Detecting social media bots is essential for maintaining the security and trustworthiness of social networks. While contemporary graph-based detection methods demonstrate promising results, their practical application is limited by label reliance and poor generalization capability across diverse communities. Generative Graph Self-Supervised Learning (GSL) presents a promising paradigm to overcome these limitations, yet existing approaches predominantly follow the homophily assumption and fail to capture the global patterns in the graph, which potentially diminishes their effectiveness when facing the challenges of interaction camouflage and distributed deployment in bot detection scenarios. To this end, we propose BotHP, a generative GSL framework tailored to boost graph-based bot detectors through heterophily-aware representation learning and prototype-guided cluster discovery. Specifically, BotHP leverages a dual-encoder architecture, consisting of a graph-aware encoder to capture node commonality and a graph-agnostic encoder to preserve node uniqueness. This enables the simultaneous modeling of both homophily and heterophily, effectively countering the interaction camouflage issue. Additionally, BotHP incorporates a prototype-guided cluster discovery pretext task to model the latent global consistency of bot clusters and identify spatially dispersed yet semantically aligned bot collectives. Extensive experiments on two real-world bot detection benchmarks demonstrate that BotHP consistently boosts graph-based bot detectors, improving detection performance, alleviating label reliance, and enhancing generalization capability.

Albeit presenting great capabilities for graph node representations, Graph neural networks (GNNs) face biases and discrimination issues. Existing solutions mostly focus on one aspect of fairness, failing to thoroughly consider the multiple dimensions of fairness. To break this limitation, we propose a novel fairness-aware framework, FacGNN, which establishes connections between group fairness, counterfactual fairness, and stability fairness for the first time. Specifically, FacGNN enhances both group fairness and counterfactual fairness while extending stability metrics beyond prior works. FacGNN employs a phased nested iterative training process with self-supervised contrastive learning, to model fairness relationships and improve discriminatory detection in adversarial frameworks. Experimental results show that FacGNN outperforms previous methods across group fairness, counterfactual fairness, and model stability while maintaining model performance.

Synthetic lethality (SL) prediction is used to identify if the co-mutation of two genes results in cell death. The prevalent strategy is to abstract SL prediction as an edge classification task on gene nodes within SL data and achieve it through graph neural networks (GNNs). However, GNNs suffer from limitations in their message passing mechanisms, including over-smoothing and over-squashing issues. Moreover, harnessing the information of non-SL gene relationships within large-scale multi-omics data to facilitate SL prediction poses a non-trivial challenge. To tackle these issues, we propose a new multi-omics sampling-based graph transformer for SL prediction (MSGT-SL). Concretely, we introduce a shallow multi-view GNN to acquire local structural patterns from both SL and multi-omics data. Further, we input gene features that encode multi-view information into the standard self-attention to capture long-range dependencies. Notably, starting with batch genes from SL data, we adopt parallel random walk sampling across multiple omics gene graphs encompassing them. Such sampling effectively and modestly incorporates genes from omics in a structure-aware manner before using self-attention. We showcase the effectiveness of MSGT-SL on real-world SL tasks, demonstrating the empirical benefits gained from the graph transformer and multi-omics data.

Localizing a vehicle on a prebuilt HD vector map is a prerequisite for many autonomous driving applications. Existing visual localization approaches usually require a separate local feature layer to function. The separate localization layer suffers from the robustness issue inherited from the local features. Also, it could be difficult to create a feature layer that aligns perfectly with an existing vector map. In this paper, we propose a monocular visual localization method that exploits the vector map directly as the localization layer. The method detects semantic traffic elements from the images and matches them with the vectors in the map. To deal with the harmful problem of false matches, we propose to align the vector map to the distance transforms of the semantic detections, which enables a non-explicit and differentiable data association process. The system is able to achieve centimeter and sub-meter accuracies in lateral and longitudinal directions, respectively.

Localization is a crucial prerequisite for automated valet parking, in which a vehicle is required to navigate itself in a GPS-denied parking lot. Traditional visual localization methods usually build a feature map and use it for future localizations. However, the feature map is not robust to changes in illumination, appearance, and viewing perspective. To deal with this issue, we need a more stable map. In this paper, we propose to use the parking lot’s HD vector map directly for localization. The vector representation is ultimately stable but brings challenges in data association as well. To this end, we present a novel data association method to match the surround-view images with the vector map. In addition, we also propose a closed-form relocalization strategy by exploiting distinctive road mark combinations in the vector map. Experiments show that the proposed method is able to achieve centimeter-level localization accuracy in a multi-floor parking lot.

Currently, people gain easy access to an increasingly diverse range of mobile devices such as personal digital assistants (PDAs), smart phones, and handheld computers. As dynamic content has become dominant on the fast-growing World Wide Web (C. Yuan et al., 2003), it is necessary to provide effective ways for the users to access such prevalent Web content in a mobile computing environment. During a course of browsing dynamic content on mobile devices, the requested content is first dynamically generated by remote Web server, then transmitted over a wireless network, and, finally, adapted for display' on small screens. This leads to considerable latency and processing load on mobile devices. By integrating a novel Web content adaptation algorithm and an enhanced caching strategy, we propose an adaptive scheme called MobiDNA for serving dynamic content in a mobile computing environment. To validate the feasibility and effectiveness of the proposed MobiDNA system, we construct an experimental testbed to investigate its performance. Experimental results demonstrate that this scheme can effectively improve mobile dynamic content browsing, by improving Web content readability on small displays, decreasing mobile browsing latency, and reducing wireless bandwidth consumption

Pictures have become increasingly common and popular in mobile communications. However, due to the limitation of mobile devices, there is a need to develop new technologies to facilitate the browsing of large pictures on the small screen. In this paper, we propose a set of novel approaches which are able to aid or automate common image browsing tasks on mobile devices. All of these approaches are based on an image attention model which is employed to illustrate the information structure within an image. An efficient algorithm to generate the optimal browsing path based on the information foraging theory is presented. Experimental evaluations of the proposed mechanism indicate that our approach is an effective way for viewing large images on small displays

Mobile devices which can capture and view pictures are becoming increasingly common in our life. The limitation of these small-form-factor devices makes the user experience of image browsing quite different from that on desktop PCs. In this paper, we first present a user study on how users interact with a mobile image browser with basic functions. We found that on small displays, users tend to use more zooming and scrolling actions in order to view interesting regions in detail. From this fact, we designed a new method to detect user interest maps and extract user attention objects from the image browsing log. This approach is more efficient than image-analysis based methods and can better represent users' actual interest. A smart image viewer was then developed based on user interest analysis. A second experiment was carried out to study how users behave with such a viewer. Experimental results demonstrate that the new smart features can improve the browsing efficiency and are a good compliment to traditional image browsers.

The synchronized multimedia integration language (SML/spl trade/) fulfills the needs of integration, synchronization, and efficient online delivery of different media types such as text, music, speech, image, and video. In this paper, we represent these multimedia elements in a synchronized manner under a unified feature space. An efficient SMIL retrieval scheme based on textual feature and content feature is proposed. Pilot experiments on our SMIL database show that the proposed method can work well on SMIL retrieval.

Web image search engine has become an important tool to organize digital images on the Web. However, most commercial search engines still use a list presentation while little effort has been placed on improving their usability. How to present the image search results in a more intuitive and effective way is still an open question to be carefully studied. In this demo, we present iFind, a scalable Web image search engine, in which we integrated two kinds of search result browsing interfaces. User study results have proved that our interfaces are superior to traditional interfaces.

In this paper, we present an efficient query-by-singing based musical retrieval system. We first combine multiple Support Vector Machines by classifier committee learning to segment the sentences from a song automatically. Many new methods in manipulating Mel-Frequency Cepstral Coefficient (MFCC) matrix are studied and compared for optimal feature selection. Experiments show that the 3rd coefficient is the most relevant to music comparison out of 13 coefficients and the proposed simplified MFCC feature is able to achieve a reasonable trade-off between accuracy and efficiency. To improve system efficiency, we re-organize the database by a new two-stage clustering scheme in both time space and feature space. We combine K-means algorithm and dynamic time wrapping similarity measurement for feature space clustering. We also propose a new method for model-selection of K-means algorithm. Experiments show that the proposed approach can achieve more than 30 percent increase in accuracy while speed up more than 16 times in average query time.

Pictures have become increasingly common and popular in mobile communications. However, due to the limitation of mobile devices, there is a need to develop new technologies to facilitate the browsing of large pictures on the small screen. In this paper, we propose a novel approach which is able to automate the scrolling and navigation of a large picture with a minimal amount of user interaction on mobile devices. An image attention model is employed to illustrate the information structure within an image. An optimal image browsing path is then calculated based on the image attention model to simulate the human browsing behaviors. Experimental evaluations of the proposed mechanism indicate that our approach is an effective way for viewing large images on small displays.