Reviewerly/exHarmony huggingface dataset:
DroNeRF: Real-time Multi-agent Drone Pose Optimization for Computing Neural Radiance Fields

We present a novel optimization algorithm called DroNeRF for the autonomous positioning of monocular camera drones around an object for real-time 3D reconstruction using only a few images. Neural Radiance Fields or NeRF, is a novel view synthesis technique used to generate new views of an object or scene from a set of input images. Using drones in conjunction with NeRF provides a unique and dynamic way to generate novel views of a scene, especially with limited scene capabilities of restricted movements. Our approach focuses on calculating optimized pose for individual drones while solely depending on the object geometry without using any external localization system. The unique camera positioning during the data-capturing phase significantly impacts the quality of the 3D model. To evaluate the quality of our generated novel views, we compute different perceptual metrics like the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure(SSIM). Our work demonstrates the benefit of using an optimal placement of various drones with limited mobility to generate perceptually better results.
Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module

In this paper, we delve into semi-supervised 2D human pose estimation. The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models. (ii) The negative impact of noise pseudo labels on training. Moreover, the labels used for 2D human pose estimation are relatively complex: keypoint category and keypoint position. To solve the problems mentioned above, we propose a semi-supervised 2D human pose estimation framework driven by a position inconsistency pseudo label correction module (SSPCM). We introduce an additional auxiliary teacher and use the pseudo labels generated by the two teacher model in different periods to calculate the inconsistency score and remove outliers. Then, the two teacher models are updated through interactive training, and the student model is updated using the pseudo labels generated by two teachers. To further improve the performance of the student model, we use the semi-supervised Cut-Occlude based on pseudo keypoint perception to generate more hard and effective samples. In addition, we also proposed a new indoor overhead fisheye human keypoint dataset WEPDTOF-Pose. Extensive experiments demonstrate that our method outperforms the previous best semi-supervised 2D human pose estimation method. We will release the code and dataset at https://github.com/hlz0606/SSPCM.
Semantically Consistent Multi-view Representation Learning

In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly concentrate on the learning process in the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel Semantically Consistent Multi-view Representation Learning (SCMRL), which makes efforts to excavate underlying multi-view semantic consensus information and utilize the information to guide the unified feature representation learning. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module, which are elegantly integrated by the contrastive learning strategy to simultaneously align semantic labels of both view-specific feature representations and the learned unified feature representation. In this way, the consensus information in the semantic space can be effectively exploited to constrain the learning process of unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority.
Soft Actor-Critic Algorithm with Truly-satisfied Inequality Constraint

Soft actor-critic (SAC) in reinforcement learning is expected to be one of the next-generation robot control schemes. Its ability to maximize policy entropy would make a robotic controller robust to noise and perturbation, which is useful for real-world robot applications. However, the priority of maximizing the policy entropy is automatically tuned in the current implementation, the rule of which can be interpreted as one for equality constraint, binding the policy entropy into its specified lower bound. The current SAC is therefore no longer maximize the policy entropy, contrary to our expectation. To resolve this issue in SAC, this paper improves its implementation with a learnable state-dependent slack variable for appropriately handling the inequality constraint to maximize the policy entropy by reformulating it as the corresponding equality constraint. The introduced slack variable is optimized by a switching-type loss function that takes into account the dual objectives of satisfying the equality constraint and checking the lower bound. In Mujoco and Pybullet simulators, the modified SAC statistically achieved the higher robustness for adversarial attacks than before while regularizing the norm of action. A real-robot variable impedance task was demonstrated for showing the applicability of the modified SAC to real-world robot control. In particular, the modified SAC maintained adaptive behaviors for physical human-robot interaction, which had no experience at all during training. https://youtu.be/EH3xVtlVaJw
TSANET: Temporal and Scale Alignment for Unsupervised Video Object Segmentation

Unsupervised Video Object Segmentation (UVOS) refers to the challenging task of segmenting the prominent object in videos without manual guidance. In recent works, two approaches for UVOS have been discussed that can be divided into: appearance and appearance-motion-based methods, which have limitations respectively. Appearance-based methods do not consider the motion of the target object due to exploiting the correlation information between randomly paired frames. Appearance-motion-based methods have the limitation that the dependency on optical flow is dominant due to fusing the appearance with motion. In this paper, we propose a novel framework for UVOS that can address the aforementioned limitations of the two approaches in terms of both time and scale. Temporal Alignment Fusion aligns the saliency information of adjacent frames with the target frame to leverage the information of adjacent frames. Scale Alignment Decoder predicts the target object mask by aggregating multi-scale feature maps via continuous mapping with implicit neural representation. We present experimental results on public benchmark datasets, DAVIS 2016 and FBMS, which demonstrate the effectiveness of our method. Furthermore, we outperform the state-of-the-art methods on DAVIS 2016.
The Lie-Group Bayesian Learning Rule

The Bayesian Learning Rule provides a framework for generic algorithm design but can be difficult to use for three reasons. First, it requires a specific parameterization of exponential family. Second, it uses gradients which can be difficult to compute. Third, its update may not always stay on the manifold. We address these difficulties by proposing an extension based on Lie-groups where posteriors are parametrized through transformations of an arbitrary base distribution and updated via the group's exponential map. This simplifies all three difficulties for many cases, providing flexible parametrizations through group's action, simple gradient computation through reparameterization, and updates that always stay on the manifold. We use the new learning rule to derive a new algorithm for deep learning with desirable biologically-plausible attributes to learn sparse features. Our work opens a new frontier for the design of new algorithms by exploiting Lie-group structures.
Nonlinear Kalman Filtering with Reparametrization Gradients

We introduce a novel nonlinear Kalman filter that utilizes reparametrization gradients. The widely used parametric approximation is based on a jointly Gaussian assumption of the state-space model, which is in turn equivalent to minimizing an approximation to the Kullback-Leibler divergence. It is possible to obtain better approximations using the alpha divergence, but the resulting problem is substantially more complex. In this paper, we introduce an alternate formulation based on an energy function, which can be optimized instead of the alpha divergence. The optimization can be carried out using reparametrization gradients, a technique that has recently been utilized in a number of deep learning models.
MenuCraft: Interactive Menu System Design with Large Language Models

Menu system design is a challenging task involving many design options and various human factors. For example, one crucial factor that designers need to consider is the semantic and systematic relation of menu commands. However, capturing these relations can be challenging due to limited available resources. With the advancement of neural language models, large language models can utilize their vast pre-existing knowledge in designing and refining menu systems. In this paper, we propose MenuCraft, an AI-assisted designer for menu design that enables collaboration between the designer and a dialogue system to design menus. MenuCraft offers an interactive language-based menu design tool that simplifies the menu design process and enables easy customization of design options. MenuCraft supports a variety of interactions through dialog that allows performing zero/few-shot learning.
Exploiting the Textual Potential from Vision-Language Pre-training for Text-based Person Search

Text-based Person Search (TPS), is targeted on retrieving pedestrians to match text descriptions instead of query images. Recent Vision-Language Pre-training (VLP) models can bring transferable knowledge to downstream TPS tasks, resulting in more efficient performance gains. However, existing TPS methods improved by VLP only utilize pre-trained visual encoders, neglecting the corresponding textual representation and breaking the significant modality alignment learned from large-scale pre-training. In this paper, we explore the full utilization of textual potential from VLP in TPS tasks. We build on the proposed VLP-TPS baseline model, which is the first TPS model with both pre-trained modalities. We propose the Multi-Integrity Description Constraints (MIDC) to enhance the robustness of the textual modality by incorporating different components of fine-grained corpus during training. Inspired by the prompt approach for zero-shot classification with VLP models, we propose the Dynamic Attribute Prompt (DAP) to provide a unified corpus of fine-grained attributes as language hints for the image modality. Extensive experiments show that our proposed TPS framework achieves state-of-the-art performance, exceeding the previous best method by a margin.
Enhancing Low-resolution Face Recognition with Feature Similarity Knowledge Distillation

In this study, we introduce a feature knowledge distillation framework to improve low-resolution (LR) face recognition performance using knowledge obtained from high-resolution (HR) images. The proposed framework transfers informative features from an HR-trained network to an LR-trained network by reducing the distance between them. A cosine similarity measure was employed as a distance metric to effectively align the HR and LR features. This approach differs from conventional knowledge distillation frameworks, which use the L_p distance metrics and offer the advantage of converging well when reducing the distance between features of different resolutions. Our framework achieved a 3% improvement over the previous state-of-the-art method on the AgeDB-30 benchmark without bells and whistles, while maintaining a strong performance on HR images. The effectiveness of cosine similarity as a distance metric was validated through statistical analysis, making our approach a promising solution for real-world applications in which LR images are frequently encountered. The code and pretrained models are publicly available on https://github.com/gist-ailab/feature-similarity-KD.
B-Treaps Revised: Write Efficient Randomized Block Search Trees with High Load

Uniquely represented data structures represent each logical state with a unique storage state. We study the problem of maintaining a dynamic set of $n$ keys from a totally ordered universe in this context. We introduce a two-layer data structure called $(\alpha,\varepsilon)$-Randomized Block Search Tree (RBST) that is uniquely represented and suitable for external memory. Though RBSTs naturally generalize the well-known binary Treaps, several new ideas are needed to analyze the {\em expected} search, update, and storage, efficiency in terms of block-reads, block-writes, and blocks stored. We prove that searches have $O(\varepsilon^{-1} + \log_\alpha n)$ block-reads, that $(\alpha, \varepsilon)$-RBSTs have an asymptotic load-factor of at least $(1-\varepsilon)$ for every $\varepsilon \in (0,1/2]$, and that dynamic updates perform $O(\varepsilon^{-1} + \log_\alpha(n)/\alpha)$ block-writes, i.e. $O(1/\varepsilon)$ writes if $\alpha=\Omega(\frac{\log n}{\log \log n} )$. Thus $(\alpha, \varepsilon)$-RBSTs provide improved search, storage-, and write-efficiency bounds in regard to the known, uniquely represented B-Treap [Golovin; ICALP'09].
Towards Trust of Explainable AI in Thyroid Nodule Diagnosis

The ability to explain the prediction of deep learning models to end-users is an important feature to leverage the power of artificial intelligence (AI) for the medical decision-making process, which is usually considered non-transparent and challenging to comprehend. In this paper, we apply state-of-the-art eXplainable artificial intelligence (XAI) methods to explain the prediction of the black-box AI models in the thyroid nodule diagnosis application. We propose new statistic-based XAI methods, namely Kernel Density Estimation and Density map, to explain the case of no nodule detected. XAI methods' performances are considered under a qualitative and quantitative comparison as feedback to improve the data quality and the model performance. Finally, we survey to assess doctors' and patients' trust in XAI explanations of the model's decisions on thyroid nodule images.
Video-P2P: Video Editing with Cross-attention Control

This paper presents Video-P2P, a novel framework for real-world video editing with cross-attention control. While attention control has proven effective for image editing with pre-trained image generation models, there are currently no large-scale video generation models publicly available. Video-P2P addresses this limitation by adapting an image generation diffusion model to complete various video editing tasks. Specifically, we propose to first tune a Text-to-Set (T2S) model to complete an approximate inversion and then optimize a shared unconditional embedding to achieve accurate video inversion with a small memory cost. For attention control, we introduce a novel decoupled-guidance strategy, which uses different guidance strategies for the source and target prompts. The optimized unconditional embedding for the source prompt improves reconstruction ability, while an initialized unconditional embedding for the target prompt enhances editability. Incorporating the attention maps of these two branches enables detailed editing. These technical designs enable various text-driven editing applications, including word swap, prompt refinement, and attention re-weighting. Video-P2P works well on real-world videos for generating new characters while optimally preserving their original poses and scenes. It significantly outperforms previous approaches.
Virtual Team Building in an Intelligent Collaborative Learning Environment

Aim/Purpose: The main purpose is to study the experience of using virtual team building as a means of forming educational and research teams in the context of the development of online education and its effect among students and teachers of higher educational institutions. Background: Methods ensuring effective engagement of students in learning are critical to the success of online education. The most obvious problems in higher educational institutions are procrastination, academic dishonesty due to easy access to electronic resources, decreased attendance, and insufficient interaction between teachers and students. Methodology: The research methodology is based on an empirical approach, which is a research survey using a questionnaire to collect data based on closed-ended questions. For quantitative analysis, the independent sample t-test was used. The survey was conducted among students and teachers of two educational institutions in the Russian Federation. Contribution: This study is of practical and scientific importance as it can contribute to the introduction of virtual team building in the modern education system. Findings: Based on the analysis of the data obtained, it can be concluded that students and teachers approximately equally assess the impact of team building on the ability to get to know each other better, improve communication skills, and psycho-emotional intimacy. Despite the need and sometimes no alternative to virtual team building (for example, during a pandemic), half of the sample of students (50.8%) agreed that team building was more effective in an offline environment while 64.3% of teachers believe that the effectiveness could have been higher in the offline environment. The respondents assessed the positive effect of team building on their interest and motivation to study or work. Recommendations for Practitioners: These findings can contribute to a broader and faster implementation of virtual team-building practices in the education system of the Russian Federation and other countries of the world. The results of this study can be applied by higher educational institutions that are interested in increasing team cohesion, interest, and motivation to study or work, as well as the creation of closer and trusting relationships, and an atmosphere of psycho-emotional safety. Recommendation for Researchers: This topic requires more observations to verify the influence of student personality on the effectiveness of virtual team building in intelligent collaborative learning environments. Impact on Society: The study highlights the importance of communication between the student and the teacher, as well as between students, as psycho-emotional well-being in the micro-society results in a better academic performance. Future Research: Further research can be aimed at studying the difference in the effectiveness of team building in online and offline learning environments, as well as the impact of team building on the teaching staff.
Target Tracking Under Large-Scale Variation Based on UAV Platform

Target tracking based on the UAV platform is difficult to avoid the problem of large-scale variation. Due to the large change in the relative position between the UAV and the target, the size of the target in the video sequence will also change accordingly, resulting in poor tracking accuracy and low success rate. In order to solve the challenge of large-scale variation, based on the Siamese series of target tracking algorithms, the author proposes two improved methods to optimize the tracking frame and update the template. The optimization process is supplemented by updating the observation model to make it better adapt to the large-scale changes in the relative movement of the UAV and the target. Through the improvement of the target tracking algorithm based on the Siamese network, the target under large-scale transformation can be effectively tracked.
An Accurate and Robust Comparison Method of the Intelligence for Two Unmanned Swarms Based on the Improved CRITIC and Hypothesis Test

This paper proposes an accurate and robust comparison method of the intelligence for two evaluated UMSs specialized in performing the same specific task. In the proposed method, an improved criteria importance through intercriteria correlation (CRITIC) method was adopted to assign the objective weights of intelligence evaluation indexes established for the same specific task, and the hypothesis test method was adopted to obtain the central intelligence scores for two evaluated UMSs according to their respective accidental intelligence scores obtained on two similar sets of tasks and further judge whether their central intelligence scores are statistically equal. The obtained evaluation result can also be used for determining whether these two evaluated UMSs belong to the same intelligence level. To verify the effectiveness of the proposed method, this paper presents a case study comparing the intelligence for two simulated unmanned ground vehicle (UGV) swarms specialized in performing the same specific pursuit task. The result of the case study shows that the central intelligence score for the simulated UGV swarm A are larger than that for the simulated UGV swarm B from the statistical point of view, and therefore they cannot be classified into the same intelligence level.
Data-Art-Science: Art Trhought Data Visualization

The use of scientific data by artists can tell a story, describe landscapes, and create worlds. Regarding the complexity of the dynamics that is an environmental crisis, this article proposes the investigation of actions that connect art, science, and data visualization with a view to mitigating climate change. From these questions, the art collective Kodos investigates Data visualization in intersection with Art and Science. This article proposes to put forward the work “this tea is the result of a data visualization” to observe the relationships with technologies that deviate from an anthropocentric vision and expand their knowledge to a new operative way of acting aesthetically, and thus propose alternative and speculative models of thinking about data visualization operations.
Tempo, information rate, and communicative efficiency in L2 speech

This presentation seeks links between speech tempo, information transmission rate and communicative efficiency in conversations involving L2 participants.First, we show that L2 speech tempo is influenced by both "state" and "trait" characteristics.Specifically, while L2 speech is invariably slower than L1 speech (L1 vs. L2 "state"), within bilingual individuals L1 rate is a significant predictor of L2 rate (individual "trait").Next, we show that the slower rate of L2 speech (fewer syllables/second) is compounded by lower L2 information density (more syllables/text) yielding a very low L2 information transmission rate (fewer bits of information transmitted/second).Finally, we show variation in conversation-based cooperative task completion time depending on degree of language background overlap between task participants.Together, these studies provide a scaffold for eventually linking acoustic level temporal variation to discourse level communicative efficiency in L2 speech communication.
Using entropy to determine vowel spaces

Vowel spaces are often illustrated with the first and second formants plotted against each other on X-Y plots.The numerical values of vowel F1, F2, and F3 were evaluated using Pearson correlations and results often show inconsistencies, indicating that some F1-F2 plots might not provide the best vowel space depiction.The same formant values were examined using conditional entropy methodology and determinations were made which formants should be the independent X variable, and which formants should be the dependent Y variable for plotting.This research describes the examination of relationships between formants to determine optimal, more accurate vowel space plots.