Multi-view region embedding has become an important technique to be pursued due to the increasing availability of diverse urban sensing data. Existing methods typically adopt attention-based fusion or contrastive alignment to integrate multiple data sources such as human mobility and points-of-interest (POIs) into unified region representations. However, these approaches often fail to effectively balance cross-view commonality modeling with the preservation of view-specific characteristics. To address this limitation, we propose ComSRE, a novel region embedding framework that explicitly disentangles shared and distinctive components in multi-view region representations. ComSRE integrates three key modules: (1) a Multi-view Representation Learning module that fuses complementary information across views, (2) a View-to-Commonality Contrastive Alignment module that aligns the representation of each view to a shared commonality anchor to enhance cross-view consistency, and (3) a Multi-view Differential Orthogonality module that isolates distinctive signals unique to each view and promotes their independence through orthogonality constraints. Extensive experiments on three real-world urban datasets demonstrate that ComSRE consistently outperforms state-of-the-art methods across multiple downstream tasks, achieving superior predictive accuracy and representation quality.

A recent trend in urban computing involves utilizing multi-modal data for urban region embedding, which can be further expanded in a variety of downstream urban sensing tasks. Many previous studies rely on multi-graph embedding techniques and follow a two-stage paradigm: first building a k-nearest neighbor graph based on fixed region correlations for each view, and then blending multi-view information in a posterior stage to learn region representations. However, multi-graph construction and multi-graph representation learning are not associated in most existing two-stage studies, and the relationship between them is not leveraged, which can provide complementary information to each other. In this paper, we unify these two stages into one by constructing learnable weighted complete graphs of regions and propose a new one-stage Region Embedding method with Adaptive region correlation Discovery (READ). Specifically, READ comprises three modules, including a disentangled region feature learning module utilizing a city-context Transformer to encode regions' semantic and mobility features, and an adaptive weighted multi-graph construction module that builds multiple complete graphs with learnable weights based on disentangled features of regions. In addition, we propose a multi-graph representation learning module to yield effective region representations that integrate information from multiple graphs. We conduct thorough experiments on three downstream tasks to assess READ. Experimental results demonstrate that READ considerably outperforms state-of-the-art baseline methods in urban region embedding.

Learning urban region embeddings has substantially advanced urban analysis, but their typical focus on individual cities leads to disparate embedding spaces, hindering cross-city knowledge transfer and the reuse of downstream task predictors. To tackle this issue, we present Consistency Region Embedding (CoRE), a unified framework integrating region embedding learning with cross-city latent space alignment. CoRE first embeds regions from two cities into separate latent spaces, followed by the alignment of latent space manifolds and fine-grained individual regions from both cities. This ensures compatible and comparable embeddings within aligned latent spaces, enabling predictions of various socioeconomic indicators without ground truth labels by migrating knowledge from label-rich cities. Extensive experiments show CoRE outperforms competitive baselines, confirming its effectiveness for cross-city knowledge transfer via aligned latent spaces.

Using multi-modal data to learn region representations has gained popularity for its ability to reveal diverse socioeconomic features in cities. However, many studies focus solely on semantic features from points-of-interest (POIs), neglecting the issue of spatial imbalance. This article introduces a Multi-Graph Representation Learning framework for Region Embedding (MGRL4RE), which leverages both inter-region and intra-region correlations through two main components: multi-graph construction based on various region correlations and multi-graph representation learning. The construction module creates a multi-graph reflecting various correlations among regions, utilizing geo-tagged POIs, region data, and human mobility data. Specifically, we assess a region’s importance relative to its spatial context (neighborhood) and develop spatially invariant semantic features to address spatial imbalance. Furthermore, the representation learning module generates comprehensive and effective region representations via multi-view embedding fusion. Our extensive experiments across various downstream tasks, including land use clustering, region popularity prediction, and crime prediction, confirm that our model significantly outperforms existing state-of-the-art region embedding methods.

Most traffic accidents are caused by bad driving habits. Online monitoring of the abnormal driving behaviors of drivers can help reduce traffic accidents. Recently, abnormal driving behavior recognition based on the sensors' data embedded in commodity smartphones has attracted much attention. Though much progress has been made about driving behavior recognition, the existing works cannot achieve high recognition accuracy and show poor robustness. To improve the driving behaviors recognition accuracy and robustness, we propose an algorithm based on Soft Thresholding and Temporal Convolutional Network (S-TCN) for driving behavior recognition. In this algorithm, we first introduce a soft attention mechanism to learn the importance of different sensors. The TCN has the advantages of small memory requirement and high computational efficiency. And the soft thresholding can further filter the redundant features and extract the main features. So, we fuse the TCN and soft thresholding to improve the model's stability and accuracy. Our proposed model is extensively evaluated on four real public data sets. The experimental results show that our proposed model outperforms best state-of-the-art baselines by 2.24%.

Presently existing lightweight indoor/outdoor detection schemes on phones acquire accuracy by sensing variations of ambient physical environmental properties with inherent sensors on mobile phones, with which, however, the detection scheme cannot work well in some ambient environments, where the variations are not very observable. This detection scheme is with very high dependency on light. The I/O detector does not work well in poor lighting or fast changing lighting settings, therefore the I/O detection is very much challenged at times like dawn, dusk, or night. The target of this paper is finding a pervasive detection scheme independent of physical environments. In this paper, we present MobiIO, an lightweight indoor and outdoor detection scheme based on analyses of human activities. By recording human indoor and outdoor motion activities with sensors, typical features of their activities are extracted. We compare assorted combinations or groupings of various properties with SVM classifier. We classify indoor/outdoor settings through classifiers like SVM, Bayes, decision trees, HMM and compare the effects of classification in between various classifying algorithms.

This paper introduces a new unsharp masking (UM) scheme, called nonlinear UM (NLUM), for mammogram enhancement. The NLUM offers users the flexibility 1) to embed different types of filters into the nonlinear filtering operator; 2) to choose different linear or nonlinear operations for the fusion processes that combines the enhanced filtered portion of the mammogram with the original mammogram; and 3) to allow the NLUM parameter selection to be performed manually or by using a quantitative enhancement measure to obtain the optimal enhancement parameters. We also introduce a new enhancement measure approach, called the second-derivative-like measure of enhancement, which is shown to have better performance than other measures in evaluating the visual quality of image enhancement. The comparison and evaluation of enhancement performance demonstrate that the NLUM can improve the disease diagnosis by enhancing the fine details in mammograms with no a priori knowledge of the image contents. The human-visual-system-based image decomposition is used for analysis and visualization of mammogram enhancement.

Conflicts occur in almost the whole course of any computer supported cooperative system. A conflict resolution strategy based on complex-system theories is presented in this paper, and has been instantiated in our engineering practice. The main contributions of this paper are: (1) applying complex-system theories to the conflict resolution strategy; (2) presenting a general conflict model based on the complex-system theories. This model can overcome the precious model dependant on the global strategy which can not be obtained thoroughly in a complex CSCW system; (3) utilizing the role assignment mechanism so as to reach conflict resolutions in a computer supported cooperative system.