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Tourism efficiency is crucial for measuring sustainable tourism development. Examining the relationship between aviation and tourism efficiency networks is key to promoting their synergistic development in China's urban areas. This study employs various methods, such as complex network analysis method, entropy-weighted TOPSIS, tourism efficiency gravity model, and quadratic assignment procedure, to analyze the networks' spatial structure evolution characteristics and interaction effects. Results show that (1) China's major cities' aviation network has improved its organizational efficiency and formed a "double rhombus-single axis" spatial evolution pattern of the axis-spoke network. The number of intermediary networks and hub cities in the central and western regions has increased. (2) The tourism efficiency network adopts a "honeycomb" structure pattern with the simultaneous layout of "point-to-point" and "star" networks. The network's tourism efficiency follows "Pareto's Law," and tourism cities above the second level form a club group development. The tourism efficiency development potential area is shifting to the southwest. (3) The aviation and tourism efficiency networks exhibit a clear trend of synergistic evolution with a "path locking" phenomenon between them. Differences in tourism resource endowment, labor advantage, and capital advantage positively impact the aviation network's structure. Conversely, differences in revenue capacity and market scale negatively impact the structure. The aviation scale advantage, openness, intimacy, and influence exhibit decreasing positive effects on the tourism efficiency network's structure.

Sequential recommendation can make predictions by fitting users’ changing interests based on the users’ continuous historical behavior sequences. Currently, many existing sequential recommendation methods put more emphasis upon users’ recent preference (i.e., short-term interests), but simplify or even ignore the influence of users’ long-term interests, resulting in important interest features of users not being effectively mined. Moreover, users’ real intentions may not be fully captured by only focusing on their behavior histories, because users’ interests are diverse and dynamic. To solve the above problems, we propose a novel sequential recommendation model for long-term interest memory and nearest neighbor influence. Firstly, item embeddings based on item similarity and dependency are constructed to alleviate the problem of data sparsity in users’ recent interest history. Secondly, in order to effectively capture long-term interests, the long sequence is divided into multiple nonoverlapping subsequences. For these subsequences, the graph attention network with node importance factor is designed to fully extract the main interests of subsequences, and LSTM is introduced to learn the dynamic changes of interest among subsequences. Long-term interests of users are modeled through complex structure within subsequences and sequential dependencies among subsequences. Finally, the user’s neighbor representation is introduced, and a gating module is designed to integrate the user’s neighbor information and self-interests. The influence of users’ short-term and long-term interests on prediction is dynamically controlled by considering nearby features in the gating network. The experimental results on two public datasets show that the proposed sequential recommendation model can outperform the baseline methods in hit rate (HR@K) and normalized discounted cumulative gain (NDCG@K).

To protect recommender systems against shilling attacks, a variety of detection methods have been proposed over the past decade. However, these methods focus mainly on individual features and rarely consider the lockstep behaviours among attack users, which suffer from low precision in detecting group shilling attacks. In this work, we propose a three-stage detection method based on strong lockstep behaviours among group members and group behaviour features for detecting group shilling attacks. First, we construct a weighted user relationship graph by combining direct and indirect collusive degrees between users. Second, we find all dense subgraphs in the user relationship graph to generate a set of suspicious groups by introducing a topological potential method. Finally, we use a clustering method to detect shilling groups by extracting group behaviour features. Extensive experiments on the Netflix and sampled Amazon review datasets show that the proposed approach is effective for detecting group shilling attacks in recommender systems, and the F1-measure on two datasets can reach over 99 percent and 76 percent, respectively.

Collaborative recommender systems are vulnerable to shilling attacks. To address this issue, many methods including supervised and unsupervised have been proposed. However, supervised detection methods require training classifiers and they only apply to detect known types of attacks. The existing unsupervised detection methods need to know the prior knowledge of attacks, otherwise they suffer from low detection precision. In this paper, we present BS-SC, an unsupervised approach for detecting shilling profiles, which does not need to know the attack size or to label the candidate spammers. BS-SC starts from an in-depth analysis of user behaviors and uses two key mechanisms (i.e., behavior features extraction and behavior similarity matrix clustering) to distinguish shilling profiles from genuine ones. The behavior features reflect the behavior difference between genuine and shilling profiles, and the behavior similarity matrix clustering is to cluster shilling profiles based on their highly similar behaviors. Experimental results on the MovieLens and the sampled Amazon review datasets indicate that BS-SC outperforms the baseline unsupervised approaches, even when the prior knowledge is given for them.

Sum-frequency generation (SFG) vibrational spectroscopy is a second-order nonlinear optical spectroscopy technique. Owing to its interfacial selectivity, SFG vibrational spectroscopy can provide interfacial molecular information, such as molecular orientations and order, which can be obtained directly, or molecular density, which can be acquired indirectly. Interfacial molecular behaviors are considered the basic factors for determining the tribological properties of surfaces. Therefore, owing to its ability to detect the molecular behavior in buried interfaces in situ and in real time, SFG vibrational spectroscopy has become one of the most appealing technologies for characterizing mechanisms at friction interfaces. This paper briefly introduces the development of SFG vibrational spectroscopy and the essential theoretical background, focusing on its application in friction and lubrication interfaces, including film-based, complex oil-based, and water-based lubricating systems. Real-time detection using SFG promotes the nondestructive investigation of molecular structures of friction interfaces in situ with submonolayer interface sensitivity, enabling the investigation of friction mechanisms. This review provides guidance on using SFG to conduct friction analysis, thereby widening the applicability of SFG vibrational spectroscopy.

Electricity theft costs utility companies billions of dollars worldwide annually. The electricity consumption data recorded by consumers’ smart meters, coupled with the aggregate energy supply data recorded by master meters provide a new opportunity to pinpoint the source of electricity theft. Existing works on electricity theft pinpointing either assume linear attack modes which often limit their capability in identifying nonlinear electricity theft behaviours, or incur extra cost for model training or sensor installation. Our insight hinges upon the fact that the value of electricity theft loss (ETL) should be more correlated to the meter readings of energy thieves than to those of honest consumers. Guided by this insight, we formulate the problem of electricity theft pinpointing as a time-series correlation analysis problem which does not require linearity assumption of attack modes or any cost of training. Two coefficients are defined to evaluate the suspicion level of a consumer’s reported energy consumption pattern. A comprehensive set of experiments has been conducted on a real-world energy usage dataset with several types of attacks, and the results show that our proposed technique significantly improves the pinpointing accuracy when compared with other state-of-the-art methods.