Abstract: Nowadays, network traffic identification, as the fundamental technique in the field of cybersecurity, suffers from a critical problem, namely “unknown traffic”. The unknown traffic refers to network traffic generated by previously unknown applications (i.e., zero-day applications) in a pre-constructed traffic classification system. The ability to divide the mixed unknown traffic into multiple clusters, each of which contains only one application traffic as far as possible, is the key to solve this problem. In this paper, we propose the DePCK to improve the clustering purity. There are two main innovations in our framework: (i) It learns to extract bottleneck features via deep auto-encoder from traffic statistical characteristics; (ii) It uses the flow correlation to guide the process of pairwise constrained k-means. To verify the effectiveness of our framework, we make contrast experiments on two real-world datasets. The experimental results show that the clustering purity rate of DePCK can exceed 94.81% on the ISP-data and 91.48% on the WIDE-data, which outperform the state-of-the-art methods: RTC, and k-means with log data.

Abstract: Converting first-time users into recurring ones is key for the success of Web-based applications. This problem is known as Pure Cold-Start and it refers to the capability of Recommender Systems (RSs) to provide useful recommendations to users without historical data. Traditionally, RSs assume that non-personalized recommendation can mitigate this problem. However, several users are not interested in consuming just biased-items, such as popular or best-rated items. Then, we introduce two new approaches inspired in user coverage maximization to deal with this problem. These coverage-based RSs reached a high number of distinct first-time users. Thus, we proposed to compose the product’s page by mixing complementary non-personalized RSs. An online study, conducted with 204 real users confirmed that we should diversify the RSs used to conquer first-time users.

Abstract: With the unprecedented prevalence of social media, rumor detection has become increasingly important since it can prevent misin- formation from spreading in the public. Traditional approaches extract features from the source tweet, the replies, the user profiles as well as the propagation path of a rumor event. However, these approaches do not take the sentiment view of the users into account. The conflicting affirmative or denial stances of users can provide crucial clues for rumor detection. Besides, the existing work attach the same importance to all the words in the source tweet, but actually these words are not equally informative. To address these problems, we propose a simple but effec- tive multi-view deep learning model that is supposed to excavate stances of users and assign weights for different words. Experimental results on a social-media based dataset reveal that the multi-view model we pro- posed are useful, which achieves the state-of-the-art performance on the accuracy of automatic rumor detection. Our three-view model achieves 95.6% accuracy and our four-view model using BERT as a view also reaches an improvement of detection accuracy.

Abstract: Building a high-quality emotion lexicon is regarded as the foundation of research on emotion analysis. Existing methods have focused on the study of primary categories (i.e., anger, disgust, fear, happiness, sadness, and surprise). However, there are many emotions expressed in texts that are difficult to be mapped to primary emotions, which poses a great challenge in emotion annotation for big data analysis. For instance, "despair" is a combination of "fear" and "sadness," and thus it is difficult to divide into each of them. To address this problem, we propose an automatic building method of emotion lexicon based on the psychological theory of compound emotion. This method could map emotional words into an emotion space, and annotate different emotion classes through a cascade clustering algorithm. Our experimental results show that our method outperforms the state-of-the-art methods in both word and sentence-level primary classification performance, and also offer us some insights into compound emotion analysis.

Abstract: The eects of climate change have been much speculated on in the past few years. Consequently, there has been intense interest in one of its key issues of food security into the future. This is particularly so given population increase, urban encroachment on arable land, and the degradation of the land itself. Recently, work has been done on predicting precipitation and temperature for the next few decades as well as developing optimisation models for crop planning. Combining these together, this paper examines the eects of climate change on a large food producing region in Australia, the Murrumbidgee Irrigation Area. For time periods between 1991 and 2071 for dry, average and wet years, an analysis is made about the way that crop mixes will need to change to adapt for the eects of climate change. It is found that sustainable crop choices will change into the future, particularly those that require large amounts of water, such as cotton