Recommendation models have been widely embedded in various online services, while most of which are designed with the assumption that users' original behaviors are available in a central server. This may cause the privacy issue. As a response, we follow a recent work called federated collaborative filtering (FCF) for item recommendation with implicit feedback, and propose a novel and generic federated recommendation (FedRec) framework for rating prediction with explicit feedback. Specifically, we federate some basic and advanced factorization-based recommendation models both in batch style and in stochastic style. More importantly, in order to protect the private information of which items each user has rated, as well as not to significantly increase the computational and communication cost, we design two simple but effective strategies, i.e., user averaging and hybrid filling, in which some (instead of all) unrated items are randomly sampled and assigned with some virtual ratings accordingly. Empirical studies on two public datasets show the effectiveness of our FedRec in terms of the closeness of a federated model and an unfederated one, and the usefulness of the two filling strategies.

With the exponentially growth in the number of Internet of Things (IoT) devices, the cloud-centric computing paradigm can hardly meet the increasingly high requirements for low-latency, high-bandwidth, easily available and more intelligent services. Therefore, a distributed and decentralized computing architecture is imperative, where edge-centric computing, such as fog computing and mist computing, is recently proposed. Edge-centric computing resources can be managed locally and personally rather than being administered by a remote centralized third-party. However, security and privacy issues are the main challenges due to the absence of trust between the IoT devices and edge computing nodes. A blockchain, as a decentralized, trustless and immutable public ledger, can well solve the trust-absence issue. In this paper, we first elaborate on the security and privacy issues of edge computing-enabled IoT, and then present the key characteristics of blockchains, which make blockchains well-suited for the edge-centric IoT scenarios. Furthermore, we propose a general framework for blockchain-based edge computing-enable IoT scenarios that specifies the step-by-step procedure of a single transaction between an IoT end and an edge computing node. In addition, we design a smart contract within a private blockchain network that exploits the-state-of-the-art machine learning algorithm, Asynchronous Advantage Actor-Critic (A3C), to allocate the edge computing resources, which exemplifies how artificial intelligence (AI) can be combined with blockchains. We further discuss the benefits of the convergence of AI and blockchains. Finally, simulation results are presented.

The human-associated microbiota is diverse and complex. It takes an essential role in human health and behavior and is closely related to the occurrence and development of disease. Although the diversity and distribution of microbial communities have been widely studied, little is known about the function and dynamics of microbes in the human body or the complex mechanisms of interaction between them and drugs, which are important for drug discovery and design. A high-quality comprehensive microbe and drug association database will be extremely beneficial to explore the relationship between them. In this article, we developed the Microbe-Drug Association Database (MDAD), a collection of clinically or experimentally supported associations between microbes and drugs, collecting 5,055 entries that include 1,388 drugs and 180 microbes from multiple drug databases and related publications. Moreover, we provided detailed annotations for each record, including the molecular form of drugs or hyperlinks from DrugBank, microbe target information from Uniprot and the original reference links. We hope MDAD will be a useful resource for deeper understanding of microbe and drug interactions and will also be beneficial to drug design, disease therapy and human health.

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT make it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Non-cryptographic device verification is needed to ensure trustworthy IoT. In this paper, we propose an enhanced deep learning framework for IoT device identification using physical layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services.

Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a competitive swarm optimizer is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on eighteen high-dimensional datasets.