The state-of-art Blockchain technology has been receiving enormous attention from researchers and industry practitioners in recent years. Basically Blockchain is a distributed immutable ledgerutilized by user mainly to perform transactions across various nodes in a blockchain network after a mutual consensus of all the associated nodes in the network. Noticeably, Blockchain is made famous by the Bitcoin cryptocurrency. As of 2017 Blockchain has seen to disrupt many applications to a global worth of 150B. The consensus models are responsible for the process of reaching an agreement between all nodes in blockchain to accept a new block. The consensus model plays a vital role in sustaining Blockchain efficiency. Using the right consensus model can significantly increase the performance of blockchain operation. The consensus models can be divided into two categories. The first category is the proof-based consensus model whereby a node in the Blockchain performs enough proof over other nodes to gain a chance to append the next block to the chain. The second category is the voting-based model in which nodes need to share their results in verifying a transaction of a new block before a final decision is reached. In this paper we discuss the characteristics of some major existing consensus models and provide some critical reviews on their performances. The reviewis mainly concerning these aspects: transaction throughput, latency, network bandwidth, and storage.

Human Activity recognition (HAR) is an important area of research in ubiquitous computing and Human Computer Interaction. To recognize activities using mobile or wearable sensor, data are collected using appropriate sensors, segmented, needed features extracted and activities categories using discriminative models (SVM, HMM, MLP etc.). Feature extraction is an important stage as it helps to reduce computation time and ensure enhanced recognition accuracy. Earlier researches have used statistical features which require domain expert and handcrafted features. However, the advent of deep learning that extracts salient features from raw sensor data and has provided high performance in computer vision, speech and image recognition. Based on the recent advances recorded in deep learning for human activity recognition, we briefly reviewed the different deep learning methods for human activities implemented recently and then propose a conceptual deep learning frameworks that can be used to extract global features that model the temporal dependencies using Gated Recurrent Units. The framework when implemented would comprise of seven convolutional layer, two Gated recurrent unit and Support Vector Machine (SVM) layer for classification of activity details. The proposed technique is still under development and will be evaluated with benchmarked datasets and compared with other baseline deep learning algorithms.

Cloud computing manages system resources such as processing, storage, and networking by providing users with multiple virtual machines (VMs) as needed. It is one of the rapidly growing fields that come with huge computational power for scientific workloads. Currently, the scientific community is ready to work over the cloud as it is considered as a resource-rich paradigm. The traditional way of executing scientific workloads on cloud computing is by using virtual machines. However, the latest emerging concept of containerization is growing more rapidly and gained popularity because of its unique features. Containers are treated as lightweight as compared to virtual machines in cloud computing. In this regard, a few VMs/containers-associated problems of performance and throughput are encountered because of middleware technologies such as virtualization or containerization. In this paper, we introduce the configurations of VMs and containers for cloud-based scientific workloads in order to utilize the technologies to solve scientific problems and handle their workloads. This paper also tackles throughput and efficiency problems related to VMs and containers in the cloud environment and explores efficient resource provisioning by combining four unique methods: hyperthreading (HT), vCPU cores selection, vCPU affinity, and isolation of vCPUs. The HEPSCPEC06 benchmark suite is used to evaluate the throughput and efficiency of VMs and containers. The proposed solution is to implement four basic techniques to reduce the effect of virtualization and containerization. Additionally, these techniques are used to make virtual machines and containers more effective and powerful for scientific workloads. The results show that allowing hyperthreading, isolation of CPU cores, proper numbering, and allocation of vCPU cores can improve the throughput and performance of virtual machines and containers.