LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection

With the development of intelligent transportation systems, vehicles are exposed to a complex network environment. As the main network of in-vehicle networks, the controller area network (CAN) has many potential security hazards, resulting in higher requirements for intrusion detection systems to ensure safety. Among intrusion detection technologies, methods based on deep learning work best without prior expert knowledge. However, they all have a large model size and rely on cloud computing, and are therefore not suitable to be installed on the in-vehicle network. Therefore, we propose a lightweight parallel neural network structure, LiPar, to allocate task loads to multiple electronic control units (ECU). The LiPar model consists of multi-dimensional branch convolution networks, spatial and temporal feature fusion learning, and a resource adaptation algorithm. Through experiments, we prove that LiPar has great detection performance, running efficiency, and lightweight model size, which can be well adapted to the in-vehicle environment practically and protect the in-vehicle CAN bus security.Comment: 13 pages, 13 figures, 6 tables, 51 referenc

Spatial Embodied Experience and Rhythm Analysis in the Everyday Life of a Traditional Community in a Metropolis: An Auto-Ethnographic Study

The study of everyday life has gained attention across various disciplines in the context of modernity. This study utilizes Lefebvre's rhythmanalysis to explore the everyday experiences of residents in the Xiguan Community, a historically significant residential area in western Guangzhou undergoing urbanization and tourism development. Adopting an emic perspective, this study employed auto-ethnography to depict the daily rhythms and spatially embodied experiences of the author, a native resident of the Xiguan Community. By incorporating reflective and self-narrative elements and comparing them across generations, this approach provides first-hand knowledge and self-awareness. This research offers an insider's comprehensive understanding of the effects of urbanization and tourism on residents' everyday lives. Informed by Lefebvre's rhythmanalysis, this analysis incorporates spatial and temporal dimensions, with a specific emphasis on residents' experiences of spatial embodiment and their engagement with everyday rhythms. The study reveals two key findings: First, urbanization and the commodification of landscapes have created a constructed "the present" in traditional communities, displacing the meaningful "existence" of everyday life. Certain spaces within these communities have detached from residents' everyday lives, serving urban and tourism purposes, and leading to partial alienation in spatial and temporal dimensions. These spaces represent the simulacra and fragments of residents' everyday lives, lacking subjectivity, temporality, and wholeness. Over time, the "existence" that embodies the meaning of residents' everyday lives has been squeezed out by structural forces such as urban renewal and community tourism. For tourists, these landscapes may serve only as replicas of attractions, devoid of the essence of residents' everyday lives. For the residents, these community spaces have become manifestations of instrumental rationality and commodification. Second, this study highlights that traditional community residents' bodies are disciplined and governed by the instrumental rationality of urban production and the invisible rhythms of the tourism industry. In large cities, the significance of individual bodies in traditional urban communities is often overlooked, as bodies become tools for creating value through work. Individuals adjust their everyday rhythms based on urban settings' production rationality and efficiency priorities. This undermines the bodily rhythms that align with natural cycles and prompts residents to distance themselves from traditional communities. While the older generation in the Xiguan Community experiences overlapping leisure time and shares community spaces, fostering solid social relationships, the younger generation faces longer working hours, extended commuting distances, and more individualized leisure time. As a result, there is a lack of synchronization in leisure rhythms among neighbors. The embodied rhythms of traditional community residents have shifted from a state of harmony with natural rhythms and community spaces to being governed by the instrumental rationality of urban production and invisible rhythms of the tourism industry. This study provides an emic and longitudinal perspective to the investigation of spatial experiences and embodied rhythms in urban and tourism development. The use of auto-ethnography amplifies residents' voices and calls for greater consideration of local daily life. These findings emphasize the importance of incorporating residents' everyday experiences into the planning and development of sustainable communities and tourism

A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite vital to the emergency rescue in the 72-h “golden window”. However, the limited quantity of interpreted landslides shortly after a massive earthquake makes landslide susceptibility prediction become a challenge. To address this gap, this work suggests an integrated method of Crossing Graph attention network and xgBoost (CGBoost). This method contains three branches, which extract the interrelations among pixels within a slope unit, the interrelations among various slope units, and the relevance between influencing factors and landslide probability, respectively, and obtain rich and discriminative features by an adaptive fusion mechanism. Thus, the difficulty of susceptibility modeling under a small number of coseismic landslides can be reduced. As a basic module of CGBoost, the proposed Crossing graph attention network (Crossgat) could characterize the spatial heterogeneity within and among slope units to reduce the false alarm in the susceptibility results. Moreover, the rainfall dynamic factors are utilized as prediction indices to improve the susceptibility performance, and the prediction index set is established by terrain, geology, human activity, environment, meteorology, and earthquake factors. CGBoost is applied to predict landslide susceptibility in the Gorkha meizoseismal area. 3.43% of coseismic landslides are randomly selected, of which 70% are used for training, and the others for testing. In the testing set, the values of Overall Accuracy, Precision, Recall, F1-score, and Kappa coefficient of CGBoost attain 0.9800, 0.9577, 0.9999, 0.9784, and 0.9598, respectively. Validated by all the coseismic landslides, CGBoost outperforms the current major landslide susceptibility assessment methods. The suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future

DataSheet1_Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm.docx

Disastrous landslides have become a focus of the world’s attention. Landslide susceptibility evaluation (LSE) can predict where landslides may occur and has caught the attention of scientists all over the world. This work establishes integrated criteria of potential landslide recognition and combines the historical landslides and newly-identified potential landslides to improve the accuracy, rationality, and practicability of a LSE map. Moreover, slope units can well reflect the topographic constraint to landslide occurrence and development, and Graph Convolutional Network (GCN) can well portray the topological and feature relation among various slope units. The combination of slope units and GCN is for the first time employed in LSE. This work focuses on Wanzhou District, a famous landslide-serious region in the Three Gorges reservoir area, and employs multisource data to conduct potential landslide recognition and LSE and to reveal the distribution characteristics of high landslide susceptibility. Some new viewpoints are suggested as follows. 1) The established criteria of potential landslide recognition consist of the characteristics of active deformation, stratum and lithology, tectonics, topography, micro-geomorphology, environment, meteorology, earthquakes, and human engineering activity. These criteria can well eliminate 4 types of false alarm regions and is successfully validated by field survey. 2) 34 potential landslides are newly discovered, and the movement of these potential landslides were controlled or induced by the combined action of soft-hard interbedding rock mass, steep topography, frequent tectonic movement, strong fluvial erosion, abundant precipitation, and intensive road and building construction. 3) The GCN algorithm reaches a relatively high accuracy (AUC: 0.941) and outperforms the other representative machine learning algorithms of Convolutional Neural Network (AUC: 0.926), Support Vector Machine (AUC: 0.835), and CART Tree (AUC: 0.762). 4) High landslide susceptibility is caused by the coupled action of weathered rock cavities, soft rock and swelling soil, strong river erosion, abundant rainfall, and intensive human engineering activity.</p