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Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

A socially responsible investment portfolio takes into consideration the environmental, social and governance aspects of companies. It has become an emerging topic for both financial investors and researchers recently. Traditional investment and portfolio theories, which are used for the optimization of financial investment portfolios, are inadequate for decision-making and the construction of an optimized socially responsible investment portfolio. In response to this problem, we introduced a Deep Responsible Investment Portfolio model that contains a Multivariate Bidirectional Long Short Term Memory neural network, to predict stock returns for the construction of a socially responsible investment portfolio. The deep reinforcement learning technique was adapted to retrain neural networks and rebalance the portfolio periodically. Our empirical data revealed that the DRIP framework could achieve competitive financial performance and better social impact compared to traditional portfolio models, sustainable indexes and funds.

Android application uses permission system to regulate the access to system resources and users' privacy-relevant information. Existing work have demonstrated several techniques to study the required permissions declared by the developers, but few attention has been paid for used permissions. Besides, no specific permission combination is identified to be effective for malware detection. To fill these gaps, we have proposed a novel pattern mining algorithm to identify a set of contrast permission patterns that aim to detect the difference between clean and malicious applications. In addition, we used a benchmark malware dataset and collected a set of 1227 clean applications to evaluate the performance of the proposed algorithm. Valuable findings are obtained by analyzing the returned contrast permission patterns.

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Splat morphology is an important factor that influences the mechanical properties and durability of thermal barrier coatings (TBCs). In this study, yttria-stabilized zirconia (YSZ) coatings with different lamellar interface morphologies were deposited by atmospheric plasma spraying (APS) using feedstocks with different particle sizes. The influence of lamellar interface roughness on the cracking resistance of the coatings was investigated. Furthermore, the thermal shock and erosion resistance of coatings deposited by two different powders was evaluated. It was found that the particle size of the feedstock powder affects the stacking morphology of the splat that forms the coating. Coatings fabricated from coarse YSZ powders (45-60 µm) show a relatively rough inter-lamellar surface, with a roughness about 3 times greater than those faricated from fine powders (15-25 µm). Coatings prepared with fine powders tend to form large cracks parallel to the substrate direction under indentation, while no cracking phenomena were found in coatings prepared with coarse powders. Due to the higher cracking resistance, coatings prepared with coarse powders show better thermal shock and erosion resistances than those with fine powders. The results of this study provide a reference for the design and optimization of the microstructure of TBCs.