Reviewerly/exHarmony huggingface dataset:
The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale

We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide $$15\times $$ more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning. We provide in-depth comprehensive statistics about the dataset, we validate the quality of the annotations, we study how the performance of several modern models evolves with increasing amounts of training data, and we demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images. We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection.
Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment

There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time.
A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning

Unmanned aerial vehicles (UAVs) have been used in wide range of areas, and a high-quality path planning method is needed for UAVs to satisfy their applications. However, many algorithms reported in the literature may not feasible or efficient, especially in the face of three-dimensional complex flight environment. In this paper, a novel reinforcement learning based grey wolf optimizer algorithm called RLGWO has been presented for solving this problem. In the proposed algorithm, the reinforcement learning is inserted that the individual is controlled to switch operations adaptively according to the accumulated performance. Considering that the proposed algorithm is designed to serve for UAVs path planning, four operations have been introduced for each individual: exploration, exploitation, geometric adjustment, and optimal adjustment. In addition, the cubic B-spline curve is used to smooth the generated flight route and make the planning path be suitable for the UAVs. The simulation experimental results show that the RLGWO algorithm can acquire a feasible and effective route successfully in complicated environment.
Personalized Cross-Silo Federated Learning on Non-IID Data

Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish the convergence of FedAMP for both convex and non-convex models, and propose a heuristic method to further improve the performance of FedAMP when clients adopt deep neural networks as personalized models. Our extensive experiments on benchmark data sets demonstrate the superior performance of the proposed methods.
Emerging Cross-lingual Structure in Pretrained Language Models

We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer.We show, contrary to what was previously hypothesized, that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains.The only requirement is that there are some shared parameters in the top layers of the multi-lingual encoder.To better understand this result, we also show that representations from monolingual BERT models in different languages can be aligned post-hoc quite effectively, strongly suggesting that, much like for non-contextual word embeddings, there are universal latent symmetries in the learned embedding spaces.For multilingual masked language modeling, these symmetries are automatically discovered and aligned during the joint training process.
Twin-field quantum key distribution over a 511 km optical fibre linking two distant metropolitan areas

The basic principle of quantum mechanics guarantee the unconditional security of quantum key distribution (QKD) at the cost of inability of amplification of quantum state. As a result, despite remarkable progress in worldwide metropolitan QKD networks over the past decades, long haul fiber QKD network without trustful relay has not been achieved yet. Here, through sending-or-not-sending (SNS) protocol, we complete a twin field QKD (TF-QKD) and distribute secure keys without any trusted repeater over a 511 km long haul fiber trunk linking two distant metropolitans. Our secure key rate is around 3 orders of magnitudes greater than what is expected if the previous QKD field test system over the same length were applied. The efficient quantum-state transmission and stable single-photon interference over such a long distance deployed fiber paves the way to large-scale fiber quantum networks.
A Survey on Security Verification of Blockchain Smart Contracts

A smart contract is an agreement between two or more parties, which is executed by the computer code. The code does the execution without giving either party the ability to back out, so it ensures the trustless execution. The smart contract is one of the most important features in blockchain applications, which implements trusted transactions without third parties. However, with the rapid development, blockchain smart contracts have also exposed many security problems, and some attacks caused by contract vulnerabilities have led to terrible losses. In order to better deal with such dilemma, making a comprehensive survey about the security verification of blockchain smart contracts from major scientific databases is quite indispensable. Even though the significance of studying security verification of blockchain smart contracts is evident, it is really fresh yet. The major contributions of our survey work come from three aspects. First, after retrieving all-sided research studies, we select 53 most related papers to show the state-of-the art of this topic, where 20 papers focus on dealing with security assurance of blockchain smart contracts, and 33 papers focus on the correctness verification of blockchain smart contracts. Second, we propose a taxonomy toward the topic of security verification of blockchain smart contracts and discuss the pros and cons of each category of related studies. Third, through in-depth analysis of these studies, we come to know that the correctness verification of smart contracts based on the formal method has already become the more significant and more effective method to validate whether a smart contract is credible and accurate. So, we further present representative studies of formal verification of smart contracts in detail to demonstrate that using a formal method to validate blockchain smart contracts must have a promising and meritorious future.
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring

We improve the recently-proposed ``MixMatch semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. - Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels. - Augmentation anchoring} feeds multiple strongly augmented versions of an input into the model and encourages each output to be close to the prediction for a weakly-augmented version of the same input. To produce strong augmentations, we propose a variant of AutoAugment which learns the augmentation policy while the model is being trained. Our new algorithm, dubbed ReMixMatch, is significantly more data-efficient than prior work, requiring between 5 times and 16 times less data to reach the same accuracy. For example, on CIFAR-10 with 250 labeled examples we reach 93.73% accuracy (compared to MixMatch's accuracy of 93.58% with 4000 examples) and a median accuracy of 84.92% with just four labels per class.
Personalized Federated Learning With Differential Privacy

To provide intelligent and personalized services on smart devices, machine learning techniques have been widely used to learn from data, identify patterns, and make automated decisions. Machine learning processes typically require a large amount of representative data that are often collected through crowdsourcing from end users. However, user data could be sensitive in nature, and training machine learning models on these data may expose sensitive information of users, violating their privacy. Moreover, to meet the increasing demand of personalized services, these learned models should capture their individual characteristics. This article proposes a privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data. Practical issues in a distributed learning system such as user heterogeneity are considered in the proposed approach. In addition, the convergence property and privacy guarantee of the proposed approach are rigorously analyzed. The experimental results on realistic mobile sensing data demonstrate that the proposed approach is robust to user heterogeneity and offers a good tradeoff between accuracy and privacy.
Blockchain technology-enabled supply chain systems and supply chain performance: a resource-based view

Purpose Using the resource-based theoretical view of the firm, this paper aims to explore how firms’ efforts to integrate blockchain technology (BCT) into their supply chain systems and activities enable certain supply chain capabilities and, consequently, improve their supply chain performance. Design/methodology/approach Using an abductive research approach, a qualitative content analysis was conducted on 126 cases of firms attempting to implement a blockchain technology-enabled supply chain system (BCTeSCS). These firms spanning across multiple industries were identified using the Nexis Uni database. Findings Findings reveal that present BCTeSCS efforts are more-oriented toward improving operational-level capabilities (information sharing and coordination capabilities) than strategic-level capabilities (integration and collaboration capabilities). These operational and strategic-level capabilities alongside BCTeSCS deliver several supply chains performance outcomes such as quality compliance and improvement, process improvement, flexibility, reduced cost and reduced process time. However, outcomes may vary by industry type based on their uncertainties. Research limitations/implications Given the nascent state of BCT, accessibility to primary data about ongoing BCTeSCS efforts is limited. The presented framework is based on 126 cases of secondary information. Within this constraint, the paper finds scope to future empirical research by proposing a resource-based framework of BCTeSCS and related propositions. Practical implications The results and discussion of this study serve as useful guidance for practitioners involved in BCTeSCS integrations. Social implications The paper creates a BCTeSCS scenario for stakeholders to assume its potential socio-economic and socio-environmental pressures. Originality/value This paper is one of the initial attempts to examine BCTeSCS efforts across multiple industries, and thus, promises a broad future research scope.
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers.Typically, the dual-encoder architecture is adopted to learn dense representations of questions and passages for semantic matching.However, it is difficult to effectively train a dual-encoder due to the challenges including the discrepancy between training and inference, the existence of unlabeled positives and limited training data.To address these challenges, we propose an optimized training approach, called RocketQA, to improving dense passage retrieval.We make three major technical contributions in RocketQA, namely crossbatch negatives, denoised hard negatives and data augmentation.The experiment results show that RocketQA significantly outperforms previous state-of-the-art models on both MS-MARCO and Natural Questions.We also conduct extensive experiments to examine the effectiveness of the three strategies in RocketQA.Besides, we demonstrate that the performance of end-to-end QA can be improved based on our RocketQA retriever 1 .
Effect of blockchain technology adoption on supply chain adaptability, agility, alignment and performance

Purpose The purpose of this paper is to illustrate how blockchain technology can improve supply chain adaptability, alignment and agility which collectively enhance competitive advantage which in turn influences firm performance. Design/methodology/approach The conceptual framework of the present study is developed by conducting an extensive literature review on blockchain technology, supply chain adaptability, alignment, agility and competitive advantage. The sample data were collected from 397 supply chain practitioners in India to validate the conceptual model. Confirmatory factor analysis was conducted to ascertain the validity of the measures used and a structural model was analyzed for testing the proposed conceptual framework. Findings The results of the present study show that blockchain technology can improve supply chain adaptability, alignment, agility which lead to competitive advantage, which leads to better firm performance. Besides, trust generated through blockchain use also increases firm performance. Research limitations/implications Currently, the respondents do not have practical experience of using blockchain technology. They have responded based on their knowledge about supply chain and blockchain which they acquired from published sources. Different supply chains require different strategic choices and different information needs. But the present study assumes that all supply chain needs are identical. The present study assumes that government regulations regarding blockchain technology are favorable; however, currently, there is no legal framework to address blockchain technology. The findings of the current study indicate that companies not only should create more awareness regarding blockchain but also should actively work with IT companies that are engaged in developing blockchain-based supply chain solution. Managers, as well as IT companies and academicians, should join hands to study and develop a framework for regulating blockchain technology and suggest these to the policy actors. Practical implications The present study shows that supply chain practitioners are confident that blockchain technology will help improve supply chain parameters. These findings can help IT companies and their marketers for developing and promoting blockchain-based IT applications. In addition, the important implication for supply chain practitioners is that blockchain helps in creating a competitive advantage and increases firm performance. Originality/value The effect of IT on important supply chain variables has been studied in the past; however, there is not a single study which sheds light on how disruptive technologies such as blockchain will affect supply chain adaptability, alignment, agility and firm performance.
Secured Data Collection With Hardware-Based Ciphers for IoT-Based Healthcare

There are tremendous security concerns with patient health monitoring sensors in Internet of Things (IoT). The concerns are also realized by recent sophisticated security and privacy attacks, including data breaching, data integrity, and data collusion. Conventional solutions often offer security to patients' health monitoring data during the communication. However, they often fail to deal with complicated attacks at the time of data conversion into cipher and after the cipher transmission. In this paper, we first study privacy and security concerns with healthcare data acquisition and then transmission. Then, we propose a secure data collection scheme for IoT-based healthcare system named SecureData with the aim to tackle security concerns similar to the above. SecureData scheme is composed of four layers: 1) IoT network sensors/devices; 2) Fog layers; 3) cloud computing layer; and 4) healthcare provider layer. We mainly contribute to the first three layers. For the first two layers, SecureData includes two techniques: 1) light-weight field programmable gate array (FPGA) hardware-based cipher algorithm and 2) secret cipher share algorithm. We study KATAN algorithm and we implement and optimize it on the FPGA hardware platform, while we use the idea of secret cipher sharing technique to protect patients' data privacy. At the cloud computing layer, we apply a distributed database technique that includes a number of cloud data servers to guarantee patients' personal data privacy at the cloud computing layer. The performance of SecureData is validated through simulations with FPGA in terms of hardware frequency rate, energy cost, and computation time of all the algorithms and the results show that SecureData can be efficient when applying for protecting security risks in IoT-based healthcare.
Low-rank kernel learning for graph-based clustering

Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning in kernel space has shown impressive performance on a number of benchmark data sets. However, its performance is largely determined by the chosen kernel matrix. To address this issue, previous multiple kernel learning algorithm has been applied to learn an optimal kernel from a group of predefined kernels. This approach might be sensitive to noise and limits the representation ability of the consensus kernel. In contrast to existing methods, we propose to learn a low-rank kernel matrix which exploits the similarity nature of the kernel matrix and seeks an optimal kernel from the neighborhood of candidate kernels. By formulating graph construction and kernel learning in a unified framework, the graph and consensus kernel can be iteratively enhanced by each other. Extensive experimental results validate the efficacy of the proposed method.
Marlin: Preprocessing zkSNARKs with Universal and Updatable SRS

We present a methodology to construct preprocessing zkSNARKs where the structured reference string (SRS) is universal and updatable. This exploits a novel use of holography [Babai et al., STOC 1991], where fast verification is achieved provided the statement being checked is given in encoded form. We use our methodology to obtain a preprocessing zkSNARK where the SRS has linear size and arguments have constant size. Our construction improves on Sonic [Maller et al., CCS 2019], the prior state of the art in this setting, in all efficiency parameters: proving is an order of magnitude faster and verification is thrice as fast, even with smaller SRS size and argument size. Our construction is most efficient when instantiated in the algebraic group model (also used by Sonic), but we also demonstrate how to realize it under concrete knowledge assumptions. We implement and evaluate our construction. The core of our preprocessing zkSNARK is an efficient algebraic holographic proof (AHP) for rank-1 constraint satisfiability (R1CS) that achieves linear proof length and constant query complexity.
Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers

Purpose: To test whether our proposed denoising approach with deep learning-based reconstruction (dDLR) can effectively denoise brain MR images.
Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between “gender-neutralized” words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling.
Recent Advances of Generative Adversarial Networks in Computer Vision

The appearance of generative adversarial networks (GAN) provides a new approach and framework for computer vision. Compared with traditional machine learning algorithms, GAN works via adversarial training concept and is more powerful in both feature learning and representation. GAN also exhibits some problems, such as non-convergence, model collapse, and uncontrollability due to high degree of freedom. How to improve the theory of GAN and apply it to computer-vision-related tasks have now attracted much research efforts. In this paper, recently proposed GAN models and their applications in computer vision are systematically reviewed. In particular, we firstly survey the history and development of generative algorithms, the mechanism of GAN, its fundamental network structures, and theoretical analysis of the original GAN. Classical GAN algorithms are then compared comprehensively in terms of the mechanism, visual results of generated samples, and Frechet Inception Distance. These networks are further evaluated from network construction, performance, and applicability aspects by extensive experiments conducted over public datasets. After that, several typical applications of GAN in computer vision, including high-quality samples generation, style transfer, and image translation, are examined. Finally, some existing problems of GAN are summarized and discussed and potential future research topics are forecasted.