Reviewerly/exHarmony huggingface dataset:
Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation

This paper studies the problem of multi-person pose estimation in a bottom-up fashion. With a new and strong observation that the localization issue of the center-offset formulation can be remedied in a local-window search scheme in an ideal situation, we propose a multi-person pose estimation approach, dubbed as LOGO-CAP, by learning the LOcal-GlObal Contextual Adaptation for human Pose. Specifically, our approach learns the keypoint attraction maps (KAMs) from the local keypoints expansion maps (KEMs) in small local windows in the first step, which are subsequently treated as dynamic convolutional kernels on the keypoints-focused global heatmaps for contextual adaptation, achieving accurate multi-person pose estimation. Our method is end-to-end trainable with near real-time inference speed in a single forward pass, obtaining state-of-the-art performance on the COCO keypoint benchmark for bottom-up human pose estimation. With the COCO trained model, our method also outperforms prior arts by a large margin on the challenging OCHuman dataset.
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information

Fine-grained image classification is a challenging computer vision task where various species share similar visual appearances, resulting in misclassification if merely based on visual clues. Therefore, it is helpful to leverage additional information, e.g., the locations and dates for data shooting, which can be easily accessible but rarely exploited. In this paper, we first demonstrate that existing multimodal methods fuse multiple features only on a single dimension, which essentially has insufficient help in feature discrimination. To fully explore the potential of multimodal information, we propose a dynamic MLP on top of the image representation, which interacts with multimodal features at a higher and broader dimension. The dynamic MLP is an efficient structure parameterized by the learned embeddings of variable locations and dates. It can be regarded as an adaptive nonlinear projection for generating more discriminative image representations in visual tasks. To our best knowledge, it is the first attempt to explore the idea of dynamic networks to exploit multimodal information in fine-grained image classification tasks. Extensive experiments demonstrate the effectiveness of our method. The t-SNE algorithm visually indicates that our technique improves the recognizability of image representations that are visually similar but with different categories. Furthermore, among published works across multiple fine-grained datasets, dynamic MLP consistently achieves SOTA results <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://paperswithcode.com/dataset/inaturalist and takes third place in the iNaturalist challenge at FGVC8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> https://www.kaggle.com/c/inaturalist-2021/leaderboard. Code is available at httpsr//glthub.com/megvii-research/DynamicMLPForFinegrained.
Statistical hypothesis testing as an instrument of pedagogical experiment

The study is urgent due the practical orientation of modern educational paradigm with data analysis applied as the main tool to obtain new scientific results in psychological and pedagogical experiments. This emphasizes the need for basic mathematical education of scientists. The study reveals the potential of statistical hypothesis testing toolkit without using special statistical data processing packages and preliminary mathematical training of scientists. The paper presents the results of a long-term pedagogical experiment on the development of pedagogical approaches in the traditional education of Evens school-age children by organizing a summer nomadic camp from 2002 to 2017 in.the Republic of Sakha (Yakutia) of the Russian Federation. The paper describes in detail the methodology of applying the Wilcoxon signed-rank test, which makes it possible to determine the statistical significance of typical shifts in coherent samples. In this study the results of the Evens school-age children’s achievements BEFORE and AFTER attending the nomadic camp were used as coherent samples. Despite the general and non-specific descriptions of the campers` achievements, the described algorithm for using the rank non-parametric criterion made it possible to quantify the main pedagogical results obtained during the pedagogical experiment to develop and test a new form of traditional education for children of indigenous small-numbered people inhabiting the North executed as a summer nomadic camp. The results thus obtained are of practical importance for the researchers in the field of data analysis in psychological and pedagogical sciences.
How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting

Accurate prediction of future human positions is an essential task for modern video-surveillance systems. Current state-of-the-art models usually rely on a “history” of past tracked locations (e.g., 3 to 5 seconds) to predict a plausible sequence of future locations (e.g., up to the next 5 seconds). We feel that this common schema neglects critical traits of realistic applications: as the collection of input trajectories involves machine perception (i.e., detection and tracking), incorrect detection and fragmentation errors may accumulate in crowded scenes, leading to tracking drifts. On this account, the model would be fed with corrupted and noisy input data, thus fatally affecting its prediction performance. In this regard, we focus on delivering accurate predictions when only few input observations are used, thus potentially lowering the risks associated with automatic perception. To this end, we conceive a novel distillation strategy that allows a knowledge transfer from a teacher network to a student one, the latter fed with fewer observations (just two ones). We show that a properly defined teacher super-vision allows a student network to perform comparably to state-of-the-art approaches that demand more observations. Besides, extensive experiments on common trajectory forecasting datasets highlight that our student network better generalizes to unseen scenarios.
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition

Unlike the conventional Knowledge Distillation (KD), Self-KD allows a network to learn knowledge from itself without any guidance from extra networks. This paper proposes to perform Self-KD from image Mixture (MixSKD), which integrates these two techniques into a unified framework. MixSKD mutually distills feature maps and probability distributions between the random pair of original images and their mixup images in a meaningful way. Therefore, it guides the network to learn cross-image knowledge by modelling supervisory signals from mixup images. Moreover, we construct a self-teacher network by aggregating multi-stage feature maps for providing soft labels to supervise the backbone classifier, further improving the efficacy of self-boosting. Experiments on image classification and transfer learning to object detection and semantic segmentation demonstrate that MixSKD outperforms other state-of-the-art Self-KD and data augmentation methods. The code is available at https://github.com/winycg/Self-KD-Lib.
Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations

Neural Radiance Field (NeRF) regresses a neural param-eterized scene by differentially rendering multi-view images with ground-truth supervision. However, when interpolating novel views, NeRF often yields inconsistent and visually non-smooth geometric results, which we consider as a generalization gap between seen and unseen views. Recent advances in convolutional neural networks have demonstrated the promise of advanced robust data augmentations, either random or learned, in enhancing both in-distribution and out-of-distribution generalization. Inspired by that, we propose Augmented NeRF (Aug-NeRF), which for the first time brings the power of robust data augmentations into regular-izing the NeRF training. Particularly, our proposal learns to seamlessly blend worst-case perturbations into three distinct levels of the NeRF pipeline with physical grounds, including (1) the input coordinates, to simulate imprecise camera parameters at image capture; (2) intermediate features, to smoothen the intrinsic feature manifold; and (3) pre-rendering output, to account for the potential degra-dation factors in the multi-view image supervision. Extensive results demonstrate that Aug-NeRF effectively boosts NeRF performance in both novel view synthesis (up to 1.5dB PSNR gain) and underlying geometry reconstruction. Fur-thermore, thanks to the implicit smooth prior injected by the triple-level augmentations, Aug-NeRF can even recover scenes from heavily corrupted images, a highly challenging setting untackled before. Our codes are available in https://github.com/VITA-Group/Aug-NeRF.
Seismic multi-hazard and impact estimation via causal inference from satellite imagery

Rapid post-earthquake reconnaissance is important for emergency responses and rehabilitation by providing accurate and timely information about secondary hazards and impacts, including landslide, liquefaction, and building damage. Despite the extensive collection of geospatial data and satellite images, existing physics-based and data-driven methods suffer from low estimation performance due to the complex and event-specific causal dependencies underlying the cascading processes of earthquake-triggered hazards and impacts. Herein, we present a rapid seismic multi-hazard and impact estimation system that leverages advanced statistical causal inference and remote sensing techniques. The unique feature of this system is that it provides accurate and high-resolution estimations on a regional scale by jointly inferring multiple hazards and building damage from satellite images through modeling their causal dependencies. We evaluate our system on multiple seismic events from diverse countries around the globe. Our results corroborate that incorporating causal dependencies significantly improves large-scale estimation accuracy for multiple hazards and impacts compared to existing systems. The results also reveal quantitative causal mechanisms among earthquake-triggered multi-hazard and impact for multiple seismic events. Our system establishes a new way to extract and utilize the complex interactions of multiple hazards and impacts for effective disaster responses and advancing understanding of seismic geological processes.
A trustless architecture of blockchain-enabled metaverse

Metaverse has rekindled human beings’ desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee.
A hybrid end-to-end learning approach for breast cancer diagnosis: convolutional recurrent network

In this study, mammography images are classified as normal, benign, and malignant using the Mammographic Image Analysis Society (MIAS) and INbreast datasets. After the preprocessing of each image, the processed images are given as input to two different end-to-end deep networks. The first network contains only a Convolutional Neural Network (CNN), while the second network is a hybrid structure that includes both the CNN and Bidirectional Long Short Term Memories (BiLSTM). The classification accuracy obtained using the first and second hybrid architectures is 97.60% and 98.56% for the MIAS dataset, respectively. In addition, experiments performed for the INbreast dataset at the study's end prove the proposed method's effectiveness. These results are comparable to those obtained in previous popular studies. The proposed study contributes to previous studies in terms of preprocessing steps, deep network design, and high diagnostic accuracy.
Teachers’ Views on Integrating Augmented Reality in Education: Needs, Opportunities, Challenges and Recommendations

The integration of augmented reality (AR) in education is promising since it enhances teaching and offers more engaging and appealing learning experiences. Teachers can have a catalytic role towards the adoption of AR in education; therefore, their perspectives with regard to AR in teaching and learning are very important. The current study explores teachers’ views on the integration of AR in education through an open-ended questionnaire that has been answered by 93 educators worldwide. A set of digital skills that can support student-centered pedagogies in an appropriate infrastructure are the main requirement for effective teaching with AR. Among the perceived benefits and opportunities are interactive teaching and learning, increased interest and engagement, better understanding of complex concepts. As barriers, participants reported the lack of AR educational applications, the cost of buying and maintaining AR equipment and resources, the lack of teachers’ and students’ digital skills, classroom management issues, and security and ethical issues. Moreover, survey participants highlighted the need for raising teachers’ awareness for the added value of AR in education and the need for teachers’ continuous professional development. Implications and future research recommendations on the integration of AR in education are discussed.
Sustainability in Digital Education: A Systematic Review of Innovative Proposals

In the last decade, the extensive use of new Information and Communication Technologies (ICT) in education in recent years has changed the nature of the teaching–learning environment. However, the adequate use of ICT is necessary for promoting educational practices that contribute to sustainable development. The systematization of the research in this area is presented as an opportunity to provide a contribution to the already existing theories and practices related to the use of ICT and the development of the Sustainable Development Goals (SDGs). The aim of this study is to conduct a systematic review of articles that address innovative approaches to sustainability in digital education. The PRISMA 2020 guidelines were used to review the literature of articles published in the last decade in the Web of Science (WoS) and Scopus databases. The results presented are based on the research questions that will guide the search and analysis of the information. They are divided into theoretical and practical research, giving relevance to the relationship between educational innovation with ICT and sustainability. The main variables that are taken into account in contributing to the SDGs through the use of ICT in educational practice are also presented. A critical discussion on this topic is elaborated, which will help to support a solid theoretical framework. Last, a conclusion on the effectiveness of digital education and its contribution to a sustainable development are provided.
An optimized fuzzy deep learning model for data classification based on NSGA-II

As a powerful paradigm, deep learning (DL) models have been used in many applications for classification tasks in images, text, and audio. Through DL models, we can learn task-driven features from big data. However, DL models are fully deterministic and cannot handle uncertain and imprecise data. DL models are often sensitive to noise in data and do not operate well in areas where data are vague. Moreover, when there is a large feature set or high-dimensional data with irrelevant and redundant features, the DL models’ performance decreases in classification due to training with irrelevant features. To gain reliable results in such high-dimensional problems, DL models require a large amount of data which usually grows exponentially concerning the number of features. Data uncertainty problems and irrelevant features in DL models cause low performance in classification tasks. This paper proposes an optimized fuzzy deep learning (OFDL) model for data classification based on Non-Dominated Sorting Genetic Algorithm II (NSGA-II). OFDL utilizes optimization in the composition of DL and fuzzy learning via the NSGA-II in multi-modal learning. To achieve effective classification, OFDL first considers intelligent feature selection by finding the best trade-offs between two conflicting objective functions, minimizing the number of features, and maximizing the accuracy (maximizing weights of selected features). Next, to reach optimized backpropagation and fuzzy membership functions, OFDL utilizes Pareto optimal solutions for multi-objective optimization using NSGA-II based on their objective functions. Furthermore, the fusion layer in OFDL fused optimized views of DL and fuzzy learning that provides a high-level representation of inputs and optimum features for classification tasks where the data contain high uncertainties and noises. This functionality gives valuable attributes during classification since identifying and selecting appropriate features ensures prompt and correct class. Also, it provides deep insight into tackling the effect of ambiguous data and each feature’s uncertainty in the classification tasks. The examination of OFDL reveals good performance in terms of F-measure, accuracy, recall, precision, and True Positive Rate (TPR) compared to fuzzy classifiers. Furthermore, OFDL has higher accuracy in classification tasks than earlier fuzzy DNN models.
Assessment and application of optical flow in background-oriented schlieren for compressible flows

Abstract Optical flow provides an opportunity to elevate the resolution and sensitivity of deflection sensing in background-oriented schlieren (BOS). Despite extensive relevant literature within the field of computer vision, there is a lack of proper quantification of its abilities and limitations with regard to the state-of-the-art BOS experiments. Thus, this study performs an assessment of accuracy and resolution limits in different flow field scenarios utilizing background patterns generated with random dot and wavelet noise distributions. Accordingly, a synthetic assessment over a theoretically generated Prandtl–Meyer expansion fan is conducted with variations introduced in the background patterns and operational parameters of optical flow. A clear superiority of accuracy and resolvable range of density gradient amplitudes over cross-correlation is demonstrated. Moreover, an experimental assessment of supersonic flow features over multiple wind tunnel models is performed. The influence of experimental constraints, limitations and uncertainties related to the application of optical flow in BOS and its comparative performance against the block-matching counterpart is characterized. Graphical abstract
Data-Centric Client Selection for Federated Learning Over Distributed Edge Networks

This work presents an efficient data-centric client selection approach, named DICE, to enable federated learning (FL) over distributed edge networks. Prior research focused on assessing the computation and communication ability of the client devices for selection in FL. On-device data quality, in terms of data volume and heterogeneity, across these distributed devices is largely overlooked. The obvious outcome is the selection of an improper subset of clients with poor-quallity data, which inevitably results in an inefficient trained model. With an aim to address this problem, in this work, we design DICE which prioritizes the data quality of the client devices in the selection phase, in addition to their computation and communication abilities, to improve the accuracy of FL. Additionally, in DICE, we introduce the assistance of vicinal edge devices to account for the lack of computation or communication abilities in certain devices without violating the privacy-preserving guarantees of FL. Towards this aim, we propose a scheme to decide the optimal edge device, in terms of latency and workload, to be selected as the helper device. The experimental results show that DICE improves convergence speed for a given level of model accuracy. Further, the simulation results show that DICE reduces delay by at least 16%, energy consumption by at least 17%, and packet loss by at least 55% compared to the existing benchmarks while prioritizing the on-device data quality across clients.
Estimation of solar irradiance and solar power based on all-sky images

The ramp event of solar irradiance is prone to occur due to atmospheric conditions, and makes it difficult to integrate solar power into regional power grid. The atmospheric aerosol, cloud occlusion, temperature and weather conditions especially influence the daily solar irradiance, and cause the uncertain of the solar power which would leads to the regional grid overloaded or lower-loaded. To integrate the solar power into the regional grid reliably, the estimation of solar irradiance and solar power output plays an important role. In the study, a system is proposed to perform accurate solar irradiance and solar power estimation. Through the all-sky image feature extractions, the regional and global weights are obtained. The long short-term memory (LSTM) is used as the training model with the weights as inputs to estimate the solar irradiance. The power curve is made by using the estimated solar irradiance and the solar power output to estimate the solar power. Several performance indices are used to evaluate the proposed method, including mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2). The experimental results show that the performance of the solar irradiance estimation of the proposed method is remarkable with the lower MAE and RMSE and the higher R2. Furthermore, the performance of the solar power estimation using the proposed method is very close to the actual estimation performance.
Artificial Intelligence in Africa: Emerging Challenges

Abstract In the current African society, Artificial Intelligence (AI) is becoming more popular and seeking to cover all facets of human activity. The adoption and use of these modern technologies in the African context are currently low due to some emerging challenges. Consequently, these difficulties may have a direct influence on African economic development. In this paper, we highlight the challenges facing the adoption of AI technologies in Africa which include skills acquisition, lack of structured data ecosystem, ethics, government policies, insufficient infrastructure and network connectivity, uncertainty, and user attitude. Finally, various solutions to enhance AI adoption in Africa were then proposed.