With the popularisation of convolutional neural networks (CNNs), many tools were developed to facilitate the development of models. One of the main tools developed is the Keras API, which is a high-level API for creating and training models. The Keras API acts as a high-level layer to facilitate the implementation of neural networks while other CNN libraries can run as backend. There is a demand for studies that compare the performance of a CNN implemented with the Keras API and the performance of CNNs implemented only with native methods to CNN libraries. To evaluate the impact of the Keras API on the performance of TensorFlow 1, TensorFlow 2 and CNTK libraries in relation to the execution time in GPUs, an experiment was conducted for measuring the execution time of the LeNet-5 model. It was implemented with the aid of the Keras API and implemented without it. Models implemented with Keras API methods showed longer execution times than models implemented only with native methods from CNTK libraries. The performance difference can be significant for some applications. The Keras API sacrifices library performance up to four times when compared to native methods.
A paradigm shift in machine learning (ML) application models has occurred in the preceding years due to privacy and deep learning aspirations. The recently created decentralised paradigm of ML is known as federated learning. Federated learning (FL) is a ML technique in which many dispersed nodes use their locally stored data to train a common prediction model. Better data privacy is possible because the training data is not routed to a central server. FL offloads the processed information to the server and does not need clients to share their personal information. However, FL is a new field that has yet to achieve mainstream acceptance and is still in the development phase. In this context, the purpose of our study is to provide a more comprehensive overview of the most important protocols, platforms, and real-world FL use cases, which will allow researchers to develop privacy-preserving solutions for businesses that require FL.
Chaos-based cryptosystems and their behaviour in cryptography attract many scientists' and researchers' attention in physics and computer science in recent decades. A new dynamic cryptosystem for video sequence based on the combination of chaotic Lorenz map and dual hash functions is proposed in this paper to improve the security level of the video applications. First, split up the video sequence into video frames and audio samples. Then, the initial conditions for both video and audio are generated using SHA-256 and MD5, respectively. Next, a higher dimensional Lorenz chaotic system is adopted to confuse and diffuse the audio samples and video frames components. Moreover, to improve the security level of the proposed cryptosystem against different types of cryptanalytic attacks, the concept of multi-key is employed. The security analysis is conducted and the results indicate that the presented cryptosystem satisfies the security requirements against various attacks and can be directly applied in the real life video applications.
Blockchain provides more security and decentralised systems. However, it is difficult for startups and small companies to build and deploy their blockchain networks. So, cloud computing providers make it easy for those to work with blockchain using their networks and cloud infrastructure through using blockchain as a service platform. There are many blockchain as a service providers and platforms. This survey provides an overview of 23 blockchain-as-a-service platforms and provides a comparison based on provider, blockchain protocol, blockchain type, cost, security, and support of smart contract.
Wireless sensor networks (WSN) detect and monitor various physical attributes from the environment, convert the observation into information and send it wirelessly to base station/sink. In most of applications of WSNs, localisation, that is, finding location of nodes sending information, is also required to be done for better inferencing. In this paper, we present a review of existing literature related to WSN node localisation problem. Firstly, an introduction on wireless sensor networks and its challenges are mentioned. Then, issues of localisation algorithms are discussed. The paper also discusses different localisation algorithms and their categorisation based on precision/hardware requirement, localisation algorithms can be categorised into two groups such as range-based and range-free algorithms. Various range-based and range-free algorithms with their merits and demerits are summarised and a list of research gaps is discussed.
