Abstract The popularity of e-commerce has given rise to fake advertisements that can expose users to financial and data risks while damaging the reputation of these e-commerce platforms. For these reasons, detecting and removing such fake advertisements are important for the success of e-commerce websites. In this paper, we propose FADAML, a novel end-to-end machine learning system to detect and filter out fake online advertisements. Our system combines techniques in multimodal machine learning and automated machine learning to achieve a high detection rate. As a case study, we apply FADAML to detect fake advertisements on popular Vietnamese real estate websites. Our experiments show that we can achieve 91.5% detection accuracy, which significantly outperforms three different state-of-the-art fake news detection systems.

Unmanned aerial vehicles (UAVs) have brought many practical benefits during the last decades. Moreover, as technology advances, UAVs become more optimal in size and range. However, the threat posed by these devices is also increasing if people misuse them for illegal activities (such as terrorism, drug trafficking, etc.), which poses a high risk to security for different organizations and governments. Hence, detection and monitoring of drones are crucial to prevent security breaches. However, the small size and similarity to wild birds in the complex background of drones pose a significant challenge. This paper addresses the detection of small drones in real surveillance videos using standard deep learning-based object recognition methods. Our method approaches the drone detection problem by training the YOLOv4 model with modifications in the network structure, training strategy, and pre-anchor boxes for better small object detection. We also integrate the Seq-NMS post-processing phase to increase detection reliability and reduce false alarms. The experimental results show that our approach can perform better than the previous methods.

Accurate insect pest recognition is significant to protect the crop or take the early treatment on the infected yield, and it helps reduce the loss for the agriculture economy. Designing an automatic pest recognition system is necessary as manual recognition is slow, time-consuming, and expensive. The Image-based pest classifier using the traditional computer vision method is not efficient due to the complexity. Insect pest classification is difficult because of various kinds, scales, shapes, complex backgrounds in the field, and high appearance similarity among insect species. With the rapid development of deep learning technology, the CNN-based method is the best way to develop a fast and accurate insect pest classifier. We present different convolutional neural network-based models for solving challenges in the insect pest recognition problem, including attention, feature pyramid, and fine-grained models. We evaluate our methods on two public datasets: the large-scale insect pest dataset, the IP102 benchmark dataset, and a smaller dataset, namely D0 in terms of the macro-average precision (MPre), the macro-average recall (MRec), the macro-average F1- score (MF1), the accuracy (Acc), and the geometric mean (GM). The experimental results show that combining these convolutional neural network-based models can better perform than the state-of-the-art methods on these two datasets. For instance, the highest accuracy we obtained on IP102 and D0 is 72.91% and 99.89%, respectively, bypassing the corresponding state-of-the-art accuracy: 67.1% (IP102) and 98.8% (D0). We also publish our codes for contributing to the current research related to the insect pest classification problem.

Recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks have shown some success with many practical applications in recent years such as machine translation, speech recognition, image processing and financial market forecasting. In recent years, a dual-stage attention-based recurrent neural network (DA-RNN) has shown some promising results on stock price prediction. We propose dual attention-dilated long short-term memory (DAD-LSTM) models combining DA-RNN and dilated recurrent neural networks (DRNN) to select the most relevant input features and capture the long-term temporal dependencies of a time series more efficiently. Numerical results from experiments on the NASDAQ 100, S&P 500, HSI and DJIA datasets show that DAD-LSTM models outperform the state-of-the-art and most recent approaches.

Nowadays, industrialization and urbanization have led to increasing stress levels in many countries. Primarily, it starts having an impact on vulnerable populations, e.g., college students and physicians. While previous works have reported an alarmingly high rate of stress in students, little is known about whether there are stress-related brain activity changes. This study aims to examine the effects of a realistic stressor (examination) on students’ stress levels and brain activities. We selected a cohort of 16 college students to participate in this study. Each subject was invited to the laboratory twice (pre-exam and in-exam) for completing a package of questionnaires. At the same time, we measure their brain physiological activity by using a portable EEG recorder. Our data reveal that examination- induced stress is associated with changes in the activity of the occipital and parietal-temporal areas in the brain. We construct a machine learning framework to estimate each student’s stress level using features extracted from brain activities and a classification method. Finally, our study underlines the necessity of applying stress intervention to create an optimal learning environment for students.

Twitter bots have evolved from easily-detectable, simple content spammers with bogus identities to sophisticated players embedded in deep levels of social networks, silently promoting affiliate campaigns, marketing various products and services, and orchestrating or coordinating political activities. Much research has been reported on building accurate machine learning classifiers to identifying bots in social networks; recent works on social bots have started the new line of research on the existence, placement, and functions of the bots in a collective manner. In this paper, we study two families of Twitter bots which have been studied previously with respect to spamming activities through advertisement and political campaigns, and perform an evolutionary comparison with the new waves of bots currently found in Twitter. We uncover various evolved tendencies of the new social bots under social, communication, and behavioral patterns. Our findings show that these bots demonstrate evolved core-periphery structure; are deeply embedded in their networks of communication; exhibit complex information diffusion and heterogeneous content authoring patterns; perform mobilization of leaders across communication roles; and reside in niche topic communities. These characteristics make them highly deceptive as well as more effective in achieving operational goals than their traditional counterparts. We conclude by discussing some possible applications of the discovered behavioral and social traits of the evolved bots, and ways to build effective bot detection systems.

BACKGROUND: microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanisms is essential for the diagnostics and therapeutics for a wide range of diseases. There have been a large number of researches which use gene expression profiles to resolve this problem. However, the current methods have their own limitations. Some of them only identify the correlation of miRNA and mRNA expression levels instead of the causal or regulatory relationships while others infer the causality but with a high computational complexity. To overcome these issues, in this study, we propose a method to identify miRNA-mRNA regulatory relationships in breast cancer using the invariant causal prediction. The key idea of invariant causal prediction is that the cause miRNAs of their target mRNAs are the ones which have persistent causal relationships with the target mRNAs across different environments. RESULTS: In this research, we aim to find miRNA targets which are consistent across different breast cancer subtypes. Thus, first of all, we apply the Pam50 method to categorize BRCA samples into different "environment" groups based on different cancer subtypes. Then we use the invariant causal prediction method to find miRNA-mRNA regulatory relationships across subtypes. We validate the results with the miRNA-transfected experimental data and the results show that our method outperforms the state-of-the-art methods. In addition, we also integrate this new method with the Pearson correlation analysis method and Lasso in an ensemble method to take the advantages of these methods. We then validate the results of the ensemble method with the experimentally confirmed data and the ensemble method shows the best performance, even comparing to the proposed causal method. CONCLUSIONS: This research found miRNA targets which are consistent across different breast cancer subtypes. Further functional enrichment analysis shows that miRNAs involved in the regulatory relationships predicated by the proposed methods tend to synergistically regulate target genes, indicating the usefulness of these methods, and the identified miRNA targets could be used in the design of wet-lab experiments to discover the causes of breast cancer.

Characterizing, predicting, and quantifying the impact of postings, tweets, messages, etc. on social media platforms is a topic of growing interest due to the increasing reliance on using social media as a means for various purposes by individuals and organizations alike. In this paper, we describe an information diffusion model on the social network of Twitter. The model treats information diffusion on social media as a multivariate time series problem and deals mainly with three different dimensions of Twitter data and the different patterns of information diffusion. These dimensions are the volume of tweets, the sentiment of tweets and influence of tweets. To discover different patterns of information diffusion on Twitter, time series clustering is used where Dynamic Time Warping distance is adopted as the distance measure. To predict different parameters of each of the three dimensions, the linear time series model of Autoregressive Integrated Moving Average (ARIMA) and the non-linear time series model of Long Short-Term Memory (LSTM) Recurrent Neural Networks are used and their performance is compared. Results indicate that LSTM models achieve far better performance and hold great potential to be utilized for real-world applications.