Reviewerly/exHarmony huggingface dataset:
Research on Teaching Mode of Art Design Specialty based on VR technology

As a new teaching model, VR virtual technology is gradually integrating into traditional teaching. VR virtual technology can build a simulation learning environment, so that students have a comprehensive and systematic understanding of the courses of art design, and cultivate students' creative ability and imagination. This paper expounds the basic connotation of VR technology, analyzes the advantages of VR technology in the teaching of art design, proposes to integrate VR virtual reality technology into the teaching mode of classroom teaching, and discusses the necessity of VR virtual reality technology, so as to make the technology widely used in the teaching of art design major.
E-learning from History to Drawbacks---A Brief Review

This paper presents a brief review of e-learning, focusing on its development throughout history and the drawbacks of e-learning on language learning. The evolution of e-learning is examined by dividing it into two main periods: pre-Pandemic-19 and post-Pandemic-19. The emergence of the COVID-19 pandemic forced the education sector to heavily rely on e-learning for teaching and learning activities. Consequently, the utilization of e-learning expanded globally, reaching every corner of the world. While e-learning offers significant benefits in education, it also poses certain drawbacks. This paper briefly discusses these drawbacks. In order to maximize the potential of e-learning in education, teachers must proactively address these challenges. While there is no one-size-fits-all solution, teachers or instructors should adopt a flexible and responsive approach, regularly soliciting feedback from students and adjusting teaching strategies accordingly.
Zeroth- and first-order difference discrimination for unsupervised domain adaptation

Abstract Unsupervised domain adaptation transfers empirical knowledge from a label-rich source domain to a fully unlabeled target domain with a different distribution. A core idea of many existing approaches is to reduce the distribution divergence between domains. However, they focused only on part of the discrimination, which can be categorized into optimizing the following four objectives: reducing the intraclass distance between domains, enlarging the interclass distances between domains, reducing the intraclass distances within domains, and enlarging the interclass distances within domains. Moreover, because few methods consider multiple types of objectives, the consistency of data representations produced by different types of objectives has not yet been studied. In this paper, to address the above issues, we propose a zeroth- and first-order difference discrimination (ZFOD) approach for unsupervised domain adaptation. It first optimizes the above four objectives simultaneously. To improve the discrimination consistency of the data across the two domains, we propose a first-order difference constraint to align the interclass differences across domains. Because the proposed method needs pseudolabels for the target domain, we adopt a recent pseudolabel generation method to alleviate the negative impact of imprecise pseudolabels. We conducted an extensive comparison with nine representative conventional methods and seven remarkable deep learning-based methods on four benchmark datasets. Experimental results demonstrate that the proposed method, as a conventional approach, not only significantly outperforms the nine conventional comparison methods but is also competitive with the seven deep learning-based comparison methods. In particular, our method achieves an accuracy of 93.4% on the Office+Caltech10 dataset, which outperforms the other comparison methods. An ablation study further demonstrates the effectiveness of the proposed constraint in aligning the objectives.
An efficient deep neural network channel state estimator for OFDM wireless systems

Abstract Channel state estimation (CSE) is essential for orthogonal frequency division multiplexing (OFDM) wireless systems to deal with multipath channel fading. To attain a high data rate with the use of OFDM technology, an efficient CSE and accurate signal detection are required. The use of machine learning (ML) to improve channel estimates has attracted a lot of attention lately. This is because ML techniques are more adaptable than traditional model-based estimation techniques. The present study proposes a receiver for low-spectrum usage in OFDM wireless systems on Rayleigh fading channels using deep learning (DL) long short-term memory (LSTM). Before online deployment and data retrieval, the proposed DL LSTM estimator gathers channel state information from transmit/receive pairs using offline training. Based on the simulation results of a comparative study, the proposed estimator outperforms conventional channel estimation approaches like minimum mean square error and least squares in noisy and interfering wireless channels. Furthermore, the proposed estimator outperforms the DL bidirectional LSTM (BiLSTM)-based CSE model. In particular, the proposed CSE performs better than other examined estimators with a reduced number of pilots, no cycle prefixes, and no prior knowledge of channel statistics. Because the proposed estimator relies on a DL neural network approach, it holds promise for OFDM wireless communication systems.
Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi–Analytic Hierarchy Process

Existing methods for scoring student presentations predominantly rely on computer-based implementations and do not incorporate a robotic multi-classification model. This limitation can result in potential misclassification issues as these approaches lack active feature learning capabilities due to fixed camera positions. Moreover, these scoring methods often solely focus on facial expressions and neglect other crucial factors, such as eye contact, hand gestures and body movements, thereby leading to potential biases or inaccuracies in scoring. To address these limitations, this study introduces Robotics-based Presentation Skill Scoring (RPSS), which employs a multi-model analysis. RPSS captures and analyses four key presentation parameters in real time, namely facial expressions, eye contact, hand gestures and body movements, and applies the fuzzy Delphi method for criteria selection and the analytic hierarchy process for weighting, thereby enabling decision makers or managers to assign varying weights to each criterion based on its relative importance. RPSS identifies five academic facial expressions and evaluates eye contact to achieve a comprehensive assessment and enhance its scoring accuracy. Specific sub-models are employed for each presentation parameter, namely EfficientNet for facial emotions, DeepEC for eye contact and an integrated Kalman and heuristic approach for hand and body movements. The scores are determined based on predefined rules. RPSS is implemented on a robot, and the results highlight its practical applicability. Each sub-model is rigorously evaluated offline and compared against benchmarks for selection. Real-world evaluations are also conducted by incorporating a novel active learning approach to improve performance by leveraging the robot's mobility. In a comparative evaluation with human tutors, RPSS achieves a remarkable average agreement of 99%, showcasing its effectiveness in assessing students' presentation skills.
A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis

Blood cancer occurs due to changes in white blood cells (WBCs). These changes are known as leukemia. Leukemia occurs mostly in children and affects their tissues or plasma. However, it could occur in adults. This disease becomes fatal and causes death if it is discovered and diagnosed late. In addition, leukemia can occur from genetic mutations. Therefore, there is a need to detect it early to save a patient’s life. Recently, researchers have developed various methods to detect leukemia using different technologies. Deep learning approaches (DLAs) have been widely utilized because of their high accuracy. However, some of these methods are time-consuming and costly. Thus, a need for a practical solution with low cost and higher accuracy is required. This article proposes a novel segmentation and classification framework model to discover and categorize leukemia using a deep learning structure. The proposed system encompasses two main parts, which are a deep learning technology to perform segmentation and characteristic extraction and classification on the segmented section. A new UNET architecture is developed to provide the segmentation and feature extraction processes. Various experiments were performed on four datasets to evaluate the model using numerous performance factors, including precision, recall, F-score, and Dice Similarity Coefficient (DSC). It achieved an average 97.82% accuracy for segmentation and categorization. In addition, 98.64% was achieved for F-score. The obtained results indicate that the presented method is a powerful technique for discovering leukemia and categorizing it into suitable groups. Furthermore, the model outperforms some of the implemented methods. The proposed system can assist healthcare providers in their services.
Commonsense Knowledge-Driven Joint Reasoning Approach for Object Retrieval in Virtual Reality

National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI), China Retrieving out-of-reach objects is a crucial task in virtual reality (VR). One of the most commonly used approaches for this task is the gesture-based approach, which allows for bare-hand, eyes-free, and direct retrieval. However, previous work has primarily focused on assigned gesture design, neglecting the context. This can make it challenging to accurately retrieve an object from a large number of objects due to the one-to-one mapping metaphor, limitations of finger poses, and memory burdens. There is a general consensus that objects and contexts are related, which suggests that the object expected to be retrieved is related to the context, including the scene and the objects with which users interact. As such, we propose a commonsense knowledge-driven joint reasoning approach for object retrieval, where human grasping gestures and context are modeled using an And-Or graph (AOG). This approach enables users to accurately retrieve objects from a large number of candidate objects by using natural grasping gestures based on their experience of grasping physical objects. Experimental results demonstrate that our proposed approach improves retrieval accuracy. We also propose an object retrieval system based on the proposed approach. Two user studies show that our system enables efficient object retrieval in virtual environments (VEs).
Enhancing Diffusion Models with 3D Perspective Geometry Constraints

While perspective is a well-studied topic in art, it is generally taken for granted in images. However, for the recent wave of high-quality image synthesis methods such as latent diffusion models, perspective accuracy is not an explicit requirement. Since these methods are capable of outputting a wide gamut of possible images, it is difficult for these synthesized images to adhere to the principles of linear perspective. We introduce a novel geometric constraint in the training process of generative models to enforce perspective accuracy. We show that outputs of models trained with this constraint both appear more realistic and improve performance of downstream models trained on generated images. Subjective human trials show that images generated with latent diffusion models trained with our constraint are preferred over images from the Stable Diffusion V2 model 70% of the time. SOTA monocular depth estimation models such as DPT and PixelFormer, fine-tuned on our images, outperform the original models trained on real images by up to 7.03% in RMSE and 19.3% in SqRel on the KITTI test set for zero-shot transfer.
Decaf: Monocular Deformation Capture for Face and Hand Interactions

Existing methods for 3D tracking from monocular RGB videos predominantly consider articulated and rigid objects ( e.g. , two hands or humans interacting with rigid environments). Modelling dense non-rigid object deformations in this setting ( e.g. when hands are interacting with a face), remained largely unaddressed so far, although such effects can improve the realism of the downstream applications such as AR/VR, 3D virtual avatar communications, and character animations. This is due to the severe ill-posedness of the monocular view setting and the associated challenges ( e.g. , in acquiring a dataset for training and evaluation or obtaining the reasonable non-uniform stiffness of the deformable object). While it is possible to naïvely track multiple non-rigid objects independently using 3D templates or parametric 3D models, such an approach would suffer from multiple artefacts in the resulting 3D estimates such as depth ambiguity, unnatural intra-object collisions and missing or implausible deformations. Hence, this paper introduces the first method that addresses the fundamental challenges depicted above and that allows tracking human hands interacting with human faces in 3D from single monocular RGB videos. We model hands as articulated objects inducing non-rigid face deformations during an active interaction. Our method relies on a new hand-face motion and interaction capture dataset with realistic face deformations acquired with a markerless multi-view camera system. As a pivotal step in its creation, we process the reconstructed raw 3D shapes with position-based dynamics and an approach for non-uniform stiffness estimation of the head tissues, which results in plausible annotations of the surface deformations, hand-face contact regions and head-hand positions. At the core of our neural approach are a variational auto-encoder supplying the hand-face depth prior and modules that guide the 3D tracking by estimating the contacts and the deformations. Our final 3D hand and face reconstructions are realistic and more plausible compared to several baselines applicable in our setting, both quantitatively and qualitatively. https://vcai.mpi-inf.mpg.de/projects/Decaf
SAILOR: Synergizing Radiance and Occupancy Fields for Live Human Performance Capture

Immersive user experiences in live VR/AR performances require a fast and accurate free-view rendering of the performers. Existing methods are mainly based on Pixel-aligned Implicit Functions (PIFu) or Neural Radiance Fields (NeRF). However, while PIFu-based methods usually fail to produce photorealistic view-dependent textures, NeRF-based methods typically lack local geometry accuracy and are computationally heavy ( e.g. , dense sampling of 3D points, additional fine-tuning, or pose estimation). In this work, we propose a novel generalizable method, named SAILOR, to create high-quality human free-view videos from very sparse RGBD live streams. To produce view-dependent textures while preserving locally accurate geometry, we integrate PIFu and NeRF such that they work synergistically by conditioning the PIFu on depth and then rendering view-dependent textures through NeRF. Specifically, we propose a novel network, named SRONet, for this hybrid representation. SRONet can handle unseen performers without fine-tuning. Besides, a neural blending-based ray interpolation approach, a tree-based voxel-denoising scheme, and a parallel computing pipeline are incorporated to reconstruct and render live free-view videos at 10 fps on average. To evaluate the rendering performance, we construct a real-captured RGBD benchmark from 40 performers. Experimental results show that SAILOR outperforms existing human reconstruction and performance capture methods.
Dual-Stage Attribute Embedding and Modality Consistency Learning-Based Visible–Infrared Person Re-Identification

Visible–infrared person re-identification (VI-ReID) is an emerging technology for realizing all-weather smart surveillance systems. To address the problem of pedestrian discriminative information being difficult to obtain and easy to lose, as well as the wide modality difference in the VI-ReID task, in this paper we propose a two-stage attribute embedding and modality consistency learning-based VI-ReID method. First, the attribute information embedding module introduces the fine-grained pedestrian information in the attribute label into the transformer backbone, enabling the backbone to extract identity-discriminative pedestrian features. After obtaining the pedestrian features, the attribute embedding enhancement module is utilized to realize the second-stage attribute information embedding, which reduces the adverse effect of losing the person discriminative information due to the deepening of network. Finally, the modality consistency learning loss is designed for constraining the network to mine the consistency information between two modalities in order to reduce the impact of modality difference on the recognition results. The results show that our method reaches 74.57% mAP on the SYSU-MM01 dataset in All Search mode and 87.02% mAP on the RegDB dataset in IR-to-VIS mode, with a performance improvement of 6.00% and 2.56%, respectively, proving that our proposed method is able to reach optimal performance compared to existing state-of-the-art methods.
Context-aware fidelity estimation

We present context-aware fidelity estimation (CAFE), a framework for benchmarking quantum operations that offers several practical advantages over existing methods such as randomized benchmarking (RB) and cross-entropy benchmarking. In CAFE, a gate or a subcircuit from some target experiment is repeated $n$ times before being measured. By using a subcircuit, we account for effects from the spatial and temporal circuit context. Since coherent errors accumulate quadratically while incoherent errors grow linearly, we can separate them by fitting the measured fidelity as a function of $n$. One can additionally interleave the subcircuit with dynamical decoupling sequences to remove certain coherent error sources from the characterization when desired. We have used CAFE to experimentally validate our single- and two-qubit unitary characterizations by measuring fidelity against estimated unitaries. In numerical simulations, we find that CAFE produces fidelity estimates at least as accurate as interleaved RB while using significantly fewer resources. We also introduce a compact formulation for preparing an arbitrary two-qubit state with a single entangling operation and use it to present a concrete example using CAFE to study controlled-$Z$ gates in parallel on a Sycamore processor.
Development and validation of BVB-UIE: an underwater image-enhancement model with two-layer deep neural network

Abstract Due to the absorption and scattering of light in water, underwater visual visibility is poor, and underwater images usually suffer from color distortion and feature loss, which poses a great challenge for underwater vision tasks. To handle these issues, we present an underwater image enhancement (UIE) method. A Gaussian pyramid is constructed for the degraded underwater images based on an improved visual saliency model, with the characteristics of luminance, orientation, texture, and color. By combining channel and spatial attention mechanisms, a deep asymmetric feature enhancement network is built to preserve the high-dimensional features of the image. In addition, a polynomial loss function is used for the depth hopping supervising constraints during the enhancement process, and the gating signals are employed to control the importance of the features at different spatial locations. These innovations maximize the efficiency of the feature extraction while simplifying the network complexity. Experiments on an open benchmark dataset for UIE show that our method effectively eliminates the color bias and contrast distortion problems while preserving the complex image details compared to the existing UIE algorithms. Objective metrics show a significant improvement in the algorithm, with a 15% increment in the peak signal-to-noise ratio metric compared to the closest competing algorithm.
Let us Break the Time Barrier—Anytime Computing

Thanks to the advances in communication technologies, the costs of connecting to our beloved ones over distance have become negligible. The provision of bandwidth, access to connectivity, and integration of cameras into almost any mobile device allows us to perceive what is happening with our communication counterparts visually. The emergence of telepresence robots even provides us with a remote presence in distant locations. Virtual and augmented environments blend virtual information worlds and physical settings into each other. All these developments together allow better communication and collaboration over distance. During the COVID-19 pandemic, an accompanying cultural and organizational transformation has also been triggered, allowing us to embrace and leverage these technological developments and thus enabling a new way of working. Employees fulfilling their duties from their homes has become the new normal. The traditional office with assigned seating is getting replaced by coworking spaces used for specific occasions, rather than being used on a daily basis. Business travel is losing importance due to in-person gatherings changing to online meetings. In a nutshell, the constraints of space determining presence at a specific location are mitigated by communication technologies that are bringing people together virtually over distance.
Awareness of Senior High School Students on Digital Literacy Skills: A Qualitative Study

The current study investigates senior high school (SHS) students' awareness of the importance of digital literacy in times of uncertainty. Studies on digital literacy in DepEd Rizal have not been given sufficient attention. Hence, there is a dearth of studies showing whether students are fully aware of the digital literacy skills needed in distance learning. Consequently, an online qualitative survey gathered data from fifty (50) senior high school students presently enrolled in the different strands and tracks in the division. To better understand students’ awareness of the needed skills, thematic analysis of Braun and Clarke (2006) was utilized. Students' qualitative responses were interpreted into several emerging themes. Results revealed that some students are cognizant of the digital skills they need to possess, such as critical thinking, online safety skills, and communication. This study hopes to provide implications to students, teachers, and curriculum developers.