of operating systems, programming languages, and design patterns. Conventional development methodologies are applied to smartphone applications, yet they perform less well. Smartphone applications have unique hardware and software requirements. All previous factors push smartphone developers to build less sophisticated and low-quality products when targeting multiple smartphone platforms. Model-driven development have been considered to generate smartphone applications from abstract models to alleviate smartphones platform fragmentation. Reusing these abstract models for other platforms was not considered because they do not fit new platforms requirements. It is possible that defining smartphone applications using a portability-driven modeling notation would facilitate smartphone developers to understand better their applications to be ported to other platforms. We call for a portability-driven modeling notation to be used within a smartphone development process. Our in-process research work will be manifested through the application of a domain-specific language complying with the three software portability principles and three design factors. This paper aims to highlight our research work, methodology and current statue.