Reviewerly/exHarmony huggingface dataset:
Leveraging deep feature learning for wearable sensors based handwritten character recognition

Despite rapid advancements in technology, handwritten characters still hold significant roles in various fields, including education, communication, biometric signature verification, and health care. These applications often require digitization of the handwritten characters and associated hand movements to facilitate effective analysis and interpretation of the underlying task. Offline and online handwriting recognition are crucial steps involving the digitization of handwritten characters. Most of these existing systems actively use image processing techniques that are highly sensitive to environmental lighting conditions. Surface Electromyography signals (sEMG), being invariant to lighting conditions, are used in online handwriting recognition to facilitate the automatic transcription of handwritten characters. In this article, we have leveraged deep representation learning to build an efficient and robust sEMG-based Handwritten Character Recognition (HCR) pipeline. A Stacked sparse denoising autoencoder network is applied to obtain an effective deep feature representation. These rich low dimensional features obtained are further introduced into basic classifiers, producing state-of-the-art accuracy for the task. Additional experiments were performed to analyze the effect of the fusion of complementary sensing modules (Accelerometer and Gyroscope) on the performance of sEMG based HCR pipeline. Extensive evaluations were performed to ensure the validity of the obtained results. For the experimentation, new datasets consisting of sEMG, Accelerometer, and Gyroscope signals corresponding to 26 handwritten lower English alphabets were collected from 15 subjects. Our proposed pipeline can be used to build real-time Human–Computer Interaction(HCI) applications for smart classrooms facilitating digitization of handwritten notes and clinical applications involving handwriting analysis tasks. • We explored autoencoders for handwritten characters recognition using sEMG signals. • We recorded sEMG signals of 15 subjects while writing 26 lowercase English alphabets. • Deep stacked sparse denoising autoencoders were applied on this new dataset. • Further experimental analysis was done by coupling sEMG with IMU sensor module. • Our proposed approach achieved an accuracy of 98.72% even with baseline classifiers. • Our model can potentially be used in smart-classrooms and clinical applications.
Detection confidence driven multi-object tracking to recover reliable tracks from unreliable detections

Multi-object tracking (MOT) systems often rely on accurate object detectors; however, accurate detectors are not available in every application domain. We present Robust Confidence Tracking (RCT), an offline MOT algorithm designed for settings where detection quality is poor. Whereas prior methods simply threshold and discard detection confidence information, RCT relies on the exact detection confidence values to increase track quality throughout the entire tracking pipeline. This innovation (along with some simple and well-studied heuristics) allows RCT to achieve robust performance with minimal identity switches, even when provided with completely unfiltered detections. To compare trackers in the presence of unreliable detections, we present a challenging real-world underwater fish tracking dataset, FISHTRAC. In an large-scale evaluation across FISHTRAC, UA-DETRAC, and MOTChallenge data, RCT outperforms a wide variety of trackers, including deep trackers and more classic approaches. We have publically released our FISHTRAC codebase and training dataset at https://github.com/tmandel/fish-detrac, which will facilitate comparing trackers on understudied problems.
A dynamic graph expansion network for multi-hop knowledge base question answering

Knowledge base question answering aims to answer a question over a knowledge base. Multi-hop knowledge base question answering is a challenging task because it requires multi-step reasoning according to the question to get the answer. Existing models infer the answer over a static subgraph or attend to different parts of the question through an intermediate signal. The former obtains limited semantic information, and the latter provides limited reasoning due to the weak supervision signal. In this paper, we eliminate the limitation of static subgraph reasoning by dynamically expanding subgraphs, which connect the question and subgraph to form a joint subgraph. We then adjust the dynamic subgraph to enable reasoning at each step. Specifically, at each step, the question connects different subgraphs, respects the context while paying attention to a specific part of the question, generates a strong intermediate signal, acts on the subsequent reasoning, and finally obtains a correct answer. A large number of experiments on three datasets show that our method performs better than previous state-of-the-art models.
An advanced Internet-of-Drones System with Blockchain for improving quality of service of Search and Rescue: A feasibility study

Drone-based systems supporting Search and Rescue (SAR) missions help expeditiously improve the possibility of discovering the missing victims. Nonetheless, the current drone-based systems have some limitations, such as the need for humans in control of large-scale drones, drone's energy inefficiency, repercussion of quality of service (QoS) from abnormalities events, shortages of automaticity and real-time interactions, deficiency of swift decisions from artificial intelligence-based SAR, and underestimation of security issues in SAR systems. Therefore, developing a more advanced drone-based system is necessary to overcome these limitations. However, it is challenging to achieve the target due to trade-off relationships of technologies and strict requirements of time-critical SAR. This paper studies the feasibility of an Internet-of-Drones (IoD) system using blockchain and artificial intelligence at the edge to overcome limitations and improve SAR QoS. An advanced IoD system architecture from drones to a back-end system and end-users terminals has been proposed. Furthermore, advanced edge services and artificial intelligence at the edge have been presented for automatically searching for missing persons. In addition, computation offloading approaches have been provided to improve the energy efficiency of drones and reduce system latency. Last but not least, public and private blockchain, including Ethereum and Hyperledger Fabric, for providing secure and decentralized healthcare platform has been investigated and analyzed to enable real-time interaction between healthcare entities and improves healthcare services. The results show that the proposed system helps overcome the limitations and improve the SAR QoS. Besides, the proposed system satisfies the security requirements, including confidentiality, integrity, authentication, authorization, access control, privacy, trust, transparency, availability, automaticity, and tolerance.
Efficient derivative-free Bayesian inference for large-scale inverse problems

Abstract We consider Bayesian inference for large-scale inverse problems, where computational challenges arise from the need for repeated evaluations of an expensive forward model. This renders most Markov chain Monte Carlo approaches infeasible, since they typically require <?CDATA $\mathcal{O}(1{0}^{4})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">O</mml:mi> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mn>1</mml:mn> <mml:msup> <mml:mrow> <mml:mn>0</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> model runs, or more. Moreover, the forward model is often given as a black box or is impractical to differentiate. Therefore derivative-free algorithms are highly desirable. We propose a framework, which is built on Kalman methodology, to efficiently perform Bayesian inference in such inverse problems. The basic method is based on an approximation of the filtering distribution of a novel mean-field dynamical system, into which the inverse problem is embedded as an observation operator. Theoretical properties are established for linear inverse problems, demonstrating that the desired Bayesian posterior is given by the steady state of the law of the filtering distribution of the mean-field dynamical system, and proving exponential convergence to it. This suggests that, for nonlinear problems which are close to Gaussian, sequentially computing this law provides the basis for efficient iterative methods to approximate the Bayesian posterior. Ensemble methods are applied to obtain interacting particle system approximations of the filtering distribution of the mean-field model; and practical strategies to further reduce the computational and memory cost of the methodology are presented, including low-rank approximation and a bi-fidelity approach. The effectiveness of the framework is demonstrated in several numerical experiments, including proof-of-concept linear/nonlinear examples and two large-scale applications: learning of permeability parameters in subsurface flow; and learning subgrid-scale parameters in a global climate model. Moreover, the stochastic ensemble Kalman filter and various ensemble square-root Kalman filters are all employed and are compared numerically. The results demonstrate that the proposed method, based on exponential convergence to the filtering distribution of a mean-field dynamical system, is competitive with pre-existing Kalman-based methods for inverse problems.
Uncertainty Guided Policy for Active Robotic 3D Reconstruction Using Neural Radiance Fields

In this letter, we tackle the problem of active robotic 3D reconstruction of an object. In particular, we study how a mobile robot with an arm-held camera can select a favorable number of views to recover an object's 3D shape efficiently. Contrary to the existing solution to this problem, we leverage the popular neural radiance fields-based object representation, which has recently shown impressive results for various computer vision tasks. However, it is not straightforward to directly reason about an object's explicit 3D geometric details using such a representation, making the next-best-view selection problem for dense 3D reconstruction challenging. This paper introduces a ray-based volumetric uncertainty estimator, which computes the entropy of the weight distribution of the color samples along each ray of the object's implicit neural representation. We show that it is possible to infer the uncertainty of the underlying 3D geometry given a novel view with the proposed estimator. We then present a next-best-view selection policy guided by the ray-based volumetric uncertainty in neural radiance fields-based representations. Encouraging experimental results on synthetic and real-world data suggest that the approach presented in this paper can enable a new research direction of using an implicit 3D object representation for the next-best-view problem in robot vision applications, distinguishing our approach from the existing approaches that rely on explicit 3D geometric modeling.
OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network

Heterogeneous Graph Neural Networks (HGNNs), as a kind of powerful graph representation learning methods on heterogeneous graphs, have attracted increasing attention of many researchers. Although, several existing libraries have supported HGNNs, they just provide the most basic models and operators. Building and benchmarking various downstream tasks on HGNNs is still painful and time consuming with them. In this paper, we will introduce OpenHGNN, an open-source toolkit for HGNNs. OpenHGNN defines a unified and standard pipeline for training and testing, which can allow users to run a model on a specific dataset with just one command line. OpenHGNN has integrated 20+ mainstream HGNNs and 20+ heterogeneous graph datasets, which can be used for various advanced tasks, such as node classification, link prediction, and recommendation. In addition, thanks to the modularized design of OpenHGNN, it can be extended to meet users' customized needs. We also release several novel and useful tools and features, including leaderboard, autoML, design space, and visualization, to provide users with better usage experiences. OpenHGNN is an open-source project, and the source code is available at https://github.com/BUPT-GAMMA/OpenHGNN.
LESSL: Can LEGO sampling and collaborative optimization contribute to self-supervised learning?

Self-supervised visual representation learning (SSL) aims to extract the most distinctive features from unlabeled datasets to overcome challenges of labor-intensive and time-consuming data annotation. Recent advances in this area are dominated by contrastive learning-based methods with various sampling strategies and optimization objectives. Unfortunately, these methods are limited by larger batch sizes and longer training epochs, resulting in non-negligible computational resource consumption and memory footprint . Unlike prior sampling manners and inspired by LEGO bricks, we design a comprehensive interleave sampling module to fully exploit unlabeled datasets, which seamlessly integrates the advantages of semantic and spatial relations complementarity from the unlabeled image via optimizing two objectives. Specifically, the unlabeled images are sampled in a dense and interleaved manner, which breaks down barriers of under-utilization of datasets existing in state-of-the-art methods. Meanwhile, the redefined collaborative optimization term is designed to alleviate feature-specificity and implicitly explore sample relationships during training, which effectively improves the performance of downstream tasks on various datasets. Besides, we focus on high-level semantic features and utilize the spatial structure relationships provided by the unlabeled datasets to ensure the learned features with low-level texture characteristics. Extensive experiments on four types of datasets demonstrate that our method performs favorably against the state-of-the-art SSL methods.
DPC-FSC: An approach of fuzzy semantic cells to density peaks clustering

Density peaks clustering (DPC) algorithm is a succinct and efficient density-based clustering approach to data analysis. It computes the local density and the relative distance for objects to seek cluster centers and form clusters. However, it is difficult to estimate an appropriate local density by a rule of thumb ; therefore, the performance of DPC may be poor on real datasets in practice. Thus, this study proposes a novel method for density peaks clustering based on fuzzy semantic cells. Specifically, each object is coarsened into a fuzzy point with the form of the fuzzy semantic cell model. Based on this model, a local density metric is defined and estimated via the principle of justifiable granularity . Our local density estimation can be converted into an optimization problem. In addition, a relative semantic distance is also introduced which concerns the distance between fuzzy semantic cells. The relative semantic distance is more informative for selecting cluster centers in the decision graph. The experimental results show that our method not only exhibits higher performance but also provides a clearer decision graph to select cluster centers.
A research framework for digital nomadism: a bibliometric study

Digital nomads live a new way of life that creates an ideal balance of work and leisure. Research on the phenomenon of digital nomads is still in its early stages and is not fully framed as a proper research category. Therefore, the present research aims to explore research on digital nomadism by study leading countries, authors and themes that can become a foundation for future research. This study is exploratory and interpretive – using bibliometrics, we systematically searched all articles indexed in the Web of Science database. The study presents the evolution of scholarly production, and identifies key authors and countries that have the potential to become pioneers in digital nomad research. We identified 17 core concepts of digital nomad research as well as concepts that have not yet received much attention from scientists. Additionally, our study provides a framework for research on digital nomadism and presents topics for future research: we determine how the 17 core concepts identified in this study affect the lives of digital nomads, research into legislation that directly affects digital nomads, study how COVID-19 has changed working styles, and offer a bibliometric analysis of data on digital nomads from other databases.
Virtual Reality Integration in Social Studies Classroom: Impact on Student Knowledge, Classroom Engagement, and Historical Empathy Development

While virtual reality (VR) technology can provide students with first-hand and situational learning experiences, limited studies have integrated VR in a K-12 classroom, resulting in the lack of understanding of the benefits and challenges of VR use in classroom settings. To examine the impact of VR on student learning, this study employed a mixed-methods quasi-experimental research approach and integrated a fully immersive VR (i.e., Oculus Quest) and non-immersive VR (i.e., 3D website) into 9th-grade social studies classrooms. The findings demonstrated that while the quantitative data did not demonstrate statistically significant improvement in knowledge development and classroom engagement after students use VR, qualitative data showed positive learning benefits and classroom engagement. Furthermore, statistically significant growth was observed in the development of historical empathy with VR use. The findings share critical insight into the impact of VR on student learning and the challenges of VR integration in a K-12 classroom.
Brain MRI high resolution image creation and segmentation with the new GAN method

• It provides a better way to analyze images by increasing the image resolution. • A new and high-accuracy method has been proposed by making the unsupervised ones of the state-of-art methods controlled while segmenting the image. • Effective results in Brain-MRI segmentation. Brain magnetic resonance imaging segmentation is a recent and still popular research area. Good and accurate segmentation results play an important role in the diagnosis of cancer or other brain diseases. In this article, a novel Generative Adversarial Network architecture is proposed for brain magnetic resonance imaging segmentation. The proposed method is called SSimDCL (Supervised SimDCL). Four studies were conducted in this article. In the first study, the SSimDCL method on the two-dimensional brain magnetic resonance imaging dataset was compared with the current state-of-art architectures CycleGAN, CUT, FastCUT, DCLGAN, and SimDCL. In the second study, the dataset resolution was improved. In the third study, being measured the efficiency of the newly created dataset. And the SSimDCL is trained for both the dataset with increased resolution and the normal dataset, and the results are obtained. In the fourth study, the results of the SSimDCL and the VolBrain brain magnetic resonance imaging segmentation results, which are widely used today, are included. When VolBrain segmentation and SSimDCL segmentation are compared. The results were compared both visually and metrically. Fréchet Inception Distance (FID), Kernel Inception Distance (KID), Peak Signal to Noise Ratio (PSNR) and Learned Perceptual Image Patch Similarity (LPIPS) were used as measurement metrics. The Jaccard and Dice similarity metrics were also used in the analysis. It was observed that the SSimDCL give satisfactory results in all four studies. This method can be used as an automatic brain MRI image segmentation system.
Role of machine learning in attaining environmental sustainability

Climate change and sustainable development are significant challenges that must be addressed as soon as possible. As a result of this vision, the renewable energy (RE) industry has experienced great growth, with artificial intelligence being applied to improve its overall efficiency (AI). However, when it comes to achieving the sustainable development goals (SDGs), they are not sufficiently explored or considered. Countries all across the world are seeking to achieve 100% renewable energy by 2050, and this is a goal shared by a large number of other nations. More research is needed to evaluate whether renewable energy (RE) can assist us in meeting our sustainable development goals, given that it has lately experienced a considerable increase in popularity and is having an increasing impact on the global energy industry. In addition, an assessment of AI potential contribution to the achievement of the Sustainable Development Goals is being carried out. In this paper, we develop a machine learning model using Regression Kriging to classify the radiative energy flux at the earth surface in order of attaining environmental sustainability using solar panels. Various factors are given as input to the model that takes into account the modelling of data in obtaining the environmental sustainability. The simulation is conducted to test the efficacy of the model in obtaining the environmental sustainability and the results shows improved sustainability than other methods.
How Immersive Virtual Reality Safety Training System Features Impact Learning Outcomes: An Experimental Study of Forklift Training

Immersive virtual reality (VR)–based training has been widely proposed in different firms to improve the hazard recognition skills of their workforce and change their unsafe behavior. However, little is known about the impact of VR-based training on the user’s behavior and learning. With the use of structural equation modeling (SEM), this study investigated the impact of VR-based training on 60 participants, and the results supported the mediating effect of VR system features on the users’ acquisition of knowledge, behavioral intention, and satisfaction. The results also indicated that the VR system features were a significant antecedent to psychological factors (presence, motivation, enjoyment, and self-efficacy). This suggests that there are two general paths: (1) usability and fidelity (UF)–enjoyment (EJ)–behavioral intention (BI); and (2) UF–EJ–satisfaction (ST), by which VR-based safety training can have a positive impact on the users’ behavior. This study also revealed that the higher level of presence in the VR training environment would not exert a strong influence on users’ behavior. The findings of this study could help to better design VR-based training programs in a cost-effective way and thus could maximize the benefits of VR technology for industry.