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With the proliferation of smart devices capable of communicating over a network using different protocols, each year more and more successful attacks are recorded against these, underlining the necessity of developing and implementing mechanisms to protect against such attacks. This paper will review some existing solutions used to secure a communication channel, such as Transport Layer Security or symmetric encryption, as well as provide a novel approach to achieving confidentiality and integrity of messages. The method, called Value-to-Keyed-Hash Message Authentication Code (Value-to-HMAC) mapping, uses signatures to send messages, instead of encryption, by implementing a Keyed-Hash Message Authentication Code generation algorithm. Although robust solutions exist that can be used to secure the communication between devices, this paper considers that not every Internet of Things (IoT) device or network design is able to afford the overhead and drop in performance, or even support such protocols. Therefore, the Value-to-HMAC method was designed to maximize performance while ensuring the messages are only readable by the intended node. The experimental procedure demonstrates how the method will achieve better performance than a symmetric-key encryption algorithm, while ensuring the confidentiality and integrity of information through the use of one mechanism.

Fractal encoding method becomes an effective image compression method because of its high compression ratio and short decompressing time. But one problem of known fractal compression method is its high computational complexity and consequent long compressing time. To address this issue, in this paper, distance clustering in high dimensional sphere surface is applied to speed up the fractal compression method. Firstly, as a preprocessing strategy, an image is divided into blocks, which are mapped on high dimensional sphere surface. Secondly, a novel image matching method is presented based on distance clustering on high dimensional sphere surface. Then, § Corresponding author. This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. 1740004-1

Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

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