MicroRNAs are small regulatory RNAs that decrease gene expression after transcription in various biological disciplines. In bioinformatics, identifying microRNAs and predicting their functionalities is critical. Finding motifs is one of the most well-known and important methods for identifying the functionalities of microRNAs. Several motif discovery techniques have been proposed, some of which rely on artificial intelligence-based techniques. However, in the case of few or no training data, their accuracy is low. In this research, we propose a new computational approach, called DiMo, for identifying motifs in microRNAs and generally macromolecules of small length. We employ word embedding techniques and deep learning models to improve the accuracy of motif discovery results. Also, we rely on transfer learning models to pre-train a model and use it in cases of a lack of (enough) training data. We compare our approach with five state-of-the-art works using three real-world datasets. DiMo outperforms the selected related works in terms of precision, recall, accuracy and f1-score.

Contrary to the public cloud storage services that impose users to accept the security restrictions delivered by the service provider, users in the private cloud benefit from self-managed, authenticated data access services. However, this may lead to security issues. A critical challenge is the provision of secure and authenticated data storage for the data owner. Moreover, the data owner should be able to access the stored data and share it with others in a controlled manner. In this article, a secure and authenticated data storage, access, and sharing model is proposed for private cloud storage, which has three components. The data storage component provides the user with secure storage of information. The data-sharing component enables sharing the stored data under the control of the data owner. The data access component enables authenticated access to the cloud storage. The security analysis demonstrates that the model is secure against various attacks. The scheme is validated to be secure via the Scyther tool, BAN Logic, and in Random Oracle Model. The performance analysis regarding the computation and communication cost via simulation in OMNeT++ show that it obtains the required security goals and efficiency of computation and communication, compared to the related methods.

Computing robust and accurate quality scores for users and items in online review systems is critical, since scores directly reflect the community-wide belief about their quality. A broad range of methods have been proposed to compute rating scores, including simple aggregation, weighted aggregation, and iterative techniques, where the latter provides relatively accurate results. However, there are still serious challenges to address, especially in terms of time complexity, accuracy, and robustness against manipulation. In this article, we propose an adaptive quality assessment framework that computes dependable and accurate quality scores for users and items. The proposed method is a semi-iterative weighted aggregation technique in which, a novel approach is used to assign weights to received reviews. The weight depends on two parameters: similarity of reviews, and review prediction. In review prediction, we utilize a combination of online machine learning and collaborative filtering to predict the review expected from the user. The intuition behind using online learning is its ability to obtain lower time complexity in comparison with batch learning. We evaluate our proposed model using a real-word dataset, and compare it with two related approaches. Results show the superiority of our proposed approach, in terms of accuracy and robustness against manipulation.

Social rating systems are widely used for gathering user feedbacks on the quality of products, items, and services. Social rating systems accept various forms of numeric and non-numeric recommendations as input to their aggregation algorithm. Fuzzy recommendations, as one form of input recommendations, while common in areas such as stock market and educational systems, are challenging in terms of aggregation and scaling. Also, taking into account trust and experience of raters while aggregating fuzzy variables is another challenge that needs investigations. In this article, we propose a trust-based experience-aware method for aggregation of fuzzy recommendations. We propose to use trust and experience of raters along with the area under the curve of the membership of the fuzzy recommendations to compute a weight for recommendations. Then, we present an iterative algorithm to aggregate these computed weighted recommendations. We evaluate our method using a real-world dataset and compare its performance with three well-known iterative algorithms. The comparison results show the superiority of our method over other related approaches.

DNS tunneling is a method used by malicious users who intend to bypass the firewall to send or receive commands and data. This has a significant impact on revealing or releasing classified information. Several researchers have examined the use of machine learning in terms of detecting DNS tunneling. However, these studies have treated the problem of DNS tunneling as a binary classification where the class label is either legitimate or tunnel. In fact, there are different types of DNS tunneling such as FTP-DNS tunneling, HTTP-DNS tunneling, HTTPS-DNS tunneling, and POP3-DNS tunneling. Therefore, there is a vital demand to not only detect the DNS tunneling but rather classify such tunnel. This study aims to propose a multilabel support vector machine in order to detect and classify the DNS tunneling. The proposed method has been evaluated using a benchmark dataset that contains numerous DNS queries and is compared with a multilabel Bayesian classifier based on the number of corrected classified DNS tunneling instances. Experimental results demonstrate the efficacy of the proposed SVM classification method by obtaining an f -measure of 0.80.

The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.