No abstract

Music content has recently been identified as useful information to promote the performance of music recommendations. Existing studies usually feed low-level audio features, such as the Mel-frequency cepstral coefficients, into deep learning models for music recommendations. However, such features cannot well characterize music audios, which often contain multiple sound sources. In this paper, we propose to model and fuse chord, melody, and rhythm features to meaningfully characterize the music so as to improve the music recommendation. Specially, we use two user-based attention mechanisms to differentiate the importance of different parts of audio features and chord features. In addition, a Long Short-Term Memory layer is used to capture the sequence characteristics. Those features are fused by a multilayer perceptron and then used to make recommendations. We conducted experiments with a subset of the last.fm-1b dataset. The experimental results show that our proposal outperforms the best baseline by [Formula: see text] on HR@10.

In recent years, with the development of various types of public transportation, they are also more and more closely connected. Among them, subway transportation has become the first choice of major cities. However, the planning of subway stations is very difficult and there are many factors to consider. Besides, few methods for selecting optimal station locations take other public transport in to consideration. In order to study the relationship between different types of public transportation, the authors collected and analyzed the travel data of subway passengers and the passenger trajectory data of taxis. In this paper, a method based on LeaderRank and Gaussian Mixed Model (GMM) is proposed to conduct subway station locations selection. In this method, the author builds a subway-passenger traffic zone weighted network and a station location prediction model. First, we evaluate the nodes in the network, then use the GPS track data of taxis to predict the location of new stations in future subway construction, and analyze and discuss the land use characteristics in the prediction area. Taking the design of the Beijing subway line as an example, the suitability of this method is illustrated.

Social engineering attacks are a growing threat to modern complex systems. Increasingly, attackers are exploiting people's "vulnerabilities" to carry out social engineering attacks for malicious purposes. Although such a severe threat has attracted the attention of academia and industry, it is challenging to propose a comprehensive and practical set of countermeasures to protect systems from social engineering attacks due to its interdisciplinary nature. Moreover, the existing social engineering defence research is highly dependent on manual analysis, which is time-consuming and labour-intensive and cannot solve practical problems efficiently and pragmatically. This paper proposes a systematic approach to generate countermeasures based on a typical social engineering attack process. Specifically, we systematically 'attack' each step of social engineering attacks to prevent, mitigate, or eliminate them, resulting in 62 countermeasures. We have designed a set of social engineering security patterns that encapsulate relevant security knowledge to provide practical assistance in the defence analysis of social engineering attacks. Finally, we present an automatic analysis framework for applying social engineering security patterns. We applied the case study method and performed semi-structured interviews with nine participants to evaluate our proposal, showing that our approach effectively defended against social engineering attacks.

With the development of mobile networks and the rapid prevalence of location-based social networks (LBSN), a massive volume of spatiotemporal data has been generated, which is valuable for points of interest (POI) recommendation. However, current studies have not unleashed the full power of such spatiotemporal data, which either explore only a single dimension of the data or consider multiple factors in an asynchronous fashion. In this article, we propose a novel spatiotemporal network-based recommender framework (STNBR) to effectively recommend POIs for users. Specifically, we first establish a comprehensive conceptual model of spatiotemporal data, involving various essential factors for POIs recommendation. On top of the conceptual model, we design a series of meaningful meta-paths that simultaneously consider the time and location factors to precisely capture the semantics of user behaviours. By profiling users based on their embedded meta-paths, our approach can yield meaningful POIs recommendations. We have evaluated our proposal using a realistic dataset obtained from Foursquare and Gowalla, the results of which show that our STNBR model outperforms existing approaches.