Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

Accurate absolute pose regression is one of the key challenges in robotics and computer vision. Existing direct regression methods suffer from two limitations. First, some noisy scenarios such as poor illumination conditions are likely to result in the uncertainty of pose estimation. Second, the output n-dimensional feature vector in the Euclidean space $\mathbb{R}^n$ cannot be well mapped to $SE(3)$ manifold. In this work, we propose a deep dual quaternion network that performs the absolute pose regression on $SE(3)$. We first develop an antipodally symmetric probability distribution over the unit dual quaternion on $SE(3)$ to model uncertainties and then propose an intermediary differential representation space to replace the final output pose, which avoids the mapping problem from $\mathbb{R}^n$ to $SE(3)$. In addition, we introduce a backpropagation method that considers the continuousness and differentiability of the proposed intermediary space. Extensive experiments on the camera re-localization task on the Cambridge Landmarks and 7-Scenes datasets demonstrate that our method greatly improves the accuracy of the pose as well as the robustness in dealing with uncertainty and ambiguity, compared to the state-of-the-art.

With the rapid increase in the use of mobile devices in people’s daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed in close proximity to mobile users, caching popular data on edge servers can ensure mobile users’ low-latency access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data caching problem with a focus on the reduction of network delay and the improvement of mobile devices’ energy efficiency. In this article, we tackle this data caching problem in the edge computing environment from a service provider’s perspective with the aim to maximize its data caching revenue. This problem is challenging because there is a trade-off between the benefit produced and the cost incurred by caching data on edge servers. In the meantime, the constraint for data access latency must also be fulfilled. In this article, we formulate the data caching problem in the edge computing environment as an integer programming (IP) problem and prove its NP-completeness. To solve this problem effectively and efficiently in large-scale scenarios, we propose an approximation approach to find near-optimal solutions. Extensive experiments are conducted on a widely-used real-world dataset to evaluate our approaches.

In this paper, we present a series of methods to improve the performance of spoken language understanding in the 1st Chinese Audio-Textual Spoken Language Understanding Challenge (CATSLU 2019) which is aimed to improve the robustness for automatic speech recognition (ASR) errors and to solve the problem of not enough labeled data in new domains. We combine word information and char information to improve the performance of the semantic parser. We also use some transfer learning methods like correlation alignments to improve the robustness of the spoken language understanding system. Then we merge the rule method and the neural network method to raise system output performance. In video and weather domains with few training data, we use both the transfer learning model trained on multi-domain data and the rule-based approach. Our approaches achieve F1 scores of 86.83%, 92.84%, 94.16%, and 93.04% on the test sets of map, music, video and weather domains.

With the rapid increase in the use of mobile devices in people's daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed around mobile users, caching popular data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache problem with a focus on the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack the data caching problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches.

With more and more text-image co-occurrence data becoming available on the Web, we are interested in how text especially Chinese context around images can aid image classification. The goal is to construct a classification system for images, and we used the context of the images to improve the classification system. First, we extracted three kinds of features, including global visual features, local visual features, and text features using both the image content and context. Then, we tried various feature combination methods and train classifiers for each kind of feature vector. Finally, we used a classifier fusion strategy based on weight learning, combining classifier outputs together, and we obtained the category of unlabeled images. In our experiments on the data set extracted from Google Image Search, we demonstrated the benefit of using context to help image classification. By comparing different feature combination methods on our feature set, we adopted the most effective one. Meanwhile, the classifier fusion approach improves the classification accuracy.

With the available resources on the Internet becoming plentiful, a large amount of harmful information is permeating in and has been seriously affecting people's normal work and living. Therefore, harmful data streams must be recognized and filtered out effectively. After analyzing some harmful contents in Internet information streams, we present a new method, which recognizes specific information by machine learning (ML). We extracted key information from a number of corpuses through the ML method to obtain the part of speech (POS) transfer-form for key information by learning from corpuses, which is based on the same pronunciation matching of key information. Furthermore, the testing value of key information will be obtained in a real corpus to examine the likelihood between matching rules from information streams and those learnt from corpuses through the average value of POS transfer probability of key information. Therefore, the testing value for the whole real data stream will be obtained The experiment proved that the method was efficient for recognizing certain Internet harmful information.