Reviewerly/exHarmony huggingface dataset:
Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks

Breast ultrasound and computer aided diagnosis (CAD) has been used to classify tumors into benignancy or malignancy. However, conventional CAD software has some problems (such as handcrafted features are hard to design; conventional CAD systems are difficult to confirm overfitting problems, etc.). In our study, we propose a CAD system for tumor diagnosis using an image fusion method combined with different image content representations and ensemble different CNN architectures on US images. The CNN-based method proposed in this study includes VGGNet, ResNet, and DenseNet. In our private dataset, there was a total of 1687 tumors that including 953 benign and 734 malignant tumors. The accuracy, sensitivity, specificity, precision, F1 score and the AUC of the proposed method were 91.10%, 85.14%, 95.77%, 94.03%, 89.36%, and 0.9697 respectively. In the open dataset (BUSI), there was a total of 697 tumors that including 437 benign lesions, 210 malignant tumors, and 133 normal images. The accuracy, sensitivity, specificity, precision, F1 score, and the AUC of the proposed method were 94.62%, 92.31%, 95.60%, 90%, 91.14%, and 0.9711. In conclusion, the results indicated different image content representations that affect the prediction performance of the CAD system, more image information improves the prediction performance, and the tumor shape feature can improve the diagnostic effect.
Securing Smart Cities through Blockchain Technology: Architecture, Requirements, and Challenges

In recent years, unprecedented work has been done in the area of smart cities. The purpose of developing smart cities is to enhance quality of life factors for people dwelling within them. To achieve that purpose, technologies such as IoT and cloud computing have been utilized. Blockchain technology is also among the promising technologies that can offer countless valuable services to its end users. It is a immutable programmable digital register for the purpose of recording virtual assets having some value and was primarily developed for digital currencies like Bitcoin. To fully utilize the services of blockchain technology within smart cities, characteristics of blockchain technology, and its key requirements and research challenges need to be identified. Hence, in this article, an attempt has been made to identify the characteristics of blockchain technology. Furthermore, indispensable requirements for incorporating blockchain technology within smart cities are enumerated. A conceptual architecture for securing smart city using blockchain technology is proposed and explained using a possible use case study. An overview of a real-world three-blockchain- based smart city case study is also presented. Finally, several imperative research challenges are identified and discussed.
SCSampler: Sampling Salient Clips From Video for Efficient Action Recognition

While many action recognition datasets consist of collections of brief, trimmed videos each containing a relevant action, videos in the real-world (e.g., on YouTube) exhibit very different properties: they are often several minutes long, where brief relevant clips are often interleaved with segments of extended duration containing little change. Applying densely an action recognition system to every temporal clip within such videos is prohibitively expensive. Furthermore, as we show in our experiments, this results in suboptimal recognition accuracy as informative predictions from relevant clips are outnumbered by meaningless classification outputs over long uninformative sections of the video. In this paper we introduce a lightweight ``clip-sampling'' model that can efficiently identify the most salient temporal clips within a long video. We demonstrate that the computational cost of action recognition on untrimmed videos can be dramatically reduced by invoking recognition only on these most salient clips. Furthermore, we show that this yields significant gains in recognition accuracy compared to analysis of all clips or randomly selected clips. On Sports1M, our clip sampling scheme elevates the accuracy of an already state-of-the-art action classifier by 7% and reduces by more than 15 times its computational cost.
Secure Health Data Sharing for Medical Cyber-Physical Systems for the Healthcare 4.0

The recent spades of cyber attacks have compromised end-users' data security and privacy in Medical Cyber-Physical Systems (MCPS) in the era of Health 4.0. Traditional standard encryption algorithms for data protection are designed based on a viewpoint of system architecture rather than a viewpoint of end-users. As such encryption algorithms are transferring the protection on the data to the protection on the keys, data safety, and privacy will be compromised once the key is exposed. In this paper, we propose a secure data storage and sharing method consisted of a selective encryption algorithm combined with fragmentation and dispersion to protect the data safety and privacy even when both transmission media (e.g. cloud servers) and keys are compromised. This method is based on a user-centric design that protects the data on a trusted device such as the end-users' smartphone and lets the end-user control the access for data sharing. We also evaluate the performance of the algorithm on a smartphone platform to prove efficiency.
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, but reconstructed images usually miss high-frequency edge information. Therefore, object detection performance showed degradation for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we applied a new edge-enhanced super-resolution GAN (EESRGAN) to improve the quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss was backpropagated into the EESRGAN to improve the detection performance. We proposed an architecture with three components: ESRGAN, EEN, and Detection network. We used residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we used a faster region-based convolutional network (FRCNN) (two-stage detector) and a single-shot multibox detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) dataset and another self-assembled (oil and gas storage tank) satellite dataset showed superior performance of our method compared to the standalone state-of-the-art object detectors.
An Overview of Platforms for Big Earth Observation Data Management and Analysis

In recent years, Earth observation (EO) satellites have generated big amounts of geospatial data that are freely available for society and researchers. This scenario brings challenges for traditional spatial data infrastructures (SDI) to properly store, process, disseminate and analyze these big data sets. To meet these demands, novel technologies have been proposed and developed, based on cloud computing and distributed systems, such as array database systems, MapReduce systems and web services to access and process big Earth observation data. Currently, these technologies have been integrated into cutting edge platforms in order to support a new generation of SDI for big Earth observation data. This paper presents an overview of seven platforms for big Earth observation data management and analysis—Google Earth Engine (GEE), Sentinel Hub, Open Data Cube (ODC), System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL), openEO, JEODPP, and pipsCloud. We also provide a comparison of these platforms according to criteria that represent capabilities of the EO community interest.
Multi-Scale Metric Learning for Few-Shot Learning

Few-shot learning in image classification is developed to learn a model that aims to identify unseen classes with only few training samples for each class. Fewer training samples and new tasks of classification make many traditional classification models no longer applicable. In this paper, a novel few-shot learning method named multi-scale metric learning (MSML) is proposed to extract multi-scale features and learn the multi-scale relations between samples for the classification of few-shot learning. In the proposed method, a feature pyramid structure is introduced for multi-scale feature embedding, which aims to combine high-level strong semantic features with low-level but abundant visual features. Then a multi-scale relation generation network (MRGN) is developed for hierarchical metric learning, in which high-level features are corresponding to deeper metric learning while low-level features are corresponding to lighter metric learning. Moreover, a novel loss function named intra-class and inter-class relation loss (IIRL) is proposed to optimize the proposed deep network, which aims to strengthen the correlation between homogeneous groups of samples and weaken the correlation between heterogeneous groups of samples. Experimental results on mini ImageNet and tiered ImageNet demonstrate that the proposed method achieves superior performance in few-shot learning problem.
Cross-Batch Memory for Embedding Learning

Mining informative negative instances are of central importance to deep metric learning (DML). However, the hard-mining ability of existing DML methods is intrinsically limited by mini-batch training, where only a mini-batch of instances are accessible at each iteration. In this paper, we identify a “slow drift” phenomena by observing that the embedding features drift exceptionally slow even as the model parameters are updating throughout the training process. It suggests that the features of instances computed at preceding iterations can considerably approximate to their features extracted by current model. We propose a cross-batch memory (XBM) mechanism that memorizes the embeddings of past iterations, allowing the model to collect sufficient hard negative pairs across multiple mini-batches - even over the whole dataset. Our XBM can be directly integrated into general pair-based DML framework.We demonstrate that, without bells and whistles, XBM augmented DML can boost the performance considerably on image retrieval. In particular, with XBM, a simple contrastive loss can have large R@1 improvements of 12%-22.5% on three large-scale datasets, easily surpassing the most sophisticated state-of-the-art methods [38, 27, 2], by a large margin. Our XBM is conceptually simple, easy to implement - using several lines of codes, and is memory efficient - with a negligible 0.2 GB extra GPU memory.
Zether: Towards Privacy in a Smart Contract World

Smart contract platforms such as Ethereum and Libra provide ways to seamlessly remove trust and add transparency to various distributed applications. Yet, these platforms lack mechanisms to guarantee user privacy, even at the level of simple payments, which are essential for most smart contracts. In this paper, we propose Zether, a trustless mechanism for privacy-preserving payments in smart contract platforms. We take an account-based approach similar to Ethereum and Libra for efficiency and usability. Zether is implemented as a smart contract that keeps account balances encrypted and exposes methods to deposit, transfer, and withdraw funds to/from accounts through cryptographic proofs at only a small cost. We address several technical challenges to protect Zether against replay attacks and front-running situations and develop a mechanism to enable interoperability with arbitrary smart contracts, making applications like auctions, payment channels, and voting privacy-preserving. To make Zether efficient, we propose $$\varSigma $$ -Bullets, a zero-knowledge proof system that is optimized for $$\varSigma $$ -protocols. We implement Zether as an Ethereum smart contract and show its practicality by measuring the amount of gas used by the Zether contract. A Zether confidential transaction costs about 0.014 ETH or approximately $1.51 (as of early 2019), which can be drastically reduced with minor changes to Ethereum that we describe in the paper.
Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering

The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study. In the proposed approach, label prediction and network parameter learning are alternately iterated to meet the following criteria: (a) pixels of similar features should be assigned the same label, (b) spatially continuous pixels should be assigned the same label, and (c) the number of unique labels should be large. Although these criteria are incompatible, the proposed approach minimizes the combination of similarity loss and spatial continuity loss to find a plausible solution of label assignment that balances the aforementioned criteria well. The contributions of this study are four-fold. First, we propose a novel end-to-end network of unsupervised image segmentation that consists of normalization and an argmax function for differentiable clustering. Second, we introduce a spatial continuity loss function that mitigates the limitations of fixed segment boundaries possessed by previous work. Third, we present an extension of the proposed method for segmentation with scribbles as user input, which showed better accuracy than existing methods while maintaining efficiency. Finally, we introduce another extension of the proposed method: unseen image segmentation by using networks pre-trained with a few reference images without re-training the networks. The effectiveness of the proposed approach was examined on several benchmark datasets of image segmentation.
A Single Stream Network for Robust and Real-Time RGB-D Salient Object Detection

Existing RGB-D salient object detection (SOD) approaches concentrate on the cross-modal fusion between the RGB stream and the depth stream. They do not deeply explore the effect of the depth map itself. In this work, we design a single stream network to directly use the depth map to guide early fusion and middle fusion between RGB and depth, which saves the feature encoder of the depth stream and achieves a lightweight and real-time model. We tactfully utilize depth information from two perspectives: (1) Overcoming the incompatibility problem caused by the great difference between modalities, we build a single stream encoder to achieve the early fusion, which can take full advantage of ImageNet pre-trained backbone model to extract rich and discriminative features. (2) We design a novel depth-enhanced dual attention module (DEDA) to efficiently provide the fore-/back-ground branches with the spatially filtered features, which enables the decoder to optimally perform the middle fusion. Besides, we put forward a pyramidally attended feature extraction module (PAFE) to accurately localize the objects of different scales. Extensive experiments demonstrate that the proposed model performs favorably against most state-of-the-art methods under different evaluation metrics. Furthermore, this model is 55.5% lighter than the current lightest model and runs at a real-time speed of 32 FPS when processing a $$384 \times 384$$ image.
My Chatbot Companion - a Study of Human-Chatbot Relationships

There has been a recent surge of interest in social chatbots, and human–chatbot relationships (HCRs) are becoming more prevalent, but little knowledge exists on how HCRs develop and may impact the broader social context of the users. Guided by Social Penetration Theory, we interviewed 18 participants, all of whom had developed a friendship with a social chatbot named Replika, to understand the HCR development process. We find that at the outset, HCRs typically have a superficial character motivated by the users' curiosity. The evolving HCRs are characterised by substantial affective exploration and engagement as the users' trust and engagement in self-disclosure increase. As the relationship evolves to a stable state, the frequency of interactions may decrease, but the relationship can still be seen as having substantial affective and social value. The relationship with the social chatbot was found to be rewarding to its users, positively impacting the participants' perceived wellbeing. Key chatbot characteristics facilitating relationship development included the chatbot being seen as accepting, understanding and non-judgmental. The perceived impact on the users' broader social context was mixed, and a sense of stigma associated with HCRs was reported. We propose an initial model representing the HCR development identified in this study and suggest avenues for future research.
Efficient Image Super-Resolution Using Pixel Attention

This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective network with a newly proposed pixel attention scheme. Pixel attention (PA) is similar as channel attention and spatial attention in formulation. The difference is that PA produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results. On the basis of PA, we propose two building blocks for the main branch and the reconstruction branch, respectively. The first one—SC-PA block has the same structure as the Self-Calibrated convolution but with our PA layer. This block is much more efficient than conventional residual/dense blocks, for its two-branch architecture and attention scheme. While the second one—U-PA block combines the nearest-neighbor upsampling, convolution and PA layers. It improves the final reconstruction quality with little parameter cost. Our final model—PAN could achieve similar performance as the lightweight networks—SRResNet and CARN, but with only 272K parameters (17.92% of SRResNet and 17.09% of CARN). The effectiveness of each proposed component is also validated by ablation study. The code is available at https://github.com/zhaohengyuan1/PAN .
Understanding Robustness of Transformers for Image Classification

Deep Convolutional Neural Networks (CNNs) have long been the architecture of choice for computer vision tasks. Recently, Transformer-based architectures like Vision Transformer (ViT) have matched or even surpassed ResNets for image classification. However, details of the Transformer architecture –such as the use of non-overlapping patches– lead one to wonder whether these networks are as robust. In this paper, we perform an extensive study of a variety of different measures of robustness of ViT models and compare the findings to ResNet baselines. We investigate robustness to input perturbations as well as robustness to model perturbations. We find that when pre-trained with a sufficient amount of data, ViT models are at least as robust as the ResNet counterparts on a broad range of perturbations. We also find that Transformers are robust to the removal of almost any single layer, and that while activations from later layers are highly correlated with each other, they nevertheless play an important role in classification.
Past, Present, and Future of Aerial Robotic Manipulators

This article analyzes the evolution and current trends in aerial robotic manipulation, comprising helicopters, conventional underactuated multirotors, and multidirectional thrust platforms equipped with a wide variety of robotic manipulators capable of physically interacting with the environment. It also covers cooperative aerial manipulation and interconnected actuated multibody designs. The review is completed with developments in teleoperation, perception, and planning. Finally, a new generation of aerial robotic manipulators is presented with our vision of the future.
Chatbots for language learning—Are they really useful? A systematic review of chatbot‐supported language learning

Abstract Background The use of chatbots as learning assistants is receiving increasing attention in language learning due to their ability to converse with students using natural language. Previous reviews mainly focused on only one or two narrow aspects of chatbot use in language learning. This review goes beyond merely reporting the specific types of chatbot employed in past empirical studies and examines the usefulness of chatbots in language learning, including first language learning, second language learning, and foreign language learning. Aims The primary purpose of this review is to discover the possible technological, pedagogical, and social affordances enabled by chatbots in language learning. Materials &amp; Methods We conducted a systematic search and identifies 25 empirical studies that examined the use of chatbots in language learning. We used the inductive grounded approach to identify the technological and pedagogical affordances, and the challenges of using chatbots for students’ language learning. We used Garrison's social presence framework to analyze the social affordances of using chatbots in language learning Results Our findings revealed three technological affordances: timeliness, ease of use, and personalization; and five pedagogical uses: as interlocutors, as simulations, for transmission, as helplines, and for recommendations. Chatbots appeared to encourage students’ social presence by affective, open, and coherent communication. Several challenges in using chatbots were identified: technological limitations, the novelty effect, and cognitive load. Discussion and Conclusion A set of rudimentary design principles for chatbots are proposed for meaningfully implementing educational chatbots in language learning, and detailed suggestions for future research are presented.
Metaverse – the new marketing universe

Purpose This study aims to present and explain the concept of Metaverse, which will revolutionize nearly every industry and can be regarded as the 3D version of the internet. Especially, the paper explores the “building blocks” of the Metaverse and how it is functioning in a case study. Design/methodology/approach The Metaverse concept is explained by the Nike–Roblox case study, where the authors explore the customer benefits, that are provided by the Nikeland project. Findings The Nike–Roblox case study is showing that virtual platforms, content services, consumer and business behavior are the most important and visible “building blocks” to the Nikeland visitors (customers) in the Nike–Roblox alliance. The Metaverse is gaining popularity among the big global brands. It is expected that the big breakthrough for Metaverse will happen when the next layer of brands, the regional and local brands, will start penetrating the Metaverse. Originality/value Metaverse will be the new future marketing platform for presenting and giving life to all kinds of brands in the 3D interactive digital space. Metaverse is a digital copy of how we are working in the physical world. In this 3D digital space, the users can come together via avatars that resemble them. This will have an enormous effect on how companies will use the marketing function and how we will communicate with each other in future.