Transfer learning has achieved promising results by leveraging knowledge from the source domain to annotate the target domain which has few or none labels. Existing methods often seek to minimize the distribution divergence between domains, such as the marginal distribution, the conditional distribution or both. However, these two distances are often treated equally in existing algorithms, which will result in poor performance in real applications. Moreover, existing methods usually assume that the dataset is balanced, which also limits their performances on imbalanced tasks that are quite common in real problems. To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution Adaptation (BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA. Based on BDA, we also propose a novel Weighted Balanced Distribution Adaptation (W-BDA) algorithm to tackle the class imbalance issue in transfer learning. W-BDA not only considers the distribution adaptation between domains but also adaptively changes the weight of each class. To evaluate the proposed methods, we conduct extensive experiments on several transfer learning tasks, which demonstrate the effectiveness of our proposed algorithms over several state-of-the-art methods.

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Food safety is becoming more and more serious topic worldwide. To tackle the food safety issues from the technical aspect, people need a trusted food traceability system that can track and monitor the whole lifespan of food production, including the processes of food raw material cultivation/breeding, processing, transporting, warehousing, and selling etc. In this paper, we propose a trusted, self-organized, open and ecological food traceability system based on blockchain and Internet of Things (IoT) technologies, which involves all parties of a smart agriculture ecosystem, even if they may not trust each other. We use IoT devices to replace manual recording and verification as many as possible, which can reduce the human intervention to the system effectively. Furthermore, we plan to use the smart contract technology to help the law-executor to find problems and process them timely.

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Cognitive radio (CR) is a promising concept for improving the utilization of scarce radio spectrum resources. A reliable strategy for the detection of unused spectrum bands is essential to the design and practical implementation of CR systems. It is widely accepted that in a real-world environment, cooperative spectrum sensing involving many secondary users scattered in a wide geographical area can greatly improve sensing accuracy. However, some secondary users may misbehave, i.e. provide false sensing information, in an attempt to maximize their own utility gains. Such selfish behaviour, if unchecked, can severely impact the operation of the CR system. In this paper, we propose a novel trustaware hybrid spectrum sensing scheme which can detect misbehaving secondary users and filter out their reported spectrum sensing results from the decision making process. The robustness and efficiency of the proposed scheme are verified through extensive computer simulations.