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.

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.

Online rating systems are widely used to facilitate making decisions on the web. For fame or profit, people may try to manipulate such systems by posting unfair evaluations. Therefore, determining objective rating scores of products or services becomes a very important yet difficult problem. Existing solutions are mostly majority based, also employing temporal analysis and clustering techniques. However, they are still vulnerable to sophisticated collaborative attacks. In this paper we propose an iterative rating algorithm which is very robust against collusion attacks as well as random and biased raters. Unlike previous iterative methods, our method is not based on comparing submitted evaluations to an approximation of the final rating scores, and it entirely decouples credibility assessment of the cast evaluations from the ranking itself. This makes it more robust against sophisticated collusion attacks than the previous iterative filtering algorithms. We provide a rigorous proof of convergence of our algorithm based on the existence of a fixed point of a continuous mapping which also happens to be a stationary point of a constrained optimization objective. We have implemented and tested our rating method using both simulated data as well as real world movie rating data. Our tests demonstrate that our model calculates realistic rating scores even in the presence of massive collusion attacks and outperforms well-known algorithms in the area. The results of applying our algorithm on the real-world data obtained from MovieLens conforms highly with the rating scores given by Rotten Tomatoes movie critics as domain experts for movies.

Worker selection is a significant and challenging issue in crowdsourcing systems. Such selection is usually based on an assessment of the reputation of the individual workers participating in such systems. However, assessing the credibility and adequacy of such calculated reputation is a real challe