Information security is one of the foundational requirements for any modern society thriving on digital connectivity. At present, information security is accomplished either through software algorithms or hardware protocols. Software algorithms use pseudo random numbers generated by one-way mathematical functions that are computationally robust in the classical era, but are shown to become vulnerable in the post-quantum era. Hardware security overcomes such limitations through physically unclonable functions (PUFs) that exploit manufacturing process variations in the physical microstructures of Si integrated circuits to obtain true random numbers. However, recent upsurge in reverse engineering strategies make Si-PUFs vulnerable to various attacks. Moreover, Si-PUFs are low-entropy, power-hungry, and area-inefficient. Here we introduce a biological PUF which exploits the inherent randomness found in the colonized populations of T cells and is difficult to reverse engineer and at the same time is high-entropy, non-volatile, reconfigurable, ultra-low-power, low-cost, and environment friendly.

No abstract

Introduction: The application of remote-sensing techniques for water quality assessment has become increasingly popular in China. However, existing reviews are often limited to qualitative description and are quite fragmented. Outcomes: We conducted a quantitative systematic review to display current research status and identify the existing challenges and future directions. Our review revealed that the application of remote-sensing techniques in water quality research has expanded dramatically in China, but the spatial distribution is quite uneven. Second, the ground object spectrometer is the most widely applied data source. Water color indicators such as chlorophyll a and suspended solid are the most widely investigated in China. Third, semiempirical method is the most commonly used inversion method. Existing studies rarely considered the anthropogenic factors, which limited the model robustness and its application in humandominated aquatic ecosystems. Discussion and Conclusion: We concluded that, in the past several decades, China has made notable progresses in monitoring and evaluation of water quality using the remote-sensing techniques (especially in inland lakes). We proposed that further improvements would be needed in terms of temporal and spatial coverage, indicator list, the incorporation of humannature interactions, inversion accuracy, and model generalization.

Marine aquaculture plays an important role in seafood supplement, economic development, and coastal ecosystem service provision. The precise delineation of marine aquaculture areas from high spatial resolution (HSR) imagery is vital for the sustainable development and management of coastal marine resources. However, various sizes and detailed structures of marine objects make it difficult for accurate mapping from HSR images by using conventional methods. Therefore, this study attempts to extract marine aquaculture areas by using an automatic labeling method based on the convolutional neural network (CNN), i.e., an end-to-end hierarchical cascade network (HCNet). Specifically, for marine objects of various sizes, we propose to improve the classification performance by utilizing multi-scale contextual information. Technically, based on the output of a CNN encoder, we employ atrous convolutions to capture multi-scale contextual information and aggregate them in a hierarchical cascade way. Meanwhile, for marine objects with detailed structures, we propose to refine the detailed information gradually by using a series of long-span connections with fine resolution features from the shallow layers. In addition, to decrease the semantic gaps between features in different levels, we propose to refine the feature space (i.e., channel and spatial dimensions) using an attention-based module. Experimental results show that our proposed HCNet can effectively identify and distinguish different kinds of marine aquaculture, with 98% of overall accuracy. It also achieves better classification performance compared with object-based support vector machine and state-of-the-art CNN-based methods, such as FCN-32s, U-Net, and DeeplabV2. Our developed method lays a solid foundation for the intelligent monitoring and management of coastal marine resources.

Disorder is fundamental to nature and natural phenomena, providing countless information sources, which are astronomically difficult to duplicate, but have yet to be exploited for cryptographic applications. While the contemporary crypto systems, relying on the premise of abstract mathematical one-way functions, are relatively difficult to decipher with reasonable and/or finite resources, the situation is bound to change with the advent of quantum computers, necessitating physically unclonable entropy sources. As such, inspiration is drawn from the disorder that is prevalent in nature and inherent to biological systems for designing disruptive mechanisms for cryptographic key generation. It is demonstrated that the spatiotemporal dynamics of an ensemble of living organisms such as T cells can be used for maximum entropy, high-density, and high-speed key generation. Further, such biology based one-way functions are oblivious to any mathematical representations and are computationally expensive to decipher even if an adversary has an exhaustive knowledge of the key generation mechanisms, which include cell type, cell density, key sampling rate, and sampling instance. The introduction of such biological one-way functions can greatly enhance the ability to protect information in the post quantum era.Digital information is experiencing an unprecedented and super-exponential growth across every sector of modern society including defense, automation, communication, computation, agriculture, healthcare, and infrastructure. Governments, military organizations, financial institutions, and corporations now withhold a large volume of confidential and classified information about their policies, employees, financial assets, customers, manufactured goods, and research and development data. Recent

The rapid development in network technology has resulted in the proliferation of Internet of Things (IoT). This trend has led to a widespread utilization of decentralized data and distributed computing power. While machine learning can benefit from the massive amount of IoT data, privacy concerns and communication costs have caused data silos. Although the adoption of blockchain and federated learning technologies addresses the security issues related to collusion attacks and privacy leakage in data sharing, the “free-rider attacks” and “model poisoning attacks” in the federated learning process require auditing of the training models one by one. However, that increases the communication cost of the entire training process. Hence, to address the problem of increased communication cost due to node security verification in the blockchain-based federated learning process, we propose a communication cost optimization method based on security evaluation. By studying the verification mechanism for useless or malicious nodes, we also introduce a double-layer aggregation model into the federated learning process by combining the competing voting verification methods and aggregation algorithms. The experimental comparisons verify that the proposed model effectively reduces the communication cost of the node security verification in the blockchain-based federated learning process.

Abstract. China has witnessed extensive development of the marine aquaculture industry over recent years. However, such rapid and disordered expansion posed risks to coastal environment, economic development, and biodiversity protection. This study aimed to produce an accurate national-scale marine aquaculture map at a spatial resolution of 16 m, using a proposed model based on deep convolution neural networks (CNNs) and applied it to satellite data from China's GF-1 sensor in an end-to-end way. The analyses used homogeneous CNNs to extract high-dimensional features from the input imagery and preserve information at full resolution. Then, a hierarchical cascade architecture was followed to capture multi-scale features and contextual information. This hierarchical cascade homogeneous neural network (HCHNet) was found to achieve better classification performance than current state-of-the-art models (FCN-32s, Deeplab V2, U-Net, and HCNet). The resulting marine aquaculture area map has an overall classification accuracy > 95 % (95.2 %–96.4, 95 % confidence interval). And marine aquaculture was found to cover a total area of ∼ 1100 km2 (1096.8–1110.6 km2, 95 % confidence interval) in China, of which more than 85 % is marine plant culture areas, with 87 % found in the Fujian, Shandong, Liaoning, and Jiangsu provinces. The results confirm the applicability and effectiveness of HCHNet when applied to GF-1 data, identifying notable spatial distributions of different marine aquaculture areas and supporting the sustainable management and ecological assessments of coastal resources at a national scale. The national-scale marine aquaculture map at 16 m spatial resolution is published in the Google Maps kmz file format with georeferencing information at https://doi.org/10.5281/zenodo.3881612 (Fu et al., 2020).

In recent years, Wireless Sensor Networks with a Mobile Sink (WSN-MS) have been an active research topic due to the widespread use of mobile devices. However, how to get the balance between data delivery latency and energy consumption becomes a key issue of WSN-MS. In this paper, we study the clustering approach by jointly considering the Route planning for mobile sink and Clustering Problem (RCP) for static sensor nodes. We solve the RCP problem by using the minimum travel route clustering approach, which applies the minimum travel route of the mobile sink to guide the clustering process. We formulate the RCP problem as an Integer Non-Linear Programming (INLP) problem to shorten the travel route of the mobile sink under three constraints: the communication hops constraint, the travel route constraint and the loop avoidance constraint. We then propose an Imprecise Induction Algorithm (IIA) based on the property that the solution with a small hop count is more feasible than that with a large hop count. The IIA algorithm includes three processes: initializing travel route planning with a Traveling Salesman Problem (TSP) algorithm, transforming the cluster head to a cluster member and transforming the cluster member to a cluster head. Extensive experimental results show that the IIA algorithm could automatically adjust cluster heads according to the maximum hops parameter and plan a shorter travel route for the mobile sink. Compared with the Shortest Path Tree-based Data-Gathering Algorithm (SPT-DGA), the IIA algorithm has the characteristics of shorter route length, smaller cluster head count and faster convergence rate.