Reviewerly/exHarmony huggingface dataset:
A systematic review on hand gesture recognition techniques, challenges and applications

Background With the development of today’s technology, and as humans tend to naturally use hand gestures in their communication process to clarify their intentions, hand gesture recognition is considered to be an important part of Human Computer Interaction (HCI), which gives computers the ability of capturing and interpreting hand gestures, and executing commands afterwards. The aim of this study is to perform a systematic literature review for identifying the most prominent techniques, applications and challenges in hand gesture recognition. Methodology To conduct this systematic review, we have screened 560 papers retrieved from IEEE Explore published from the year 2016 to 2018, in the searching process keywords such as “hand gesture recognition” and “hand gesture techniques” have been used. However, to focus the scope of the study 465 papers have been excluded. Only the most relevant hand gesture recognition works to the research questions, and the well-organized papers have been studied. Results The results of this paper can be summarized as the following; the surface electromyography (sEMG) sensors with wearable hand gesture devices were the most acquisition tool used in the work studied, also Artificial Neural Network (ANN) was the most applied classifier, the most popular application was using hand gestures for sign language, the dominant environmental surrounding factor that affected the accuracy was the background color, and finally the problem of overfitting in the datasets was highly experienced. Conclusions The paper will discuss the gesture acquisition methods, the feature extraction process, the classification of hand gestures, the applications that were recently proposed, the challenges that face researchers in the hand gesture recognition process, and the future of hand gesture recognition. We shall also introduce the most recent research from the year 2016 to the year 2018 in the field of hand gesture recognition for the first time.
"Blockchain Research in Information Systems: Current Trends and an Inclusive Future Research Agenda"

The potential of blockchain has been extensively discussed in practitioner literature, yet rigorous empirical and theory-driven information systems (IS) research on blockchain remains scarce. This special issue addresses the need for innovative research that offers a fresh look at the opportunities and challenges of blockchain. This editorial integrates and goes beyond the papers included in this special issue by providing a framework for blockchain research in IS that emphasizes two important issues. First, we direct the attention of IS research toward the blockchain protocol level, which is characterized by recursive interactions between human agents and the blockchain protocol. Second, we highlight the need for IS research to consider how the protocol level constrains and affords blockchain applications, and how these constraints and other concerns at the application level lead to changes at the protocol level. Rooted in a socio-material view of IS, we offer a multi-paradigmatic IS research agenda that underscores the need for behavioral (individual, group, and organizational), design science, and IS economics research on blockchain. Our research agenda emphasizes issues of blockchain governance, human and material agency, blockchain affordances and constraints, as well as the consequences of its use.
Current landscape and influence of big data on finance

Abstract Big data is one of the most recent business and technical issues in the age of technology. Hundreds of millions of events occur every day. The financial field is deeply involved in the calculation of big data events. As a result, hundreds of millions of financial transactions occur in the financial world each day. Therefore, financial practitioners and analysts consider it an emerging issue of the data management and analytics of different financial products and services. Also, big data has significant impacts on financial products and services. Therefore, identifying the financial issues where big data has a significant influence is also an important issue to explore with the influences. Based on these concepts, the objective of this paper was to show the current landscape of finance dealing with big data, and also to show how big data influences different financial sectors, more specifically, its impact on financial markets, financial institutions, and the relationship with internet finance, financial management, internet credit service companies, fraud detection, risk analysis, financial application management, and so on. The connection between big data and financial-related components will be revealed in an exploratory literature review of secondary data sources. Since big data in the financial field is an extremely new concept, future research directions will be pointed out at the end of this study.
Graph Contrastive Learning with Augmentations

Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been developed for convolutional neural networks (CNNs) for image data, self-supervised learning and pre-training are less explored for GNNs. In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data. We first design four types of graph augmentations to incorporate various priors. We then systematically study the impact of various combinations of graph augmentations on multiple datasets, in four different settings: semi-supervised, unsupervised, and transfer learning as well as adversarial attacks. The results show that, even without tuning augmentation extents nor using sophisticated GNN architectures, our GraphCL framework can produce graph representations of similar or better generalizability, transferrability, and robustness compared to state-of-the-art methods. We also investigate the impact of parameterized graph augmentation extents and patterns, and observe further performance gains in preliminary experiments. Our codes are available at this https URL.
Towards an Infrastructure Enabling the Internet of Production

New levels of cross-domain collaboration between manufacturing companies throughout the supply chain are anticipated to bring benefits to both suppliers and consumers of products. Enabling a fine-grained sharing and analysis of data among different stakeholders in an automated manner, such a vision of an Internet of Production (IoP) introduces demanding challenges to the communication, storage, and computation infrastructure in production environments. In this work, we present three example cases that would benefit from an IoP (a fine blanking line, a high pressure die casting process, and a connected job shop) and derive requirements that cannot be met by today's infrastructure. In particular, we identify three orthogonal research objectives: (i) real-time control of tightly integrated production processes to offer seamless low-latency analysis and execution, (ii) storing and processing heterogeneous production data to support scalable data stream processing and storage, and (iii) secure privacy-aware collaboration in production to provide a basis for secure industrial collaboration. Based on a discussion of state-of-the-art approaches for these three objectives, we create a blueprint for an infrastructure acting as an enabler for an IoP.
Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

Breast cancer is the second most common cause of cancer-related deaths among women.Early detection leads to better prognosis and saves lives.The 5-year survival rate of breast cancer is 99% if it is located only in breast.Conventional computer-aided diagnosis (CADx) systems for breast cancer use the single view information of mammograms to assist the radiologists.More recent work has focused on more than one views.Existing multi-view based CADx systems normally employ only two views namely Cranio-Caudal (CC) and Medio-Lateral-Oblique (MLO).The information fusion of the two views proved the effectiveness of the system for mammogram classification which cannot be achieved by single view information.However, combining the information of four views of mammograms increases the performance of classification.In this study, we propose Multi-View Feature Fusion (MVFF) based CADx system using feature fusion technique of four views for classification of mammogram.The complete CADx tool contains three stages, the first stage have the ability to classify mammogram into abnormal or normal, second stage is about classification of mass or calcification and in the final stage classification of malignant or benign classification is performed.Convolutional Neural Network (CNN) based feature extraction models operate on each view separately.These extracted features were fused into one final layer for ultimate prediction.Our proposed system is trained on four views of mammograms, after data augmentation.We performed our experiments on publicly available datasets such as CBIS-DDSM (Curated Breast Imaging Subset of DDSM) and mini-MIAS database of mammograms.In comparison with literature the MVFF based system is performed better than a single view-based system for mammogram classification.We have achieved area under ROC curve (AUC) of 0.932 for mass and calcification and 0.84 for malignant and benign, which is higher than all single-view based systems.The value of AUC for normal and abnormal classification is 0.93.
Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification

Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of discriminative features for each class. In order to overcome these challenges, we propose to use cross-modality attention with semantic graph embedding for multi-label classification. Based on the constructed label graph, we propose an adjacency-based similarity graph embedding method to learn semantic label embeddings, which explicitly exploit label relationships. Then our novel cross-modality attention maps are generated with the guidance of learned label embeddings. Experiments on two multi-label image classification datasets (MS-COCO and NUS-WIDE) show our method outperforms other existing state-of-the-arts. In addition, we validate our method on a large multi-label video classification dataset (YouTube-8M Segments) and the evaluation results demonstrate the generalization capability of our method.
Few-Shot Action Recognition with Permutation-Invariant Attention

Many few-shot learning models focus on recognising images. In contrast, we tackle a challenging task of few-shot action recognition from videos. We build on a C3D encoder for spatio-temporal video blocks to capture short-range action patterns. Such encoded blocks are aggregated by permutation-invariant pooling to make our approach robust to varying action lengths and long-range temporal dependencies whose patterns are unlikely to repeat even in clips of the same class. Subsequently, the pooled representations are combined into simple relation descriptors which encode so-called query and support clips. Finally, relation descriptors are fed to the comparator with the goal of similarity learning between query and support clips. Importantly, to re-weight block contributions during pooling, we exploit spatial and temporal attention modules and self-supervision. In naturalistic clips (of the same class) there exists a temporal distribution shift–the locations of discriminative temporal action hotspots vary. Thus, we permute blocks of a clip and align the resulting attention regions with similarly permuted attention regions of non-permuted clip to train the attention mechanism invariant to block (and thus long-term hotspot) permutations. Our method outperforms the state of the art on the HMDB51, UCF101, miniMIT datasets.
Probing Pretrained Language Models for Lexical Semantics

The success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge they implicitly capture.While prior research focused on morphosyntactic, semantic, and world knowledge, it remains unclear to which extent LMs also derive lexical type-level knowledge from words in context.In this work, we present a systematic empirical analysis across six typologically diverse languages and five different lexical tasks, addressing the following questions: 1) How do different lexical knowledge extraction strategies (monolingual versus multilingual source LM, out-ofcontext versus in-context encoding, inclusion of special tokens, and layer-wise averaging) impact performance?How consistent are the observed effects across tasks and languages?2) Is lexical knowledge stored in few parameters, or is it scattered throughout the network?3) How do these representations fare against traditional static word vectors in lexical tasks?4) Does the lexical information emerging from independently trained monolingual LMs display latent similarities?Our main results indicate patterns and best practices that hold universally, but also point to prominent variations across languages and tasks.Moreover, we validate the claim that lower Transformer layers carry more type-level lexical knowledge, but also show that this knowledge is distributed across multiple layers.
MPViT: Multi-Path Vision Transformer for Dense Prediction

Dense computer vision tasks such as object detection and segmentation require effective multi-scale feature representation for detecting or classifying objects or regions with varying sizes. While Convolutional Neural Networks (CNNs) have been the dominant architectures for such tasks, recently introduced Vision Transformers (ViTs) aim to replace them as a backbone. Similar to CNNs, ViTs build a simple multi-stage structure (i.e., fine-to-coarse) for multi-scale representation with single-scale patches. In this work, with a different perspective from existing Transformers, we explore multi-scale patch embedding and multi-path structure, constructing the Multi-Path Vision Transformer (MPViT). MPViT embeds features of the same size (i.e., sequence length) with patches of different scales simultaneously by using overlapping convolutional patch embedding. Tokens of different scales are then independently fed into the Transformer encoders via multiple paths and the resulting features are aggregated, enabling both fine and coarse feature representations at the same feature level. Thanks to the diverse, multi-scale feature representations, our MPViTs scaling from tiny (5M) to base (73M) consistently achieve superior performance over state-of-the-art Vision Transformers on ImageNet classification, object detection, instance segmentation, and semantic segmentation.
Ki67 reproducibility using digital image analysis: an inter-platform and inter-operator study

Ki67 expression has been a valuable prognostic variable in breast cancer, but has not seen broad adoption due to lack of standardization between institutions. Automation could represent a solution. Here we investigate the reproducibility of Ki67 measurement between three image analysis platforms with supervised classifiers performed by the same operator, by multiple operators, and finally we compare their accuracy in prognostic potential. Two breast cancer patient cohorts were used for this study. The standardization was done with the 30 cases of ER+ breast cancer that were used in phase 3 of International Ki67 in Breast Cancer Working Group initiatives where blocks were centrally cut and stained for Ki67. The outcome cohort was from 149 breast cancer cases from the Yale Pathology archives. A tissue microarray was built from representative tissue blocks with median follow-up of 120 months. The Mib-1 antibody (Dako) was used to detect Ki67 (dilution 1:100). HALO (IndicaLab), QuantCenter (3DHistech), and QuPath (open source software) digital image analysis (DIA) platforms were used to evaluate Ki67 expression. Intraclass correlation coefficient (ICC) was used to measure reproducibility. Between-DIA platform reproducibility was excellent (ICC: 0.933, CI: 0.879-0.966). Excellent reproducibility was found between all DIA platforms and the reference standard Ki67 values of Spectrum Webscope (QuPath-Spectrum Webscope ICC: 0.970, CI: 0.936-0.986; HALO-Spectrum Webscope ICC: 0.968, CI: 0.933-0.985; QuantCenter-Spectrum Webscope ICC: 0.964, CI: 0.919-0.983). All platforms showed excellent intra-DIA reproducibility (QuPath ICC: 0.992, CI: 0.986-0.996; HALO ICC: 0.972, CI: 0.924-0.988; QuantCenter ICC: 0.978, CI: 0.932-0.991). Comparing each DIA against outcome, the hazard ratios were similar. The inter-operator reproducibility was particularly high (ICC: 0.962-0.995). Our results showed outstanding reproducibility both within and between-DIA platforms, including one freely available DIA platform (QuPath). We also found the platforms essentially indistinguishable with respect to prediction of breast cancer patient outcome. Results justify multi-institutional DIA studies to assess clinical utility.
Towards Ghost-Free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN

Shadow removal is an essential task for scene understanding. Many studies consider only matching the image contents, which often causes two types of ghosts: color in-consistencies in shadow regions or artifacts on shadow boundaries (as shown in Figure. 1). In this paper, we tackle these issues in two ways. First, to carefully learn the border artifacts-free image, we propose a novel network structure named the dual hierarchically aggregation network (DHAN). It contains a series of growth dilated convolutions as the backbone without any down-samplings, and we hierarchically aggregate multi-context features for attention and prediction, respectively. Second, we argue that training on a limited dataset restricts the textural understanding of the network, which leads to the shadow region color in-consistencies. Currently, the largest dataset contains 2k+ shadow/shadow-free image pairs. However, it has only 0.1k+ unique scenes since many samples share exactly the same background with different shadow positions. Thus, we design a shadow matting generative adversarial network (SMGAN) to synthesize realistic shadow mattings from a given shadow mask and shadow-free image. With the help of novel masks or scenes, we enhance the current datasets using synthesized shadow images. Experiments show that our DHAN can erase the shadows and produce high-quality ghost-free images. After training on the synthesized and real datasets, our network outperforms other state-of-the-art methods by a large margin. The code is available: http://github.com/vinthony/ghost-free-shadow-removal/
Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review

Objectives:Patient representation learning refers to learning a dense mathematical representation of a patient that encodes meaningful information from Electronic Health Records (EHRs).This is generally performed using advanced deep learning methods.This study presents a systematic review of this field and provides both qualitative and quantitative analyses from a methodological perspective. Methods:We identified studies developing patient representations from EHRs with deep learning methods from MEDLINE, EMBASE, Scopus, the Association for Computing Machinery (ACM) Digital Library, and the Institute of Electrical and Electronics Engineers (IEEE) Xplore Digital Library.After screening 363 articles, 49 papers were included for a comprehensive data collection. Results:Publications developing patient representations almost doubled each year from 2015 until 2019.We noticed a typical workflow starting with feeding raw data, applying deep learning models, and ending with clinical outcome predictions as evaluations of the learned representations.Specifically, learning representations from structured EHR data was dominant (37 out of 49 studies).Recurrent Neural Networks were widely applied as the deep learning architecture (Long short-term memory: 13 studies, Gated recurrent unit: 11 studies).Learning was mainly performed in a supervised manner (30 studies) optimized with cross-entropy loss.Disease prediction was the most common application and evaluation (31 studies).Benchmark datasets were mostly unavailable (28 studies) due to privacy concerns of EHR data, and code availability was assured in 20 studies. Discussion & Conclusion:The existing predictive models mainly focus on the prediction of single diseases, rather than considering the complex mechanisms of patients from a holistic review.We show the importance and feasibility of learning comprehensive representations of patient EHR data through a systematic review.Advances in patient representation learning techniques will be essential for powering patient-level EHR analyses.Future work will still be devoted to leveraging the richness and potential of available EHR data.Reproducibility and transparency of reported results will hopefully improve.Knowledge distillation and advanced learning techniques will be exploited to assist the capability of learning patient representation further.
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization

In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SIMCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i.e., quality estimation) assisted by contrastive learning.Experimental results show that, with minor modification over existing topscoring systems, SimCLS can improve the performance of existing top-performing models by a large margin.Particularly, 2.51 absolute improvement against BART (Lewis et al., 2020) and2.50 over PEGASUS (Zhang et al., 2020a) w.r.t ROUGE-1 on the CNN/DailyMail dataset, driving the state-of-the-art performance to a new level.We have open-sourced our codes and results: https://github.com/yixinL7/SimCLS.Results of our proposed models have been deployed into EX-PLAINABOARD (Liu et al., 2021a) platform, which allows researchers to understand our systems in a more fine-grained way.
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer

A few-shot semantic segmentation model is typically composed of a CNN encoder, a CNN decoder and a simple classifier (separating foreground and background pixels). Most existing methods meta-learn all three model components for fast adaptation to a new class. However, given that as few as a single support set image is available, effective model adaption of all three components to the new class is extremely challenging. In this work we propose to simplify the meta-learning task by focusing solely on the simplest component – the classifier, whilst leaving the en-coder and decoder to pre-training. We hypothesize that if we pretrain an off-the-shelf segmentation model over a set of diverse training classes with sufficient annotations, the encoder and decoder can capture rich discriminative features applicable for any unseen classes, rendering the sub-sequent meta-learning stage unnecessary. For the classifier meta-learning, we introduce a Classifier Weight Transformer (CWT) designed to dynamically adapt the support-set trained classifier’s weights to each query image in an inductive way. Extensive experiments on two standard bench-marks show that despite its simplicity, our method outperforms the state-of-the-art alternatives, often by a large margin. Code is available on https://github.com/zhiheLu/CWT-for-FSS.