An important component of an automated surveillance environment is person re-identification. The problem is often addressed using data received from vision sensors using appearance-based features, which are heavily reliant on visual cues such as colour, texture, and so on, limiting the reliability of re-identification of an individual. Much research has been performed to solve the problem of re-identification utilising human gait using inertial measurement units (IMU) data, which is thought to be unique and offer a distinct biometric signature that is especially ideal for re-ID in uncontrolled conditions. The locomotive activity of walking was the primary emphasis. The current study utilised not only locomotive activities but also non-locomotive activities of daily living. The data was obtained from the WISDM lab. The data is collected while engaging in six distinct everyday activities. The dataset was originally gathered for the purpose of Human Activity Recognition. Nonetheless, each person is given a unique ID. This information was utilised to re-identify the individual. The dataset consists of data of 36 volunteers. Shanakht-Net, a novel convolutional neural network, is introduced. The F1-score obtained is 93\%. Precision, recall, and accuracy are assessed and reported as well.

In the span of a decade, innovations in cloud computing have led to a new understanding of computing to be used as a utility. Majority of cloud service providers are making the service better and competitive for end-user. Aside from the number of services introduced by these providers, users are feeling uneasy and are unaware of consequences while switching from one service to another. Internal architecture of the cloud makes it difficult for end-users to understand. To overcome this issue a new concept of multi-cloud has been introduced. In multi-cloud technology, we can use multiple clouds from different vendors without platform complexity. Hence summarized, Multi-cloud is the usage of autonomous cloud platforms with one interface which may clue to different administrative and implementation domains. This paper reviews the literature of recently presented solutions and architectures for multi-cloud platforms.

Human Activity Recognition (HAR) an important area of research in the light of enormous applications that it provides, such as health monitoring, sports, entertainment, efficient human computer interface, child care, education and many more. Use of Computer Vision for Human Activity Recognition has many limitations. The use of inertial sensors which include accelerometer and gyroscopic sensors for HAR is becoming the norm these days considering their benefits over traditional Computer Vision techniques. In this paper we have proposed a 1-dimensional Convolutions Neural Network which is inspired by two state-of-the art architectures proposed for image classifications; namely Inception Net and Dense Net. We have evaluated its performance on two different publicly available datasets for HAR. Precision, Recall, F1-measure and accuracies are reported.

ARM processors have taken over the mobile industry from a long time now. Future of data centers and the IT industry is estimated to make use of ARM Processors. Projects like Openstack on ARM are enabling use of ARM in data centers . Single board computers (SBCs) based on ARM processors have become the norm these days. Reason for their popularity lies in their cost effective and power efficient nature. There are hundreds of them available in the market having different sizes, compute power and prices. The reason for their popularity is largely due to the rise of new technology called IoT (Internet of Things) but there is another perspective where they can become handy. Low Price and Power Usage of single board computers makes them top candidate to be used for teaching many courses with hands-on experience in developing countries like Pakistan. Many boards support full Linux distributions and can be used as general-purpose computers while many of them are open hardware based. In this paper, we have reviewed the famous options available and tried to figure out which of them are better for teaching what kind of courses

Applications like Big Data, Machine Learning, Deep Learning and even other Engineering and Scientific research requires a lot of computing power; making High-Performance Computing (HPC) an important field. But access to Supercomputers is out of range from the majority. Nowadays Supercomputers are actually clusters of computers usually made-up of commodity hardware. Such clusters are called Beowulf Clusters. The history of which goes back to 1994 when NASA built a Supercomputer by creating a cluster of commodity hardware. In recent times a lot of effort has been done in making HPC Clusters of even single board computers (SBCs). Although the creation of clusters of commodity hardware is possible but is a cumbersome task. Moreover, the maintenance of such systems is also difficult and requires special expertise and time. The concept of cloud is to provide on-demand resources that can be services, platform or even infrastructure and this is done by sharing a big resource pool. Cloud computing has resolved problems like maintenance of hardware and requirement of having expertise in networking etc. An effort is made of bringing concepts from cloud computing to HPC in order to get benefits of cloud. The main target is to create a system which can develop a capability of providing computing power as a service which to further be referred to as Supercomputer as a service. A prototype was made using Raspberry Pi (RPi) 3B and 3B+ Single Board Computers. The reason for using RPi boards was increasing popularity of ARM processors in the field of HPC