Navigation within Cave Automatic Virtual Environment (CAVE) systems often faces challenges due to limited physical space and the necessity for seamless user interaction. Traditional solutions typically rely on multi-view tracking systems or constrained locomotion techniques, which can interrupt immersion and hinder usability. In this paper, we introduce MoRLACS, a novel locomotion approach for CAVE systems that leverages a single RGBD camera. This hybrid framework integrates small-scale physical walking with controller-based large-scale exploration through a tailored guidance method. By accurately tracking the user's head position in the real world and synchronizing it with the virtual camera, MoRLACS enables natural walking within confined CAVE spaces and supports extended interaction in larger virtual environments. Preliminary user experiments demonstrate the approach's effectiveness, revealing improvements in usability and a heightened sense of presence. These findings underscore the potential of MoRLACS to enrich user experiences in immersive CAVE settings and offer valuable design insights for integrating 3D sensor data into multimedia interaction frameworks.

The widespread application of face recognition technology has exacerbated privacy threats. Face de-identification is an effective means of protecting visual privacy by concealing identity information. While deep learning-based methods have greatly improved de-identification results, most existing algorithms rely on 2D generative models that struggle to produce identity-consistent results for multiple views. In this paper, we focus on identity disentanglement within the latest 3D-aware face generation model, and propose an advanced face de-identification framework that can be applied to various scenarios. Our proposed framework disentangles identity from other facial features, modifies only the former and generates the de-identified face using a 3D generator. This approach results in high-quality, identity-consistent de-identification that preserves other facial features. We demonstrate our approach on StyleNeRF, one of the most widely-used style-based neural radiation field models. Through extensive experiments, we demonstrate the effectiveness of our approach in achieving face de-identification both for a single image and group images with the same identity. Our work is a significant step forward in the field of face de-identification, opening up new possibilities for practical applications.

In recent years, neural radiance fields have exhibited impressive performance in novel view synthesis. However, exploiting complex network structures to achieve generalizable NeRF usually results in inefficient rendering. Existing methods for accelerating rendering directly employ simpler inference networks or fewer sampling points, leading to unsatisfactory synthesis quality. To address the challenge of balancing rendering speed and quality in generalizable NeRF, we propose a novel framework, NeRF-SDP, which achieves both efficiency and high fidelity by introducing scene depth perception. We incorporate more scene information into the radiance field by using our proposed geometry feature extraction and depth-encoded ray transformer to improve the model’s inference capabilities with sparse points. With the aid of scene depth perception, NeRF-SDP can better understand the scene’s structure, thus better reconstructing the objects’ edges with significantly fewer artifacts. Experimental results demonstrate that NeRF-SDP achieves comparable synthesis quality to state-of-the-art methods while significantly improving rendering efficiency. Furthermore, ablation studies confirm that the depth-encoded ray transformer enhances the model’s robustness to varying numbers of sampling points.

Emerging interests have been brought to blind quality assessment for images/videos captured in the wild, known as in-the-wild I/VQA. Prior deep learning based approaches have achieved considerable progress in I/VQA, but are intrinsically troubled with two issues. Firstly, most existing methods fine-tune the image-classification-oriented pre-trained models for the absence of large-scale I/VQA datasets. However, the task misalignment between I/VQA and image classification leads to degraded generalization performance. Secondly, existing VQA methods directly conduct temporal pooling on the predicted frame-wise scores, resulting in ambiguous inter-frame relation modeling. In this work, we propose a two-stage architecture to separately predict image and video quality in the wild. In the first stage, we resort to supervised contrastive learning to derive quality-aware representations that facilitate the prediction of image quality. Specifically, we propose a novel quality-aware contrastive loss to pull together samples of similar quality and push away quality-different ones in embedding space. In the second stage, we develop a Relation-Guided Temporal Attention (RTA) module for video quality prediction, which captures global inter-frame dependencies in embedding space to learn frame-wise attention weights for frame quality aggregation. Extensive experiments demonstrate that our approach performs favorably against state-of-the-art methods on both authentically distorted image benchmarks and video benchmarks.

With the soaring development of large scale online social networks, online information sharing is becoming ubiquitous everyday. Various information is propagating through online social networks including both the positive and negative. In this paper, we focus on the negative information problems such as the online rumors. Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on a realistic scenario. In addition, different from existing problems of influence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method.

Twitter displays the tweets a user received in a reversed chronological order, which is not always the best choice. As Twitter is full of messages of very different qualities, many informative or relevant tweets might be flooded or displayed at the bottom while some nonsense buzzes might be ranked higher. In this work, we present a supervised learning method for personalized tweets reordering based on user interests. User activities on Twitter, in terms of tweeting, retweeting, and replying, are leveraged to obtain the training data for reordering models. Through exploring a rich set of social and personalized features, we model the relevance of tweets by minimizing the pairwise loss of relevant and irrelevant tweets. The tweets are then reordered according to the predicted relevance scores. Experimental results with real twitter user activities demonstrated the effectiveness of our method. The new method achieved above 30% accuracy gain compared with the default ordering in twitter based on time.

In this poster we report our study on the microblog spammers with samples attracted by 50 honeyspots from two popular Chinese microblogging networks: Sina Weibo (weibo.com), and Ten cent Weibo (t.QQ.com) in seven months. We studied their features such as social information, activity, account age and spamming strategy. Several distinguishing characteristics of spammers on these two social network communities are observed, which can be helpful to the further study on automatic detection of microblog spammers. To our best knowledge our work is the first of its kind on the analysis of features of Chinese micloblog spammers.

As the popularity of social networking sites increase, so does their attractiveness for criminals. In this work, we show how an adversary can build artificial identities using semantic information in social network. Our method make the identities look more like real people, therefore can be used to support many kinds of attacks, such as ASE, profile cloning. A prototype of this method is implemented, includes following stages: Firstly, categories of virtual identity are predefined, and each category has multiple properties, such as geographical region, hobby, education, age, interested topic/keywords, etc. Secondly, based on category information, each identity will foster its own "life" semantically, such as edit profile and update status, find hot related news/topic from Google then post to wall, find related groups/networks then request to add in, and find/like/create/comment pages/posts, etc. Thirdly, artificial identity will evolve to multiple stages according to its status (for example, number of friends of real people), single identity with different evolutionary stages is linked together to a group that will help to ensure the number of attack edges.

The sequential context modeling framework is generalized to a non-sequential one by context relaxation from consecutive suffix of the subsequences of symbols to the permutation of the preceding symbols as result of considering complex context structures in such sources as video and program binaries. Context weighting tree is also extended to a series of context trees which are built according to the "model tree", in which the descendent relationship in the formation of non-sequential context sets is described. Model redundancy and maximum a posteriori model in the framework are discussed and compared. A decision method based on the greedy algorithm is proposed to customize sets of models fitting the concrete sources. Brief description of application to executable data files incorporating with the semantics and syntax constraints are given and experiment are made accordingly as a validation.