Reviewerly/exHarmony huggingface dataset:
An obstacle avoidance strategy for complex obstacles based on artificial potential field method

Abstract When there are obstacles around the target point, the mobile robot cannot reach the target using the traditional artificial potential field (APF). Besides, the traditional APF is prone to local oscillation in complex terrain such as three‐point collinear or semiclosed obstacles. Aiming at solving the defects of traditional APF, a novel improved APF algorithm named back virtual obstacle setting strategy‐APF has been proposed in this paper. There are two main advantages of the proposed method. First, by redefining the gravitational function as a logarithmic function, the proposed method can make the mobile robot reach the target point when there are obstacles around the target. Second, the proposed method can avoid falling into local oscillation for both three‐point collinear and semiclosed obstacles. Compare with APF and other improved APF, the feasibility of the algorithm is proved through software simulation and practical application.
Perceptual Quality Assessment for Digital Human Heads

Digital humans are attracting more and more research interest during the last decade, the generation, representation, rendering, and animation of which have been put into large amounts of effort. However, the quality assessment of digital humans has fallen behind. Therefore, to tackle the challenge of digital human quality assessment issues, we propose the first large-scale quality assessment database for three-dimensional (3D) scanned digital human heads (DHHs). The constructed database consists of 55 reference DHHs and 1,540 distorted DHHs along with the subjective perceptual ratings. Then, a simple yet effective full-reference (FR) projection-based method is proposed to evaluate the visual quality of DHHs. The pretrained Swin Transformer tiny is employed for hierarchical feature extraction and the multi-head attention module is utilized for feature fusion. The experimental results reveal that the proposed method exhibits state-of-the-art performance among the mainstream FR metrics. The database is released at https://github.com/zzc-1998/DHHQA.
Phase-based vibration imaging for structural dynamics applications: Marker-free full-field displacement measurements with confidence measures

Inspired by the concept of thermal imaging, we develop a phase-based vibration imaging technique using marker-free full-field displacement measurements, which aims to determine vibrational energy distribution and possibly vibration parameters as the thermal imaging determining thermal energy distribution using colormap. In this study, accurate full-field measurements are conducted at the sub-pixel level by performing a full-field phase-based optical flow with three novel progresses. First, an image-preprocessing Gaussian smoothing gradient is applied to reduce the nonlinear phase caused by homogenous patterns and image noise. Second, two confidence measures (phase nonlinearity and filter response) are adopted to obtain reliable displacement measurements estimated from the multi-scale and multi-direction phases. Third, the combination of vector average and the intersection of constraints ensure the accuracy of integrating every pixel’s displacement components. The vibrations can then be imaged by analyzing the acquired full-field displacements using techniques such as mean absolute deviation and frequency analyses. The capability of the proposed technique is demonstrated through numerical experiments using artificial patterns. After then, experimental validation is performed, which includes full-field operational deflection shape extraction on a 5-story structure and vibration imaging of an in-service hydrogen charging station, further proving the effectiveness of the proposed vibration imaging technique.
Blockchain‐based authentication and secure communication in IoT networks

Abstract The application of IoT covers various domains, including smart cities, smart homes, e‐health, autonomous vehicles and so forth. These are powered by IoT networks which consist of a collection of heterogeneous devices. These devices collect and analyze personal data and sometimes make decisions for us. Since IoTs are involved in the decision‐making process, the authenticity and privacy of such networks are critical. In this article, authenticated devices configuration protocol (ADCP) is proposed to handle the authentication and construct a secure overlay network in the existing IoT networks; authenticated device transmission protocol is also proposed to secure the communication within the overlay network. Besides the authentication, ADCP can also mitigate the zero‐day attack and achieve zero the zero‐day attack can also be mitigated and achieved 0 round trip time key exchange. These protocols utilize a distributed blockchain database to store the authentication records optimized for data integrity. These properties guarantee the integrity of the authentication records. Both protocols are compatible with the existing communication protocols and do not require reprogramming the current software. The formal analysis of the proposed scheme has proven its resilience against various possible attacks. In addition, a stochastic threat model is considered and numerical results give solid evidence of security enhancement.
Comparative analysis of the distance learning implementation in Ukrainian system of higher education during the COVID-19 pandemic and martial law

The pandemic has globally changed the landscape of the educational system in most countries of the world, including Ukraine. Their educational systems have faced massive challenges in distance learning implementation. However, in addition to the destructive impact of the pandemic, Ukraine has a more serious problem in the form of a full-scale invasion by the Russian army. In the conditions of the active phase of the conflict, it is extremely problematic to conduct full-time classes, especially for higher education establishments. Therefore, distance learning technology is of particular importance for Ukraine. This research investigates the situation of distance learning implementation during the COVID-19 pandemic and martial law in Ukrainian system of higher education. Questions regarding the features of the distance learning development in Ukrainian universities and comparative analyses of key aspects of the distance learning implementation within two destructive phenomena (pandemic and war), are raised. The results clarify the historical background of the distance learning development in the Ukrainian system of higher education, general trends and the main problematic aspects of its implementation. The study makes it possible to more comprehensively understand this issue and find the most effective strategy for solving emerging problems.
DOES SUB‐SAHARAN AFRICA'S RAPID POPULATION GROWTH HAVE POSITIVE OR NEGATIVE EFFECTS ON AGGREGATE DOMESTIC INVESTMENT?

This study examines the effect of population growth on aggregate domestic investments in 45 Sub‐Saharan African (SSA) countries over the period 2000–2020. It applies the Quantile Method of Moments with fixed effects (i.e., MM‐QR), which has the ability to identify both negative and positive effects while controlling for trade, HIV/AIDS prevalence, and economic growth. The findings show that SSA's rapid population growth has a positive and statistically significant effect on aggregate domestic investments. Findings from this study show that an increase in population growth in the 10th to 60th quantiles is associated with an increase of domestic investment in SSA. This suggests that, as the population grows in SSA, it generates demand for domestic investments such as healthcare services, education, and other social services. Our findings also show that trade is positive and significant across all quantiles (10th to 90th). We find evidence that disease risks, such as the prevalence of HIV/AIDS, slowed the increase in domestic investment in the region across all quantiles (10th to 90th). We conclude by arguing that, since SSA's population growth will double in the near future, the region is bound to become world's next investment hub. In order to enable long‐term domestic investments in the region, future policy options should take the challenges of population growth into account.
Parallel or Cross? Effects of Two Collaborative Modes on Augmented Reality Co-located Operations

Augmented reality (AR) can bring a new interactive experience to the collaboration between users. When users are in the same place, there are two modes of joint operation for the same object: parallel-work (PW) and cross-work (CW). PW means two users perform their tasks, while CW means assisting each other. To investigate the difference that collaboration using PW and CW in an AR environment brings to users, we developed a two-person local collaboration system, LoCol. We designed and conducted user experiments by selecting the tasks of adjusting the virtual model of the assembly and adding missing boundaries in the model. The results showed that CW led to a higher sense of social coexistence while reducing workload. In terms of task completion time and accuracy, CW and PW each had advantages. We found that users generally want to reduce unnecessary repetitive operations and frequent movement by working with others. This is likely an important criterion for determining who is better suited for a particular job in either approach.
Random Interpolation Resize: A free image data augmentation method for object detection in industry

In the inspection task of industrial machine vision, insufficient data frequently occurs, and data enhancement can artificially incorporate the prior knowledge of human vision, expand the image data, and improve the model’s performance, which has become the model’s standard configuration. However, the majority of current data enhancement methods are intended for general scenes, and there are few image enhancement methods designed particularly for industrial object detection. Furthermore, many data enhancement methods require additional training time or annotation recalculation. This paper proposes a novel image data enhancement method, Random Interpolation Resize (RIR). We alter the interpolation method of the standard resize step in the preprocessing from fixed collocation to random selection in order to expand the image data and improve the model’s generalization and detection capabilities. There are no online editing options for annotations, and no additional training time is available. Multiple data sets were tested using various target detection algorithms, resulting in a 0.45% to 5.6% enhancement in mAP0.5 and an increase in the detection effect.
Study of Spatio-Temporal Modeling in Video Quality Assessment

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Supervised breast cancer prediction using integrated dimensionality reduction convolutional neural network

Breast cancer is a major health problem with high mortality rates. Early detection of breast cancer will promote treatment. A technology that determines whether a tumor is benign desirable. This article introduces a new method in which deep learning is used to classify breast cancer.A new computer-aided detection (CAD) system is presented to classify benign and malignant masses in breast tumor cell samples. In the CAD system, (1) for the pathological data of unbalanced tumors, the training results are biased towards the side with the larger number of samples. This paper uses a Conditional Deep Convolution Generative Adversarial Network (CDCGAN) method to generate small samples by orientation data set to solve the imbalance problem of collected data. (2) For the high-dimensional data redundancy problem, this paper proposes an integrated dimension reduction convolutional neural network (IDRCNN) model, which solves the high-dimensional data dimension reduction problem of breast cancer and extracts effective features. The subsequent classifier found that by using the IDRCNN model proposed in this paper, the accuracy of the model was improved.Experimental results show that IDRCNN combined with the model of CDCGAN model has superior classification performance than existing methods, as revealed by sensitivity, area under the curve (AUC), ROC curve and accuracy, recall, sensitivity, specificity, precision,PPV,NPV and f-values analysis.This paper proposes a Conditional Deep Convolution Generative Adversarial Network (CDCGAN) which can solve the imbalance problem of manually collected data by directionally generating small sample data sets. And an integrated dimension reduction convolutional neural network (IDRCNN) model, which solves the high-dimensional data dimension reduction problem of breast cancer and extracts effective features.
NR-MVSNet: Learning Multi-View Stereo Based on Normal Consistency and Depth Refinement

Multi-view Stereo (MVS) aims to reconstruct a 3D point cloud model from multiple views. In recent years, learning-based MVS methods have received a lot of attention and achieved excellent performance compared with traditional methods. However, these methods still have apparent shortcomings, such as the accumulative error in the coarse-to-fine strategy and the inaccurate depth hypotheses based on the uniform sampling strategy. In this paper, we propose the NR-MVSNet, a coarse-to-fine structure with the depth hypotheses based on the normal consistency (DHNC) module, and the depth refinement with reliable attention (DRRA) module. Specifically, we design the DHNC module to generate more effective depth hypotheses, which collects the depth hypotheses from neighboring pixels with the same normals. As a result, the predicted depth can be smoother and more accurate, especially in texture-less and repetitive-texture regions. On the other hand, we update the initial depth map in the coarse stage by the DRRA module, which can combine attentional reference features and cost volume features to improve the depth estimation accuracy in the coarse stage and address the accumulative error problem. Finally, we conduct a series of experiments on the DTU, BlendedMVS, Tanks & Temples, and ETH3D datasets. The experimental results demonstrate the efficiency and robustness of our NR-MVSNet compared with the state-of-the-art methods. Our implementation is available at https://github.com/wdkyh/NR-MVSNet.
A Virtual Reality Museum to Reinforce the Interpretation of Contemporary Art and Increase the Educational Value of User Experience

The cultural heritage sector increasingly integrates augmented and virtual reality (VR) solutions to meet dissemination and interpretation needs for its collections. As research in the field grows, the required entertainment and learning impacts of such applications are rising. This study presents a VR museum that aims to facilitate an understanding of cultural heritage. More specifically, an exhibition was designed, curated and developed in a VR environment based on a framework that encourages the public’s interaction with the artworks and experiential learning through activities that utilize VR functionalities in a meaningful way. This framework was applied in a contemporary art museum where the description of artistic concepts is not always obvious to the general public due to the abstract forms of the artworks or the particularities of different artistic movements. This paper focuses on the application development and three user experience evaluations (museum experts, technical experts and general audience). The results were positive regarding the perceived sense of control, usability and the feelings of the user, including their sense of entertainment. Additionally, the participants valued the educational value of the developed activity types and their usefulness. Moreover, the users were interested in exploring the cultural heritage content available in the exhibition, and they would suggest the application to colleagues or friends.
D-CONFORMER: Deformable Sparse Transformer Augmented Convolution for Voxel-Based 3D Object Detection

Although CNN-based and Transformer-based detectors have made impressive improvements in 3D object detection, these two network paradigms suffer from the interference of insufficient receptive field and local detail weakening, which significantly limits the feature extraction performance of the backbone. In this paper, we propose to fuse convolution and transformer, and simultaneously considering the different contributions of non-empty voxels at different positions in 3D space to object detection, it is not consistent with applying standard convolution and transformer directly on voxels. Specifically, we design a novel deformable sparse transformer to perform long-range information interaction on fine-grained local detail semantics aggregated by focal sparse convolution, termed D-Conformer. D-Conformer learns valuable voxels with position-wise in sparse space and can be applied to most voxel-based detectors as a backbone. Extensive experiments demonstrate that our method achieves satisfactory detection results and outperforms state-of-the-art 3D detection methods by a large margin.
F-PABEE: Flexible-Patience-Based Early Exiting For Single-Label and Multi-Label Text Classification Tasks

Computational complexity and overthinking problems have become the bottlenecks for pre-training language models (PLMs) with millions or even trillions of parameters. A Flexible-Patience-Based Early Exiting method (F-PABEE) has been proposed to alleviate the problems mentioned above for single-label classification (SLC) and multi-label classification (MLC) tasks. F-PABEE makes predictions at the classifier and will exit early if predicted distributions of cross-layer are consecutively similar. It is more flexible than the previous state-of-the-art (SOTA) early exiting method PABEE because it can simultaneously adjust the similarity score thresholds and the patience parameters. Extensive experiments show that: (1) F-PABEE makes a better speedup-accuracy balance than existing early exiting strategies on both SLC and MLC tasks. (2) F-PABEE achieves faster inference and better performances on different PLMs such as BERT and ALBERT. (3) F-PABEE-JSKD performs best for F-PABEE with different similarity measures.
Projector-based augmented stacking framework for irregularly shaped objects

Abstract Augmented reality in additive fabrication is predominantly applied to the manufacturing of structures from regularly shaped materials. Our work however embraces natural heterogeneity, by focusing on the assembly of irregularly shaped elements such as mineral scraps. We introduce a computer-aided framework with a 3D stacking engine and an augmented reality interface capable of guiding users through the balanced positioning of discrete and highly nonuniform objects according to an on-the-fly computed model and without prior trimming of the building unit. The pipeline is tested by the construction of two dry-stone structures (i.e., lacking any adhesives), and its accuracy is validated with a comparative study between the point cloud of the as-built digitized artifacts and the generated execution model, which shows an average 2.9 ± 1.8 cm error between the stones of the two models. We finally show how the proposed framework could be improved both in terms of software and hardware. In the interests of reproducibility, all the methods are shared as open source with the community. Graphical abstract
Fully Distributed Federated Learning with Efficient Local Cooperations

Recently, a shift has been observed towards the so-called edge machine learning, which allow multiple devices with local computational and storage resources to collaborate with the assistance of a centralized server. The well-known federated learning approach is able to utilize such architectures by allowing the exchange of only parameters with the server, while keeping the datasets private to each contributing device. In this work, we propose a communication-efficient, fully distributed, diffusion-based learning algorithm that does not require a parameter server and propose an adaptive combination rule for the cooperation of the devices. By adopting a classification task on the MNIST dataset, the efficacy of the proposed algorithm is demonstrated in non-IID dataset scenarios.
Contextual Similarity is More Valuable Than Character Similarity: An Empirical Study for Chinese Spell Checking

Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors. Recently, related researches focus on introducing character similarity from confusion set to enhance the CSC models, ignoring the context of characters that contain richer information. To make better use of contextual information, we propose a simple yet effective Curriculum Learning (CL) framework for the CSC task. With the help of our model-agnostic CL framework, existing CSC models will be trained from easy to difficult as humans learn Chinese characters and achieve further performance improvements. Extensive experiments and detailed analyses on widely used SIGHAN datasets show that our method outperforms previous state-of-the-art methods. More instructively, our study empirically suggests that contextual similarity is more valuable than character similarity for the CSC task.
From Easy to Hard: Two-Stage Selector and Reader for Multi-Hop Question Answering

Multi-hop question answering (QA) is a challenging task that requires complex reasoning over multiple documents. Existing works commonly introduce techniques such as graph modeling and question decomposition to explore precise intermediate results of multi-hop reasoning, leading to complexity growth and error accumulation. In this paper, we propose FE2H, a simple yet effective framework without extra tasks to address these problems. FE2H is based on our key observation that a standard fine-tuned pre-trained language model (PLM) for QA could achieve strong performance once the input context could be encoded by PLM without truncation. Specifically, a novel two-stage document selector is pro-posed to generate sufficient context while avoiding input truncation. Additionally, an enhanced reader trained with a two-stage strategy is devised to further boost the performance. Extensive experiments on the popular multi-hop QA benchmark HotpotQA show that despite its simplicity, FE2H achieves competitive results compared to state-of-the-art methods.
De’hubert: Disentangling Noise in a Self-Supervised Model for Robust Speech Recognition

Existing self-supervised pre-trained speech models have offered an effective way to leverage massive unannotated corpora to build good automatic speech recognition (ASR). However, many current models are trained on a clean corpus from a single source, which tends to do poorly when noise is present during testing. Nonetheless, it is crucial to overcome the adverse influence of noise for real-world applications. In this work, we propose a novel training framework, called deHuBERT, for noise reduction encoding inspired by H. Barlow’s redundancy-reduction principle. The new framework improves the HuBERT training algorithm by introducing auxiliary losses that drive the self- and cross-correlation matrix between pairwise noise-distorted embeddings towards identity matrix. This encourages the model to produce noise- agnostic speech representations. With this method, we report improved robustness in noisy environments, including unseen noises, without impairing the performance on the clean set.