OpenRaiser/Intern-Atlas huggingface dataset:
Moving Avatars and Agents in Social Extended Reality Environments
Venue: arXiv cs.HC 2023
Year: 2023

Natural interaction between multiple users within a shared virtual environment (VE) relies on each other's awareness of the current position of the interaction partners. This, however, cannot be warranted when users employ noncontinuous locomotion techniques, such as teleportation, which may cause confusion among bystanders. In this paper, we pursue two approaches to create a pleasant experience for both the moving user and the bystanders observing that movement. First, we will introduce a Smart Avatar system that delivers continuous full-body human representations for noncontinuous locomotion in shared virtual reality (VR) spaces. Smart Avatars imitate their assigned user's real-world movements when close-by and autonomously navigate to their user when the distance between them exceeds a certain threshold, i.e., after the user teleports. As part of the Smart Avatar system, we implemented four avatar transition techniques and compared them to conventional avatar locomotion in a user study, revealing significant positive effects on the observer's spatial awareness, as well as pragmatic and hedonic quality scores. Second, we introduce the concept of Stuttered Locomotion, which can be applied to any continuous locomotion method. By converting a continuous movement into short-interval teleport steps, we provide the merits of non-continuous locomotion for the moving user while observers can easily keep track of their path. Thus, while the experience for observers is similarly positive as with continuous motion, a user study confirmed that Stuttered Locomotion can significantly reduce the occurrence of cybersickness symptoms for the moving user, making it an attractive choice for shared VEs. We will discuss the potential of Smart Avatars and Stuttered Locomotion for shared VR experiences, both when applied individually and in combination.
TaiChi Action Capture and Performance Analysis with Multi-view RGB Cameras
Venue: arXiv cs.CV 2023
Year: 2023

Recent advances in computer vision and deep learning have influenced the field of sports performance analysis for researchers to track and reconstruct freely moving humans without any marker attachment. However, there are few works for vision-based motion capture and intelligent analysis for professional TaiChi movement. In this paper, we propose a framework for TaiChi performance capture and analysis with multi-view geometry and artificial intelligence technology. The main innovative work is as follows: 1) A multi-camera system suitable for TaiChi motion capture is built and the multi-view TaiChi data is collected and processed; 2) A combination of traditional visual method and implicit neural radiance field is proposed to achieve sparse 3D skeleton fusion and dense 3D surface reconstruction. 3) The normalization modeling of movement sequences is carried out based on motion transfer, so as to realize TaiChi performance analysis for different groups. We have carried out evaluation experiments, and the experimental results have shown the efficiency of our method.
Near-Field Beamforming for STAR-RIS Networks
Venue: arXiv cs.IT 2023
Year: 2023

Recently, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have received significant research interest. The employment of large STAR-RIS and high-frequency signaling inevitably make the near-field propagation dominant in wireless communications. In this work, a STAR-RIS aided near-field multiple-input multiple-multiple (MIMO) communication framework is proposed. A weighted sum rate maximization problem for the joint optimization of the active beamforming at the base station (BS) and the transmission/reflection-coefficients (TRCs) at the STAR-RIS is formulated. The non-convex problem is solved by a block coordinate descent (BCD)-based algorithm. In particular, under given STAR-RIS TRCs, the optimal active beamforming matrices are obtained by solving a convex quadratically constrained quadratic program. For given active beamforming matrices, two algorithms are suggested for optimizing the STAR-RIS TRCs: a penalty-based iterative (PEN) algorithm and an element-wise iterative (ELE) algorithm. The latter algorithm is conceived for STAR-RISs with a large number of elements. Numerical results illustrate that: i) near-field beamforming for STAR-RIS aided MIMO communications significantly improves the achieved weighted sum rate compared with far-field beamforming; ii) the near-field channels facilitated by the STAR-RIS provide enhanced degrees-of-freedom and accessibility for the multi-user MIMO system; and iii) the BCD-PEN algorithm achieves better performance than the BCD-ELE algorithm, while the latter has a significantly lower computational complexity.
Transfer Learning across Several Centuries: Machine and Historian Integrated Method to Decipher Royal Secretary's Diary
Venue: arXiv cs.CL 2023
Year: 2023

A named entity recognition and classification plays the first and foremost important role in capturing semantics in data and anchoring in translation as well as downstream study for history. However, NER in historical text has faced challenges such as scarcity of annotated corpus, multilanguage variety, various noise, and different convention far different from the contemporary language model. This paper introduces Korean historical corpus (Diary of Royal secretary which is named SeungJeongWon) recorded over several centuries and recently added with named entity information as well as phrase markers which historians carefully annotated. We fined-tuned the language model on history corpus, conducted extensive comparative experiments using our language model and pretrained muti-language models. We set up the hypothesis of combination of time and annotation information and tested it based on statistical t test. Our finding shows that phrase markers clearly improve the performance of NER model in predicting unseen entity in documents written far different time period. It also shows that each of phrase marker and corpus-specific trained model does not improve the performance. We discuss the future research directions and practical strategies to decipher the history document.
Video object detection for privacy-preserving patient monitoring in intensive care
Venue: arXiv cs.CV 2023
Year: 2023

Patient monitoring in intensive care units, although assisted by biosensors, needs continuous supervision of staff. To reduce the burden on staff members, IT infrastructures are built to record monitoring data and develop clinical decision support systems. These systems, however, are vulnerable to artifacts (e.g. muscle movement due to ongoing treatment), which are often indistinguishable from real and potentially dangerous signals. Video recordings could facilitate the reliable classification of biosignals using object detection (OD) methods to find sources of unwanted artifacts. Due to privacy restrictions, only blurred videos can be stored, which severely impairs the possibility to detect clinically relevant events such as interventions or changes in patient status with standard OD methods. Hence, new kinds of approaches are necessary that exploit every kind of available information due to the reduced information content of blurred footage and that are at the same time easily implementable within the IT infrastructure of a normal hospital. In this paper, we propose a new method for exploiting information in the temporal succession of video frames. To be efficiently implementable using off-the-shelf object detectors that comply with given hardware constraints, we repurpose the image color channels to account for temporal consistency, leading to an improved detection rate of the object classes. Our method outperforms a standard YOLOv5 baseline model by +1.7% mAP@.5 while also training over ten times faster on our proprietary dataset. We conclude that this approach has shown effectiveness in the preliminary experiments and holds potential for more general video OD in the future.
Towards Operating Wind Turbine Inspections using a LiDAR-equipped UAV
Venue: arXiv cs.RO 2023
Year: 2023

In this study, a novel technique for the autonomous visual inspection of rotating wind turbine rotor blades utilizing an unmanned aerial vehicle (UAV) was developed. This approach addresses the challenges presented by the dynamic environment at hand and the requirement of maintaining a safe distance from the moving rotor blades. The application of UAV-based inspection techniques mitigates these dangers and the expenses associated with traditional wind turbine inspection methods which involve halting normal wind farm operations. Our proposed system incorporates algorithms and sensor technologies. It relies on a light detection and ranging (LiDAR) sensor system, an inertial measurement unit, and a GPS to accurately identify the relative position of the rotating wind turbine with respect to the UAV's own position. Once this position is determined, a non-destructive visual analysis of the rotating rotor blades is performed by generating a suitable trajectory and triggering a camera fitted on a gimbal system as the blades approach. This new technique, built upon the existing research on UAV inspection of rotating wind turbines, has been empirically validated using data collected from real-world wind farm applications. This article contributes to the ongoing trend of enhancing the safety and efficiency of infrastructure inspection. It also presents a good base for future research, with potential applications for other types of infrastructure, such as bridges or power lines.
Mono-to-stereo through parametric stereo generation
Venue: arXiv cs.SD 2023
Year: 2023

Generating a stereophonic presentation from a monophonic audio signal is a challenging open task, especially if the goal is to obtain a realistic spatial imaging with a specific panning of sound elements. In this work, we propose to convert mono to stereo by means of predicting parametric stereo (PS) parameters using both nearest neighbor and deep network approaches. In combination with PS, we also propose to model the task with generative approaches, allowing to synthesize multiple and equally-plausible stereo renditions from the same mono signal. To achieve this, we consider both autoregressive and masked token modelling approaches. We provide evidence that the proposed PS-based models outperform a competitive classical decorrelation baseline and that, within a PS prediction framework, modern generative models outshine equivalent non-generative counterparts. Overall, our work positions both PS and generative modelling as strong and appealing methodologies for mono-to-stereo upmixing. A discussion of the limitations of these approaches is also provided.
PhD Thesis: Exploring the role of (self-)attention in cognitive and computer vision architecture
Venue: arXiv cs.AI 2023
Year: 2023

We investigate the role of attention and memory in complex reasoning tasks. We analyze Transformer-based self-attention as a model and extend it with memory. By studying a synthetic visual reasoning test, we refine the taxonomy of reasoning tasks. Incorporating self-attention with ResNet50, we enhance feature maps using feature-based and spatial attention, achieving efficient solving of challenging visual reasoning tasks. Our findings contribute to understanding the attentional needs of SVRT tasks. Additionally, we propose GAMR, a cognitive architecture combining attention and memory, inspired by active vision theory. GAMR outperforms other architectures in sample efficiency, robustness, and compositionality, and shows zero-shot generalization on new reasoning tasks.
Beyond AUROC & co. for evaluating out-of-distribution detection performance
Venue: arXiv cs.CV 2023
Year: 2023

While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://github.com/glhr/beyond-auroc
Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis
Venue: arXiv cs.LG 2023
Year: 2023

The efficient utilization of wind power by wind turbines relies on the ability of their pitch systems to adjust blade pitch angles in response to varying wind speeds. However, the presence of multiple health conditions in the pitch system due to the long-term wear and tear poses challenges in accurately classifying them, thus increasing the maintenance cost of wind turbines or even damaging them. This paper proposes a novel method based on hard sample mining-enabled supervised contrastive learning (HSMSCL) to address this problem. The proposed method employs cosine similarity to identify hard samples and subsequently, leverages supervised contrastive learning to learn more discriminative representations by constructing hard sample pairs. Furthermore, the hard sample mining framework in the proposed method also constructs hard samples with learned representations to make the training process of the multilayer perceptron (MLP) more challenging and make it a more effective classifier. The proposed approach progressively improves the fault diagnosis model by introducing hard samples in the SCL and MLP phases, thus enhancing its performance in complex multi-class fault diagnosis tasks. To evaluate the effectiveness of the proposed method, two real datasets comprising wind turbine pitch system cog belt fracture data are utilized. The fault diagnosis performance of the proposed method is compared against existing methods, and the results demonstrate its superior performance. The proposed approach exhibits significant improvements in fault diagnosis performance, providing promising prospects for enhancing the reliability and efficiency of wind turbine pitch system fault diagnosis.
Self-supervised novel 2D view synthesis of large-scale scenes with efficient multi-scale voxel carving
Venue: arXiv cs.CV 2023
Year: 2023

The task of generating novel views of real scenes is increasingly important nowadays when AI models become able to create realistic new worlds. In many practical applications, it is important for novel view synthesis methods to stay grounded in the physical world as much as possible, while also being able to imagine it from previously unseen views. While most current methods are developed and tested in virtual environments with small scenes and no errors in pose and depth information, we push the boundaries to the real-world domain of large scales in the new context of UAVs. Our algorithmic contributions are two folds. First, we manage to stay anchored in the real 3D world, by introducing an efficient multi-scale voxel carving method, which is able to accommodate significant noises in pose, depth, and illumination variations, while being able to reconstruct the view of the world from drastically different poses at test time. Second, our final high-resolution output is efficiently self-trained on data automatically generated by the voxel carving module, which gives it the flexibility to adapt efficiently to any scene. We demonstrated the effectiveness of our method on highly complex and large-scale scenes in real environments while outperforming the current state-of-the-art. Our code is publicly available: https://github.com/onorabil/MSVC.
MFDPG: Multi-Factor Authenticated Password Management With Zero Stored Secrets
Venue: arXiv cs.CR 2023
Year: 2023

While password managers are a vital tool for internet security, they can also create a massive central point of failure, as evidenced by several major recent data breaches. For over 20 years, deterministic password generators (DPGs) have been proposed, and largely rejected, as a viable alternative to password management tools. In this paper, we survey 45 existing DPGs to asses the main security, privacy, and usability issues hindering their adoption. We then present a new multi-factor deterministic password generator (MFDPG) design that aims to address these shortcomings. The result not only achieves strong, practical password management with zero credential storage, but also effectively serves as a progressive client-side upgrade of weak password-only websites to strong multi-factor authentication.
A denoised Mean Teacher for domain adaptive point cloud registration
Venue: arXiv cs.CV 2023
Year: 2023

Point cloud-based medical registration promises increased computational efficiency, robustness to intensity shifts, and anonymity preservation but is limited by the inefficacy of unsupervised learning with similarity metrics. Supervised training on synthetic deformations is an alternative but, in turn, suffers from the domain gap to the real domain. In this work, we aim to tackle this gap through domain adaptation. Self-training with the Mean Teacher is an established approach to this problem but is impaired by the inherent noise of the pseudo labels from the teacher. As a remedy, we present a denoised teacher-student paradigm for point cloud registration, comprising two complementary denoising strategies. First, we propose to filter pseudo labels based on the Chamfer distances of teacher and student registrations, thus preventing detrimental supervision by the teacher. Second, we make the teacher dynamically synthesize novel training pairs with noise-free labels by warping its moving inputs with the predicted deformations. Evaluation is performed for inhale-to-exhale registration of lung vessel trees on the public PVT dataset under two domain shifts. Our method surpasses the baseline Mean Teacher by 13.5/62.8%, consistently outperforms diverse competitors, and sets a new state-of-the-art accuracy (TRE=2.31mm). Code is available at https://github.com/multimodallearning/denoised_mt_pcd_reg.
Représentation graphique de la langue des signes française et édition logicielle
Venue: arXiv cs.CL 2023
Year: 2023

Cet article propose une méthode pour définir une forme graphique éditable standardisée pour les langues des signes, ainsi qu'une proposition "AZVD" et un éditeur logiciel associé. Inspirée d'une part par les régularités observées dans les pratiques spontanées de locuteurs pratiquant la schématisation, la démarche tente garantir un système qualifié d'adoptable. Liée d'autre part au modèle formel de représentation AZee, elle vise également à spécifier un système dont toutes les productions ont une lecture déterminée au point où elles sont automatiquement synthétisables par un avatar. -- This paper proposes a definition method for an editable standard graphical form of Sign Language discourse representation. It also puts forward a tentative system "AZVD", and presents an associated software editor. The system is inspired by the regularities observed in spontaneous diagrams produced by some language users, in order to make it as adoptable as possible. Moreover, it is built upon the formal representation model AZee, so that any graphical instance produced by the system determines its own read-out form, to the point that they can be automatically synthesised by an avatar.
INDEXITY: a web-based collaborative tool for medical video annotation
Venue: arXiv cs.CV 2023
Year: 2023

This technical report presents Indexity 1.4.0, a web-based tool designed for medical video annotation in surgical data science projects. We describe the main features available for the management of videos, annotations, ontology and users, as well as the global software architecture.
Automatic Assessment of Divergent Thinking in Chinese Language with TransDis: A Transformer-Based Language Model Approach
Venue: arXiv cs.CL 2023
Year: 2023

Language models have been increasingly popular for automatic creativity assessment, generating semantic distances to objectively measure the quality of creative ideas. However, there is currently a lack of an automatic assessment system for evaluating creative ideas in the Chinese language. To address this gap, we developed TransDis, a scoring system using transformer-based language models, capable of providing valid originality (quality) and flexibility (variety) scores for Alternative Uses Task (AUT) responses in Chinese. Study 1 demonstrated that the latent model-rated originality factor, comprised of three transformer-based models, strongly predicted human originality ratings, and the model-rated flexibility strongly correlated with human flexibility ratings as well. Criterion validity analyses indicated that model-rated originality and flexibility positively correlated to other creativity measures, demonstrating similar validity to human ratings. Study 2 & 3 showed that TransDis effectively distinguished participants instructed to provide creative vs. common uses (Study 2) and participants instructed to generate ideas in a flexible vs. persistent way (Study 3). Our findings suggest that TransDis can be a reliable and low-cost tool for measuring idea originality and flexibility in Chinese language, potentially paving the way for automatic creativity assessment in other languages. We offer an open platform to compute originality and flexibility for AUT responses in Chinese and over 50 other languages (https://osf.io/59jv2/).
MOVES: Movable and Moving LiDAR Scene Segmentation in Label-Free settings using Static Reconstruction
Venue: arXiv cs.RO 2023
Year: 2023

Accurate static structure reconstruction and segmentation of non-stationary objects is of vital importance for autonomous navigation applications. These applications assume a LiDAR scan to consist of only static structures. In the real world however, LiDAR scans consist of non-stationary dynamic structures - moving and movable objects. Current solutions use segmentation information to isolate and remove moving structures from LiDAR scan. This strategy fails in several important use-cases where segmentation information is not available. In such scenarios, moving objects and objects with high uncertainty in their motion i.e. movable objects, may escape detection. This violates the above assumption. We present MOVES, a novel GAN based adversarial model that segments out moving as well as movable objects in the absence of segmentation information. We achieve this by accurately transforming a dynamic LiDAR scan to its corresponding static scan. This is obtained by replacing dynamic objects and corresponding occlusions with static structures which were occluded by dynamic objects. We leverage corresponding static-dynamic LiDAR pairs.