Reviewerly/exHarmony huggingface dataset:
wChain: A Fast Fault-Tolerant Blockchain Protocol for Multihop Wireless Networks

This paper presents <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathit {wChain}$ </tex-math></inline-formula> , a blockchain protocol specifically designed for multihop wireless networks that deeply integrates wireless communication properties and blockchain technologies under the realistic SINR model. We adopt a hierarchical spanner as the communication backbone to address medium contention and achieve fast data aggregation within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\log N\log \Gamma)$ </tex-math></inline-formula> slots where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the network size and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula> refers to the ratio of the maximum distance to the minimum distance between any two nodes. Besides, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathit {wChain}$ </tex-math></inline-formula> employs data aggregation and reaggregation as well as node recovery mechanisms to ensure efficiency, fault tolerance, persistence, and liveness. The worst-case runtime of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathit {wChain}$ </tex-math></inline-formula> is upper bounded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(f\log N\log \Gamma)$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f=\lfloor \frac {N}{2} \rfloor $ </tex-math></inline-formula> is the upper bound of the number of faulty nodes. To validate our design, we conduct both theoretical analysis and simulation studies. The results not only demonstrate the nice properties of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathit {wChain}$ </tex-math></inline-formula> , but also point to a large new space for the exploration of blockchain protocols in wireless networks.
Learning features from covariance matrix of gabor wavelet for face recognition under adverse conditions

Face recognition under adverse conditions, such as low-resolution, difficult illumination, blur and noise remains a challenging task. Among existing face recognition methods, Gabor wavelet plays a significant role and has robust performance under adverse conditions since it models the visual cortices of mammalian brain. It has been demonstrated the subbands of Gabor Wavelet (GW) can be efficiently represented by a covariance matrix. However, because covariance matrix does not belong to Euclidean space, Euclidean-based measure such as 2-norm cannot be directly applied to covariance matrix, and more importantly, it is difficult to incorporate learning techniques for the covariance matrix to promote the performance of face recognition. To address this issue, we propose two promising methods by learning the Covariance Matrix of Gabor Wavelet (LCMoG). The first method, called LCMoG-CNN, uses a shallow Convolutional Neural Network (CNN) to project the covariance matrices of GW into a feature vector of Euclidean space; the second method, called LCMoG-LWPZ, uses matrix-logarithm to embed the covariance matrix in the linear space and then uses Whitening Principal Component Analysis (WPCA) to learn the face features from the embedded covariance matrix. The proposed methods are effective to extract the fine features from the face image and have better performance than Deep CNN (DCNN) for small-varying face pose . For the large-varying face pose , LCMoG features combining with DCNN feature can enhance the performance of face recognition. In the experiments, the proposed methods yield promising recognition & verification accuracies under adverse conditions.
Learning To Count Everything

Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-shot regression task. To tackle this task, we present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category. We also introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. The images are annotated with two types of annotation, dots and bounding boxes, and they can be used for developing few-shot counting models. Experiments on this dataset shows that our method outperforms several state-of-the-art object detectors and few-shot counting approaches. Our code and dataset can be found at https://github.com/cvlab-stonybrook/LearningToCountEverything.
Pre-trained Language Model based Ranking in Baidu Search

As the heart of a search engine, the ranking system plays a crucial role in satisfying users' information demands. More recently, neural rankers fine-tuned from pre-trained language models (PLMs) establish state-of-the-art ranking effectiveness. However, it is nontrivial to directly apply these PLM-based rankers to the large-scale web search system due to the following challenging issues: (1) the prohibitively expensive computations of massive neural PLMs, especially for long texts in the web document, prohibit their deployments in an online ranking system that demands extremely low latency; (2) the discrepancy between existing ranking-agnostic pre-training objectives and the ad-hoc retrieval scenarios that demand comprehensive relevance modeling is another main barrier for improving the online ranking system; (3) a real-world search engine typically involves a committee of ranking components, and thus the compatibility of the individually fine-tuned ranking model is critical for a cooperative ranking system. In this work, we contribute a series of successfully applied techniques in tackling these exposed issues when deploying the state-of-the-art Chinese pre-trained language model, i.e., ERNIE, in the online search engine system. We first articulate a novel practice to cost-efficiently summarize the web document and contextualize the resultant summary content with the query using a cheap yet powerful Pyramid-ERNIE architecture. Then we endow an innovative paradigm to finely exploit the large-scale noisy and biased post-click behavioral data for relevance-oriented pre-training. We also propose a human-anchored fine-tuning strategy tailored for the online ranking system, aiming to stabilize the ranking signals across various online components. Extensive offline and online experimental results show that the proposed techniques significantly boost the search engine's performance.
Product innovation in entrepreneurial firms: How business model design influences disruptive and adoptive innovation

Blockchain technology has the potential to drive product innovation in various industries. This emerging technology is an important enabler of the next generation of new ventures. Our study examines the effects of the business model designs of blockchain-based entrepreneurial firms on the scope of product innovation, including disruptive innovation and adoptive innovation. To test this relationship, we conduct a survey to collect data from 159 blockchain-based ventures. The findings indicate that the novel business model strongly entices disruptive innovation but does not influence adoptive product innovation. The results also indicate that the efficient business model strongly encourages adoptive product innovation, but strongly discourages disruptive product innovation. Further, our analysis shows that the firm's disruptive technological capability strengthens the positive relationship between the novel business model and disruptive product innovation but weakens the positive relationship of design efficiency with adoptive innovation. Additionally, we found that the disruptive technological capability strongly entices blockchain-based entrepreneurial firms to favor disruptive product innovation over adoptive product innovation.
Dense Prediction and Local Fusion of Superpixels: A Framework for Breast Anatomy Segmentation in Ultrasound Image With Scarce Data

Segmentation of the breast ultrasound (BUS) image is an important step for subsequent assessment and diagnosis of breast lesions. Recently, Deep-learning-based methods have achieved satisfactory performance in many computer vision tasks, especially in medical image segmentation. Nevertheless, those methods always require a large number of pixel-wise labeled data that is expensive in medical practices. In this study, we propose a new segmentation method by dense prediction and local fusion of superpixels for breast anatomy with scarce labeled data. First, the proposed method generates superpixels from the BUS image enhanced by histogram equalization, a bilateral filter, and a pyramid mean shift filter. Second, using a convolutional neural network (CNN) and distance metric learning-based classifier, the superpixels are projected onto the embedding space and then classified by calculating the distance between superpixels' embeddings and the centers of categories. By using superpixels, we can generate a large number of training samples from each BUS image. Therefore, the problem of the scarcity of labeled data can be better solved. To avoid the misclassification of the superpixels, K-nearest neighbor (KNN) is used to reclassify the superpixels within every local region based on the spatial relationships among them. Fivefold cross-validation was taken and the experimental results show that our method outperforms several often used deep-learning methods under the condition of the absence of a large number of labeled data (48 BUS images for training and 12 BUS images for testing).
Augmented reality is human-like: How the humanizing experience inspires destination brand love

• Applying humanizing experience theory in digital marketing. • Augmented reality (AR) as a discruptive technology in digital marketing are anthropomorphism, self-representation, and intimacy. • An emerging disruptive technology, AR, may help meet the need for contactless services in unique situations, such as the unexpected COVID-19 pandemic. • Green destination brand love enhanced by AR through humanizing digital experience in contactless service journeys. Drawing on humanizing experience theory, we examined the antecedents and consequences of humanizing the digital experience in a virtual tourism context. Using a scenario survey approach, we designed dynamic 360° augmented reality (AR) panorama and 360° spin-of-the-mouse environments to examine the research model and hypotheses. Data from 263 subjects were collected and analyzed with AMOS 20. The empirical results elucidate the psychological mechanism behind the formation of humanizing digital experiences, showing that a 360° AR panorama shapes three features of a humanizing experience—anthropomorphism, self-representation, and intimacy—significantly better than a spin of the mouse, resulting in a stronger effect on green destination brand love. Furthermore, the technology readiness of online tourists significantly moderates the effects of a humanizing digital experience on green destination brand love. This study demonstrates how an emerging disruptive technology, AR, may help meet the need for contactless services in unique situations, such as the unexpected COVID-19 pandemic. The study helps researchers of green destination brand love and AR better understand what a humanizing digital experience in contactless service journeys can offer, paving the way for further research and practical development in this area. Because AR can be used to process big data, ethical compliance is also addressed.
Multi-layer Perceptron Training Using Hybridized Bat Algorithm

Gajic, Luka Cvetnic, Dusan Zivkovic, Miodrag Bezdan, Timea Bacanin, Nebojsa Milosevic, StefanNeural networks (NNs) are a subset in the field of machine learning (ML) that tends to make it possible for a machine to learn and make new predictions based on previous experiences and provided data. It is important to emphasize that there is no need to program this kind of behavior since the whole process is supported by the “self-adjustment” of the algorithm, which can evaluate itself and therefore adjust its parameters to get better performance and accuracy. Neural networks are different from other types of machine learning algorithms in such a way that they do not use statistical and mathematical models to make future predictions. Instead, they replicate the structure and the processes that happen inside the human brain. However, this type of learning is very computationally expensive since there is an enormous amount of states and conditions in which the network itself can be found. Therefore, it can be said that a process of learning for neural networks relates to the collection of NP-complete problems because of the large search space of possible solutions. Swarm intelligence (SI) algorithms can help to reduce this large space of solutions by finding a solution that is not optimal but close optimal and provide satisfactory results given how much longer it would take to train a network without them. In this paper, the authors have proposed a solution for the stated problem based on hybridized bat algorithm.
Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment

Learning computational models of image aesthetics can have a substantial impact on visual art and graphic design. Although automatic image aesthetics assessment is a challenging topic by its subjective nature, psychological studies have confirmed a strong correlation between image layouts and perceived image quality. While previous state-of-the-art methods attempt to learn holistic information using deep Convolutional Neural Networks (CNNs), our approach is motivated by the fact that Graph Convolutional Network (GCN) architecture is conceivably more suited for modeling complex relations among image regions than vanilla convolutional layers. Specifically, we present a Hierarchical Layout-Aware Graph Convolutional Network (HLA-GCN) to capture layout information. It is a dedicated double-subnet neural network consisting of two LA-GCN modules. The first LA-GCN module constructs an aesthetics-related graph in the coordinate space and performs reasoning over spatial nodes. The second LA-GCN module performs graph reasoning after aggregating significant regions in a latent space. The model output is a hierarchical representation with layout-aware features from both spatial and aggregated nodes for unified aesthetics assessment. Extensive evaluations show that our proposed model outperforms the state-of-the-art on the AVA and AADB datasets across three different tasks. The code is available at http://github.com/days1011/HLAGCN.
The Impact of COVID-19 on E-learning: Advantages and Challenges

COVID-19 is the recent coronavirus illness in 2019, began in Wuhan city of China, and has spread quickly worldwide, making billions of individuals face the huge lockdown. The World Health Organization (WHO) proclaimed the COVID plague a pandemic. A considerable number of colleges and educational institutions over the world have either dropped all occasions or postponed those, including educational workshops, meetings, and different sports activities. Colleges and educational institutions take in-depth measures to shield all students and staff members from the exceptionally contagious illness. Due to this pandemic, the shifting to E-learning platforms became the safest option to continue the educational process. In this review, this paper introduces a summary of several prior pieces of research done about the merits and demerits of E-learning along with some global statistics and survey done by using Google forms on 82 university students (undergraduates and postgraduates) who study in UAE to determine if they oppose or support the reliance on E-learning.
Cross-View Regularization for Domain Adaptive Panoptic Segmentation

Panoptic segmentation unifies semantic segmentation and instance segmentation which has been attracting increasing attention in recent years. However, most existing research was conducted under a supervised learning setup whereas unsupervised domain adaptive panoptic segmentation which is critical in different tasks and applications is largely neglected. We design a domain adaptive panoptic segmentation network that exploits inter-style consistency and inter-task regularization for optimal domain adaptive panoptic segmentation. The inter-style consistency leverages semantic invariance across the same image of the different styles which ‘ fabricates’ certain self-supervisions to guide the network to learn domain-invariant features. The inter-task regularization exploits the complementary nature of instance segmentation and semantic segmentation and uses it as a constraint for better feature alignment across domains. Extensive experiments over multiple domain adaptive panoptic segmentation tasks (e.g. synthetic-to-real and real-to-real) show that our proposed network achieves superior segmentation performance as compared with the state-of-the-art.
PC-GAIN: Pseudo-label conditional generative adversarial imputation networks for incomplete data

Datasets with missing values are very common in real world applications. GAIN, a recently proposed deep generative model for missing data imputation, has been proved to outperform many state-of-the-art methods. But GAIN only uses a reconstruction loss in the generator to minimize the imputation error of the non-missing part, ignoring the potential category information which can reflect the relationship between samples. In this paper, we propose a novel unsupervised missing data imputation method named PC-GAIN, which utilizes potential category information to further enhance the imputation power. Specifically, we first propose a pre-training procedure to learn potential category information contained in a subset of low-missing-rate data. Then an auxiliary classifier is determined using the synthetic pseudo-labels. Further, this classifier is incorporated into the generative adversarial framework to help the generator to yield higher quality imputation results. The proposed method can improve the imputation quality of GAIN significantly. Experimental results on various benchmark datasets show that our method is also superior to other baseline approaches. Our code is available at https://github.com/WYu-Feng/pc-gain .
The Main Catalysts for Collaborative R&amp;D Projects in Dubai Industrial Sector

This study highlights joint activities in Research and Development provided by respective stakeholders in the Dubai industrial sector. The study's importance lies in the fact that it is one of the few studies that address cooperative efforts for industrial R&D in Dubai's Emirate. Systematic reviews analysis was used to identify prior accomplishments on collaborative R&D projects. The analysis started by identifying included terminology and criteria that allowed to search in Google Scholar, ProQuest, Emerald, and ScienceDirect. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA, []) was applied to select the included studies based on the Quality Assessment Checklist []. Thirty-seven articles were filtered into sixteen studies containing the required data. Ultimately, the study identified three catalysts that encourage R&D stakeholders for collaborative activities in Dubai's industry, including Networks, R&D Risk Management, and Shared value. As a result, the research model is proposed for interrelationships between the stakeholders and the identified catalysts towards enhancing collaborative R&D projects in Dubai's industry. These will develop new digital technology solutions.
Context Modeling in 3D Human Pose Estimation: A Unified Perspective

Estimating 3D human pose from a single image suffers from severe ambiguity since multiple 3D joint configurations may have the same 2D projection. The state-of-the-art methods often rely on context modeling methods such as pictorial structure model (PSM) or graph neural network (GNN) to reduce ambiguity. However, there is no study that rigorously compares them side by side. So we first present a general formula for context modeling in which both PSM and GNN are its special cases. By comparing the two methods, we found that the end-to-end training scheme in GNN and the limb length constraints in PSM are two complementary factors to improve results. To combine their advantages, we propose ContextPose based on attention mechanism that allows enforcing soft limb length constraints in a deep network. The approach effectively reduces the chance of getting absurd 3D pose estimates with incorrect limb lengths and achieves state-of-the-art results on two benchmark datasets. More importantly, the introduction of limb length constraints into deep networks enables the approach to achieve much better generalization performance.
The impact of using biased performance metrics on software defect prediction research

Software engineering researchers have undertaken many experiments investigating the potential of software defect prediction algorithms. Unfortunately some widely used performance metrics are known to be problematic, most notably F1, but nevertheless F1 is widely used. To investigate the potential impact of using F1 on the validity of this large body of research. We undertook a systematic review to locate relevant experiments and then extract all pairwise comparisons of defect prediction performance using F1 and the unbiased Matthews correlation coefficient (MCC). We found a total of 38 primary studies. These contain 12,471 pairs of results. Of these comparisons, 21.95% changed direction when the MCC metric is used instead of the biased F1 metric. Unfortunately, we also found evidence suggesting that F1 remains widely used in software defect prediction research. We reiterate the concerns of statisticians that the F1 is a problematic metric outside of an information retrieval context, since we are concerned about both classes (defect-prone and not defect-prone units). This inappropriate usage has led to a substantial number (more than one fifth) of erroneous (in terms of direction) results. Therefore we urge researchers to (i) use an unbiased metric and (ii) publish detailed results including confusion matrices such that alternative analyses become possible.