We present a one-step layer deposition procedure employing ammonium iodide (NH4I) to achieve photovoltaic quality PbS quantum dot (QD) layers. Ammonium iodide is used to replace the long alkyl organic native ligands binding to the QD surface resulting in iodide terminated QDs that are stabilized in polar solvents such as N,N-dimethylformamide without particle aggregation. We extensively characterized the iodide terminated PbS QD via UV-vis absorption, transmission electron microscopy (TEM), thermogravimetric analysis (TGA), FT-IR transmission spectroscopy, and X-ray photoelectron spectroscopy (XPS). Finally, we fabricated PbS QD photovoltaic cells that employ the iodide terminated PbS QDs. The resulting QD-PV devices achieved a best power conversion efficiency of 2.36% under ambient conditions that is limited by the layer thickness. The PV characteristics compare favorably to similar devices that were prepared using the standard layer-by-layer ethandithiol (EDT) treatment that had a similar layer thickness.

With the development of machine learning, expectations for artificial intelligence (AI) technology are increasing day by day. In particular, deep learning has shown enriched performance results in a variety of fields. There are many applications that are closely related to our daily life, such as making significant decisions in application area based on predictions or classifications, in which a deep learning (DL) model could be relevant. Hence, if a DL model causes mispredictions or misclassifications due to malicious external influences, it can cause very large difficulties in real life. Moreover, training deep learning models involves relying on an enormous amount of data and the training data often includes sensitive information. Therefore, deep learning models should not expose the privacy of such data. In this paper, we reviewed the threats and developed defense methods on the security of the models and the data privacy under the notion of SPAI: Secure and Private AI. We also discuss current challenges and open issues.

Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user. Towards this goal, energy-based sequence generative adversarial nets (EB-SeqGANs) are adopted for recommendation by learning a generative model for the time series of user-preferred items. By recasting the energy function as the feature function, the proposed EB-SeqGANs is interpreted as an instance of maximum-entropy imitation learning.

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