Reviewerly/exHarmony huggingface dataset:
Improving Convolutional Networks With Self-Calibrated Convolutions

Recent advances on CNNs are mostly devoted to designing more complex architectures to enhance their representation learning capacity. In this paper, we consider how to improve the basic convolutional feature transformation process of CNNs without tuning the model architectures. To this end, we present a novel self-calibrated convolutions that explicitly expand fields-of-view of each convolutional layers through internal communications and hence enrich the output features. In particular, unlike the standard convolutions that fuse spatial and channel-wise information using small kernels (e.g., 3×3), self-calibrated convolutions adaptively build long-range spatial and inter-channel dependencies around each spatial location through a novel self-calibration operation. Thus, it can help CNNs generate more discriminative representations by explicitly incorporating richer information. Our self-calibrated convolution design is simple and generic, and can be easily applied to augment standard convolutional layers without introducing extra parameters and complexity. Extensive experiments demonstrate that when applying self-calibrated convolutions into different backbones, our networks can significantly improve the baseline models in a variety of vision tasks, including image recognition, object detection, instance segmentation, and keypoint detection, with no need to change the network architectures. We hope this work could provide a promising way for future research in designing novel convolutional feature transformations for improving convolutional networks. Code is available on the project page.
Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda

This study presents a systematic literature review (SLR) of research on blockchain applications in the healthcare domain. The review incorporated 42 articles presenting state-of-the-art knowledge on current implications and gaps pertaining to the use of blockchain technology for improving healthcare processes. The SLR findings indicate that blockchain is being used to develop novel and advanced interventions to improve the prevalent standards of handling, sharing, and processing of medical data and personal health records. The application of blockchain technology is undergoing a conceptual evolution in the healthcare industry where it has added significant value through improved efficiency, access control, technological advancement, privacy protection, and security of data management processes. The findings also suggest that the extant limitations primarily pertain to model performance, as well as the constraints and costs associated with implementation. An integrated framework is presented to address potential areas wherein future researchers can contribute significant value, including addressing concerns regarding regulatory compliance, system architecture, and data protection. Finally, the SLR suggests that future research can facilitate the widespread deployment of blockchain applications to address critical issues related to medical diagnostics, legal compliance, avoiding fraud, and improving patient care in cases of remote monitoring or emergencies.
YOLACT++ Better Real-Time Instance Segmentation

We present a simple, fully-convolutional model for real-time ( fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Moreover, we obtain this result after training on only one GPU. We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty. Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time.
Supply chain transparency through blockchain-based traceability: An overview with demonstration

Traceability can be referred to as the ability to track and trace information. Application of traceability can create transparency in supply chains. Conventionally available, centralized traceability solutions are not preferable for supply chains as they are exposed to many problems such as data manipulations, single point of failure, etc. Blockchain, the recently emerged distributed ledger technology, is gaining popularity with its tremendous applications in various fields, particularly in supply chain management. Technically, blockchain is a decentralized and distributed database where information can be securely recorded. Blockchain-based traceability solutions can tackle the shortcomings of centralized traceability solutions. Firms have already started incorporating blockchain into their supply chain activities in order to improve the transparency through tracking and tracing the events. This paper ultimately aims to present an overview of the various blockchain-based traceability solutions reported in the literature. Primarily, this work provides an insight on the possibilities of blockchain traceability solutions in making a supply chain transparent. Apart from this, it analyses how blockchain traceability solutions affect the visibility of various supply chain distribution network designs, and gives an outline on how technologies such as the Internet of Things (IoT), and smart contracts elevate the opportunities of blockchain. In order to demonstrate how blockchain traceability solutions improve supply chain transparency, a Proof of Concept (PoC) for a cold chain scenario is presented using Microsoft Azure Blockchain Workbench.
Blockchain technology adoption, architecture, and sustainable agri-food supply chains

The emerging Information and Communication Technology play a vital role in electronic agriculture. The application of such technologies as blockchain, Internet-of-Things, wireless sensor networks, cloud computing, and machine learning can improve the agri-food supply chain efficiency and quality management. However, there is little evidence on the determinants of blockchain integrated ICTs adoption for the supply chain actors and their propensity to embrace and use such technologies. To bridge this research gap in the literature, we identify a few potential drivers of blockchain technology adoption, considering the grape wine supply chain and employing a rating-based conjoint analysis. The study finds that dis-intermediation, traceability, price, trust, compliance, and coordination and control in order of their relative importance and utilities can influence the supply chain actors’ adoption-intention decision processes. The adoption factors further rationalize the design for a modular, scalable, interoperable, and cost-effective architecture for supply chain integration and sustainability.
Suppress and Balance: A Simple Gated Network for Salient Object Detection

Most salient object detection approaches use U-Net or feature pyramid networks (FPN) as their basic structures. These methods ignore two key problems when the encoder exchanges information with the decoder: one is the lack of interference control between them, the other is without considering the disparity of the contributions of different encoder blocks. In this work, we propose a simple gated network (GateNet) to solve both issues at once. With the help of multilevel gate units, the valuable context information from the encoder can be optimally transmitted to the decoder. We design a novel gated dual branch structure to build the cooperation among different levels of features and improve the discriminability of the whole network. Through the dual branch design, more details of the saliency map can be further restored. In addition, we adopt the atrous spatial pyramid pooling based on the proposed “Fold” operation (Fold-ASPP) to accurately localize salient objects of various scales. Extensive experiments on five challenging datasets demonstrate that the proposed model performs favorably against most state-of-the-art methods under different evaluation metrics.
Privacy-Preserving Machine Learning: Threats and Solutions

For privacy concerns to be addressed adequately in today's machine-learning (ML) systems, the knowledge gap between the ML and privacy communities must be bridged. This article aims to provide an introduction to the intersection of both fields with special emphasis on the techniques used to protect the data.
RGB-T object tracking: Benchmark and baseline

RGB-Thermal (RGB-T) object tracking receives more and more attention due to the strongly complementary benefits of thermal information to visible data. However, RGB-T research is limited by lacking a comprehensive evaluation platform. In this paper, we propose a large-scale video benchmark dataset for RGB-T tracking. It has three major advantages over existing ones: 1) Its size is sufficiently large for large-scale performance evaluation (total number of frames: 234K, maximum number of frames per sequence: 8K). 2) The alignment between RGB-T sequence pairs is highly accurate, which does not need pre- or post-processing. 3) The occlusion levels are annotated for occlusion-sensitive performance analysis of different tracking algorithms. Moreover, we propose a novel graph-based approach to learn a robust object representation for RGB-T tracking. In particular, the tracked object is represented with a graph with image patches as nodes. Given initial weights of nodes, this graph including graph structure, node weights and edge weights is dynamically learned in a unified optimization framework. Extensive experiments on the large-scale dataset are executed to demonstrate the effectiveness of the proposed tracker against other state-of-the-art tracking methods. We also provide new insights and potential research directions to the field of RGB-T object tracking.
Entangled Transformer for Image Captioning

In image captioning, the typical attention mechanisms are arduous to identify the equivalent visual signals especially when predicting highly abstract words. This phenomenon is known as the semantic gap between vision and language. This problem can be overcome by providing semantic attributes that are homologous to language. Thanks to the inherent recurrent nature and gated operating mechanism, Recurrent Neural Network (RNN) and its variants are the dominating architectures in image captioning. However, when designing elaborate attention mechanisms to integrate visual inputs and semantic attributes, RNN-like variants become unflexible due to their complexities. In this paper, we investigate a Transformer-based sequence modeling framework, built only with attention layers and feedforward layers. To bridge the semantic gap, we introduce EnTangled Attention (ETA) that enables the Transformer to exploit semantic and visual information simultaneously. Furthermore, Gated Bilateral Controller (GBC) is proposed to guide the interactions between the multimodal information. We name our model as ETA-Transformer. Remarkably, ETA-Transformer achieves state-of-the-art performance on the MSCOCO image captioning dataset. The ablation studies validate the improvements of our proposed modules.
A survey of blockchain consensus algorithms performance evaluation criteria

How to reach an agreement in a blockchain network is a complex and important task that is defined as a consensus problem and has wide applications in reality including distributed computing, load balancing, and transaction validation in blockchains. Over recent years, many studies have been done to cope with this problem. In this paper, a comparative and analytical review on the state-of-the-art blockchain consensus algorithms is presented to enlighten the strengths and constraints of each algorithm. Based on their inherent specifications, each algorithm has a different domain of applicability that yields to propose several performance criteria for the evaluation of these algorithms. To overview and provide a basis of comparison for further work in the field, a set of incommensurable and conflicting performance evaluation criteria is identified and weighted by the pairwise comparison method. These criteria are classified into four categories including algorithms’ throughput, the profitability of mining, degree of decentralization and consensus algorithms vulnerabilities and security issues. Based on the proposed framework, the pros and cons of consensus algorithms are systematically analyzed and compared in order to provide a deep understanding of the existing research challenges and clarify the future study directions.
Equalization Loss for Long-Tailed Object Recognition

Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on large vocabulary and long-tailed datasets, e.g. LVIS. In this work, we analyze this problem from a novel perspective: each positive sample of one category can be seen as a negative sample for other categories, making the tail categories receive more discouraging gradients. Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories. The equalization loss protects the learning of rare categories from being at a disadvantage during the network parameter updating. Thus the model is capable of learning better discriminative features for objects of rare classes. Without any bells and whistles, our method achieves AP gains of 4.1% and 4.8% for the rare and common categories on the challenging LVIS benchmark, compared to the Mask R-CNN baseline. With the utilization of the effective equalization loss, we finally won the 1st place in the LVIS Challenge 2019. Code has been made available at: https://github.com/tztztztztz/eql.detectron2.
A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean

This study develops a novel metaheuristic algorithm that is motivated by the behavior of jellyfish in the ocean and is called artificial Jellyfish Search (JS) optimizer. The simulation of the search behavior of jellyfish involves their following the ocean current, their motions inside a jellyfish swarm (active motions and passive motions), a time control mechanism for switching among these movements, and their convergences into jellyfish bloom. JS optimizer is tested using a comprehensive set of mathematical benchmark functions and applied to a series of structural engineering problems. Fifty small/average-scale and twenty-five large-scale functions involving various dimensions were used to validate JS optimizer, which was compared with ten well-known metaheuristic algorithms. JS optimizer was found to outperform those algorithms in solving mathematical benchmark functions. The JS algorithm was then used to solve structural optimization problems, including 25-bar tower design, 52-bar tower design and 582-bar tower design problems. In those cases, JS not only performed best but also required the fewest evaluations of objective functions. Therefore, JS is potentially an excellent metaheuristic algorithm for solving optimization problems.
Principles and Practice of Explainable Machine Learning

Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However, such a highly positive impact is coupled with a significant challenge: how do we understand the decisions suggested by these systems in order that we can trust them? In this report, we focus specifically on data-driven methods-machine learning (ML) and pattern recognition models in particular-so as to survey and distill the results and observations from the literature. The purpose of this report can be especially appreciated by noting that ML models are increasingly deployed in a wide range of businesses. However, with the increasing prevalence and complexity of methods, business stakeholders in the very least have a growing number of concerns about the drawbacks of models, data-specific biases, and so on. Analogously, data science practitioners are often not aware about approaches emerging from the academic literature or may struggle to appreciate the differences between different methods, so end up using industry standards such as SHAP. Here, we have undertaken a survey to help industry practitioners (but also data scientists more broadly) understand the field of explainable machine learning better and apply the right tools. Our latter sections build a narrative around a putative data scientist, and discuss how she might go about explaining her models by asking the right questions. From an organization viewpoint, after motivating the area broadly, we discuss the main developments, including the principles that allow us to study transparent models vs. opaque models, as well as model-specific or model-agnostic post-hoc explainability approaches. We also briefly reflect on deep learning models, and conclude with a discussion about future research directions.
What is XR? Towards a Framework for Augmented and Virtual Reality

Augmented Reality (AR), Virtual Reality (VR), Mixed Reality, and Extended Reality (often – misleadingly – abbreviated as XR) are commonly used terms to describe how technologies generate or modify reality. However, academics and professionals have been inconsistent in their use of these terms. This has led to conceptual confusion and unclear demarcations. Inspired by prior research and qualitative insights from XR professionals, we discuss the meaning and definitions of various terms and organize them in our proposed framework. As a result, we conclude that (1) XR should not be used to connote extended reality, but as a more open approach where the X implies the unknown variable: xReality; (2) AR and VR have fundamental differences and thus should be treated as different experiences; (3) AR experiences can be described on a continuum ranging from assisted reality to mixed reality (based on the level of local presence); and (4), VR experiences can be conceptualized on a telepresence-continuum ranging from atomistic to holistic VR.
Data Augmentation Using Random Image Cropping and Patching for Deep CNNs

Deep convolutional neural networks (CNNs) have achieved remarkable results in image processing tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. Recent CNN architectures with more parameters are rendering traditional data augmentation techniques insufficient. In this study, we propose a new data augmentation technique called random image cropping and patching (RICAP) which randomly crops four images and patches them to create a new training image. Moreover, RICAP mixes the class labels of the four images, resulting in an advantage similar to label smoothing. We evaluated RICAP with current state-of-the-art CNNs (e.g., the shake-shake regularization model) by comparison with competitive data augmentation techniques such as cutout and mixup. RICAP achieves a new state-of-the-art test error of $2.19\%$ on CIFAR-10. We also confirmed that deep CNNs with RICAP achieve better results on classification tasks using CIFAR-100 and ImageNet and an image-caption retrieval task using Microsoft COCO.
Blockchain technology in supply chain management for sustainable performance: Evidence from the airport industry

This paper investigates the major implications of blockchain technology for operations management (OM) with a focus on the decision-making processes in supply chain management (SCM) from the perspective of sustainable performance. The links between blockchain technology, OM, and sustainability issues within SCM are analysed. This two-step research study includes a broad review of the main contributions in the literature that have focused on blockchain technology and OM in SCM. It covers the airport industry from the perspective of sustainable performance and data analysis by reading and processing financial statements, non-financial reports, and the website of one strategic airport infrastructure in southern Italy. The Italian airport infrastructure investigated successfully adopted the Airport Collaborative Decision Making (A-CDM) platform. This is one of the main blockchain technology applications in the airport industry. It promotes cooperation between the main players in the aviation industry and the air traffic controllers (ATCs) to reduce fragmentation, inefficiency, and uncoordinated operations. It also allows information and data sharing, but it is still not possible to observe a high level of sustainable performance. Although the adoption of blockchain technology presents numerous benefits, especially in improving OM, these new technological solutions do not guarantee the achievement of the best performance in terms of effectiveness, efficiency, and sustainability issues. Managers and policy makers need to work together to create a real forum within their collaborative network in which there is a common culture and mutual trust. This article adds an interesting reading of blockchain technology to the existing research with concerns about OM and sustainability issues within the airport setting in Italy.
Overview and comparative study of dimensionality reduction techniques for high dimensional data

The recent developments in the modern data collection tools, techniques, and storage capabilities are leading towards huge volume of data. The dimensions of data indicate the number of features that have been measured for each observation. It has become a challenging task to analyze high dimensional data. Different dimensionality reduction techniques are available in literature to eliminate irrelevant and redundant features. Selection of an appropriate dimension reduction technique can help to enhance the processing speed and reduce the time and effort required to extract valuable information. This paper presents the state-of-the art dimensionality reduction techniques and their suitability for different types of data and application areas. Furthermore, the issues of dimensionality reduction techniques have been highlighted that can affect the accuracy and relevance of results.
Relation-Aware Graph Attention Network for Visual Question Answering

In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the interactive dynamics between different objects. We propose a Relation-aware Graph Attention Network (ReGAT), which encodes each image into a graph and models multi-type inter-object relations via a graph attention mechanism, to learn question-adaptive relation representations. Two types of visual object relations are explored: (i) Explicit Relations that represent geometric positions and semantic interactions between objects; and (ii) Implicit Relations that capture the hidden dynamics between image regions. Experiments demonstrate that ReGAT outperforms prior state-of-the-art approaches on both VQA 2.0 and VQA-CP v2 datasets. We further show that ReGAT is compatible to existing VQA architectures, and can be used as a generic relation encoder to boost the model performance for VQA.