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This paper describes a large resource of multi-center and multi-topic heart sound databases, which were based on the measured data from more than 9,000 heart sound samples (saved in WAV file format). According to different research topics, these samples were respectively stored in different folders (corresponding to different research topics and distributed over various cooperative research centers), most of which as subfolds were stored in a pooled folder in the principal center. According to different research topics, the measured data from these samples were used to create different databases. Relevant data for a specific topic can be pooled in a large database for further analysis. This resource is shared by members of related centers for their own specific topic. The applications of this resource include evaluation of cardiac safety of pregnant women, evaluation of cardiac reserve for children, athletes, addicts, astronauts, and general populations, as well as studies on a bedside method for evaluating cardiac energy, reversal of S1-S2 ratio, etc.

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The authentication protocol is vital for the security of the wireless sensor network to resist the known threats, such as eavesdropping, replay attack, man-in-the-middle attack, etc. In this paper, a lightweight authentication protocol for vehicular ad hoc networks is proposed using the symmetric encryption, the group communication method, and the proactive authentication technique, which not only achieves the desired security goals but also guarantees the practical anonymity and the accountability. The analysis demonstrates that the proposed protocol works properly in the high-density and the low-density traffic environment.

Together with impressive advances touching every aspect of our society, AI technology based on Deep Neural Networks (DNN) is bringing increasing security concerns. While attacks operating at test time have monopolised the initial attention of researchers, backdoor attacks, exploiting the possibility of corrupting DNN models by interfering with the training process, represents a further serious threat undermining the dependability of AI techniques. In a backdoor attack, the attacker corrupts the training data so to induce an erroneous behaviour at test time. Test time errors, however, are activated only in the presence of a triggering event corresponding to a properly crafted input sample. In this way, the corrupted network continues to work as expected for regular inputs, and the malicious behaviour occurs only when the attacker decides to activate the backdoor hidden within the network. In the last few years, backdoor attacks have been the subject of an intense research activity focusing on both the development of new classes of attacks, and the proposal of possible countermeasures. The goal of this overview paper is to review the works published until now, classifying the different types of attacks and defences proposed so far. The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time. As such, the proposed analysis is particularly suited to highlight the strengths and weaknesses of both attacks and defences with reference to the application scenarios they are operating in.