Reviewerly/exHarmony huggingface dataset:
Heterogeneous Network Representation Learning

Representation learning has offered a revolutionary learning paradigm for various AI domains. In this survey, we examine and review the problem of representation learning with the focus on heterogeneous networks, which consists of different types of vertices and relations. The goal of this problem is to automatically project objects, most commonly, vertices, in an input heterogeneous network into a latent embedding space such that both the structural and relational properties of the network can be encoded and preserved. The embeddings (representations) can be then used as the features to machine learning algorithms for addressing corresponding network tasks. To learn expressive embeddings, current research developments can fall into two major categories: shallow embedding learning and graph neural networks. After a thorough review of the existing literature, we identify several critical challenges that remain unaddressed and discuss future directions. Finally, we build the Heterogeneous Graph Benchmark to facilitate open research for this rapidly-developing topic.
MAVEN: A Massive General Domain Event Detection Dataset

Event detection (ED), which means identifying event trigger words and classifying event types, is the first and most fundamental step for extracting event knowledge from plain text.Most existing datasets exhibit the following issues that limit further development of ED: (1) Data scarcity.Existing smallscale datasets are not sufficient for training and stably benchmarking increasingly sophisticated modern neural methods.(2) Low coverage.Limited event types of existing datasets cannot well cover general-domain events, which restricts the applications of ED models.To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4, 480 Wikipedia documents, 118, 732 event mention instances, and 168 event types.MAVEN alleviates the data scarcity problem and covers much more general event types.We reproduce the recent state-of-the-art ED models and conduct a thorough evaluation on MAVEN.The experimental results show that existing ED methods cannot achieve promising results on MAVEN as on the small datasets, which suggests that ED in the real world remains a challenging task and requires further research efforts.We also discuss further directions for general domain ED with empirical analyses.
Multistage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images

The attention mechanism can refine the extracted feature maps and boost the classification performance of the deep network, which has become an essential technique in computer vision and natural language processing. However, the memory and computational costs of the dot-product attention mechanism increase quadratically with the spatio-temporal size of the input. Such growth hinders the usage of attention mechanisms considerably in application scenarios with large-scale inputs. In this Letter, we propose a Linear Attention Mechanism (LAM) to address this issue, which is approximately equivalent to dot-product attention with computational efficiency. Such a design makes the incorporation between attention mechanisms and deep networks much more flexible and versatile. Based on the proposed LAM, we re-factor the skip connections in the raw U-Net and design a Multi-stage Attention ResU-Net (MAResU-Net) for semantic segmentation from fine-resolution remote sensing images. Experiments conducted on the Vaihingen dataset demonstrated the effectiveness and efficiency of our MAResU-Net. Open-source code is available at https://github.com/lironui/Multistage-Attention-ResU-Net.
A cloud-based supply chain management system: effects on supply chain responsiveness

Purpose Despite the ongoing calls for the incorporation of the cloud utility model, the effect of the cloud on elements of supply chain performance is still an evolving area of research. The purpose of this paper is to develop the architecture of a cloud-based supply chain management (C-SCM) ecosystem and explore how it enhances supply chain responsiveness (SCR). Design/methodology/approach First, the authors discuss the potential benefits that cloud computing can yield, compared to existing mature SCM information systems and solutions through a comprehensive literature review. The authors conceptualise SCR in terms of the level of visibility in the supply chain, supply chain flexibility and rapid detection and reaction to changes, and then the authors build the detailed architecture of a C-SCM system. The proposed ecosystem introduces a view of SCM and the associated practices when transferred to cloud environments. The potential to enhance SCR through the cloud is explored with scenarios on a case of supply chain operations in fashion retail industry. Findings The findings show that the proposed system can enhance all three dimensions of SCR. Implications for supply chain practice and how companies can migrate to a cloud supply chain are drawn. Originality/value Given that the development, creation and delivery of goods and services are increasingly becoming a joint effort of several parties in a supply chain, the authors contribute to the existing literature by introducing a comprehensive C-SCM system and show how companies can enhance their SCR.
Deep Hashing Network for Unsupervised Domain Adaptation

The inadequacy of labeled data in various domains has limited the use of deep learning on several tasks. In many cases it is quite expensive in terms of both time and human effort to collect, annotate, and organize large datasets to train deep neural networks. In recent years, domain adaptation algorithms have been highly successful in leveraging labeled data from related but different datasets to build accurate classification models for unlabeled target datasets. In this chapter we present a deep learning based hashing model for domain adaptation. Hashing techniques are popular in computer vision for their efficiency in both data storage and data retrieval. We use hash-based image feature representations for robust similarity measures between features. We propose a Deep Hashing Network that is trained to learn unique hash codes by leveraging the data from both the labeled source domain and the unlabeled target domain, to correctly classify unlabeled target data. We present a detailed study of several transfer tasks across multiple datasets to corroborate the advantages of our framework.
A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.
The future of mathematics education since COVID-19: humans-with-media or humans-with-non-living-things

The COVID-19 pandemic has changed the agenda of mathematics education. This change will be analyzed by looking at three trends in mathematics education: the use of digital technology, philosophy of mathematics education, and critical mathematics education. Digital technology became a trend in mathematics education in response to the arrival of a different kind of artifact to the mathematics classroom. It was thrust into the spotlight as the pandemic suddenly moved classrooms online around the world. Challenges specific to mathematics education in this context must be addressed. The link between the COVID-19 pandemic and digital technology in education also raises epistemological issues highlighted by philosophy of mathematics education and critical mathematics education. Using the notion that the basic unit of knowledge production throughout history is humans-with-media, I discuss how humans are connected to the virus, how it has laid bare social inequality, and how it will change the agendas of these three trends in mathematics education. I highlight the urgent need to study how mathematics education happens online for children when the home environment and inequalities in access to digital technologies assume such significant roles as classes move on-line. We need to understand the political role of agency of artifacts such as home in collectives of humans-with-media-things, and finally we need to learn how to implement curricula that address social inequalities. This discussion is intertwined with examples.
Deep-learning-enabled dual-frequency composite fringe projection profilometry for single-shot absolute 3D shape measurement

Single-shot high-speed 3D imaging is important for reconstructions of dynamic objects.For fringe projection profilometry (FPP), however, it is still challenging to recover accurate 3D shapes of isolated objects by a single fringe image.In this paper, we demonstrate that the deep neural networks can be trained to directly recover the absolute phase from a unique fringe image that involves spatially multiplexed fringe patterns of different frequencies.The extracted phase is free from spectrum-aliasing problem which is hard to avoid for traditional spatial-multiplexing methods.Experiments on both static and dynamic scenes show that the proposed approach is robust to object motion and can obtain high-quality 3D reconstructions of isolated objects within a single fringe image.
DaViT: Dual Attention Vision Transformers

In this work, we introduce Dual Attention Vision Transformers (DaViT), a simple yet effective vision transformer architecture that is able to capture global context while maintaining computational efficiency. We propose approaching the problem from an orthogonal angle: exploiting self-attention mechanisms with both “spatial tokens” and “channel tokens”. With spatial tokens, the spatial dimension defines the token scope, and the channel dimension defines the token feature dimension. With channel tokens, we have the inverse: the channel dimension defines the token scope, and the spatial dimension defines the token feature dimension. We further group tokens along the sequence direction for both spatial and channel tokens to maintain the linear complexity of the entire model. We show that these two self-attentions complement each other: (i) since each channel token contains an abstract representation of the entire image, the channel attention naturally captures global interactions and representations by taking all spatial positions into account when computing attention scores between channels; (ii) the spatial attention refines the local representations by performing fine-grained interactions across spatial locations, which in turn helps the global information modeling in channel attention. Extensive experiments show DaViT backbones achieve state-of-the-art performance on four different tasks. Specially, DaViT-Tiny, DaViT-Small, and DaViT-Base achieve 82.8%, 84.2%, and 84.6% top-1 accuracy on ImageNet-1K without extra training data, using 28.3M, 49.7M, and 87.9M parameters, respectively. When we further scale up DaViT with 1.5B weakly supervised image and text pairs, DaViT-Giant reaches 90.4% top-1 accuracy on ImageNet-1K. Code is available at https://github.com/microsoft/DaViT.
Factors Affecting Enjoyment of Virtual Reality Games: A Comparison Involving Consumer-Grade Virtual Reality Technology

Objective: This study investigated psychological responses to playing videogames using a virtual reality (VR) head-mounted display (HMD). We also investigated how cybersickness impacts the sense of presence one feels in the virtual environment, as well as how cybersickness affects enjoyment. Materials and Methods: Participants played randomly assigned VR games that varied in the level of sensory conflict they provided: “Lucky's Tale,” “Elite: Dangerous,” and “Minecraft.” Results were compared based on two headset conditions—the Oculus DK2 and the recently released Oculus Rift Consumer Version (CV1). Results: Cybersickness was not reduced by playing games with a VR HMD of higher technological quality—the Oculus Rift CV1. Furthermore, cybersickness responses were significantly different based on the level of sensory conflict in the games. Games with less sensory conflict, such as third-person platformer games, or space and flight simulator games, produce less cybersickness in players than first-person games. Enjoyment of VR games was shown to be directly influenced by a sense of spatial presence, which was affected by interactivity and realism. Findings suggest that the variables that are important to the enjoyment of console, mobile, or motion-based games are consistently important to enjoyment of VR games. Conclusion: Better technology does not affect the frequency or severity of cybersickness for players; but sensory conflict has a significant impact on how sick users become. Additionally, we present a model that indicates how enjoyment is produced in VR gaming experiences. These findings bear further investigation as new methods of interacting with VR games are developed.
Towards a question answering system over the Semantic Web

With the development of the Semantic Web, a lot of new structured data has become available on the Web in the form of knowledge bases (KBs). Making this valuable data accessible and usable for end-users is one of the main goals of question answering (QA) over KBs. Most current QA systems query one KB, in one language (namely English). The existing approaches are not designed to be easily adaptable to new KBs and languages. We first introduce a new approach for translating natural language questions to SPARQL queries. It is able to query several KBs simultaneously, in different languages, and can easily be ported to other KBs and languages. In our evaluation, the impact of our approach is proven using 5 different well-known and large KBs: Wikidata, DBpedia, MusicBrainz, DBLP and Freebase as well as 5 different languages namely English, German, French, Italian and Spanish. Second, we show how we integrated our approach, to make it easily accessible by the research community and by end-users. To summarize, we provide a conceptional solution for multilingual, KB-agnostic question answering over the Semantic Web. The provided first approximation validates this concept.
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

Nonlinear ICA is a fundamental problem for unsupervised representation learning, emphasizing the capacity to recover the underlying latent variables generating the data (i.e., identifiability). Recently, the very first identifiability proofs for nonlinear ICA have been proposed, leveraging the temporal structure of the independent components. Here, we propose a general framework for nonlinear ICA, which, as a special case, can make use of temporal structure. It is based on augmenting the data by an auxiliary variable, such as the time index, the history of the time series, or any other available information. We propose to learn nonlinear ICA by discriminating between true augmented data, or data in which the auxiliary variable has been randomized. This enables the framework to be implemented algorithmically through logistic regression, possibly in a neural network. We provide a comprehensive proof of the identifiability of the model as well as the consistency of our estimation method. The approach not only provides a general theoretical framework combining and generalizing previously proposed nonlinear ICA models and algorithms, but also brings practical advantages.
STUDY ON HERMITIAN GRAPH WAVELETS IN FEATURE DETECTION

The enormous information flow in our day today life, initiates the necessitates of the identifying the valuable data that are to be concentrated. In case of image segmentation and signal processing, the feature detection takes up the role of fixating to the data that are to be focused. Thus directing to the pixels or information that are to be concentrated eliminating the time and the energy wastage in examining the pixels or the information’s that are of least important. The paper is the study, focusing on the advantages of utilizing the Hermitian wavelet transform incorporated with the graph wavelet in the feature detection, leading to an accurate identification of the information to be processed further.
A Secure and Efficient Cloud-Centric Internet-of-Medical-Things-Enabled Smart Healthcare System With Public Verifiability

The potential of the Internet-of-Medical-Things (IoMT) technology for interconnecting the biomedical sensors in e-health has ameliorated the people's living standards. Another technology recognized in the recent e-healthcare is outsourcing the medical data to the cloud. There are, however, several stipulations for adopting these two technologies. The most difficult is the privacy of medical data and the challenge resulting from the resource constraint environment of sensor devices. In this article, we present the state-of-the-art secure and efficient cloud-centric IoMT-enabled smart healthcare system with public verifiability. The system novelty implements an escrow-free identity-based aggregate signcryption (EF-IDASC) scheme to secure data transmission, which is also proposed in this article. The proposed smart healthcare system fetches the medical data from multiple sensors implanted on the patient's body, signcrypts and aggregates them under the proposed EF-IDASC scheme, and outsources the data on the medical cloud server via smartphone. The system does not reveal any information about the identity and medical data of the patient. We further analyze the performance of the proposed smart healthcare system in terms of energy consumption. Moreover, we compare the performance of the proposed EF-IDASC scheme with other related schemes.
A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection

Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reflect the shape and direction characteristics of arbitrary-oriented objects, but also have high parameter-tuning efforts. In this paper, a novel AOOD method called General Gaussian Heatmap Label Assignment (GGHL) is proposed. Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects. Based on OLA, an oriented-bounding-box (OBB) representation component (ORC) is developed to indicate OBBs and adjust the Gaussian center prior weights to fit the characteristics of different objects adaptively through neural network learning. Moreover, a joint-optimization loss (JOL) with area normalization and dynamic confidence weighting is designed to refine the misalign optimal results of different subtasks. Extensive experiments on public datasets demonstrate that the proposed GGHL improves the AOOD performance with low parameter-tuning and time costs. Furthermore, it is generally applicable to most AOOD methods to improve their performance including lightweight models on embedded platforms.
Data augmentation: A comprehensive survey of modern approaches

To ensure good performance, modern machine learning models typically require large amounts of quality annotated data. Meanwhile, the data collection and annotation processes are usually performed manually, and consume a lot of time and resources. The quality and representativeness of curated data for a given task is usually dictated by the natural availability of clean data in the particular domain as well as the level of expertise of developers involved. In many real-world application settings it is often not feasible to obtain sufficient training data. Currently, data augmentation is the most effective way of alleviating this problem. The main goal of data augmentation is to increase the volume, quality and diversity of training data. This paper presents an extensive and thorough review of data augmentation methods applicable in computer vision domains. The focus is on more recent and advanced data augmentation techniques. The surveyed methods include deeply learned augmentation strategies as well as feature-level and meta-learning-based data augmentation techniques. Data synthesis approaches based on realistic 3D graphics modeling, neural rendering, and generative adversarial networks are also covered. Different from previous surveys, we cover a more extensive array of modern techniques and applications. We also compare the performance of several state-of-the-art augmentation methods and present a rigorous discussion of the effectiveness of various techniques in different scenarios of use based on performance results on different datasets and tasks.
Digital Whole Slide Imaging Compared With Light Microscopy for Primary Diagnosis in Surgical Pathology

The adoption of digital capture of pathology slides as whole slide images (WSI) for educational and research applications has proven utility.To compare pathologists' primary diagnoses derived from WSI versus the standard microscope. Because WSIs differ in format and method of observation compared with the current standard glass slide microscopy, this study is critical to potential clinical adoption of digital pathology.The study enrolled a total of 2045 cases enriched for more difficult diagnostic categories and represented as 5849 slides were curated and provided for diagnosis by a team of 19 reading pathologists separately as WSI or as glass slides viewed by light microscope. Cases were reviewed by each pathologist in both modalities in randomized order with a minimum 31-day washout between modality reads for each case. Each diagnosis was compared with the original clinical reference diagnosis by an independent central adjudication review.The overall major discrepancy rates were 3.64% for WSI review and 3.20% for manual slide review diagnosis methods, a difference of 0.44% (95% CI, -0.15 to 1.03). The time to review a case averaged 5.20 minutes for WSI and 4.95 minutes for glass slides. There was no specific subset of diagnostic category that showed higher rates of modality-specific discrepancy, though some categories showed greater discrepancy than others in both modalities.WSIs are noninferior to traditional glass slides for primary diagnosis in anatomic pathology.