Reviewerly/exHarmony huggingface dataset:
Scalable On-Chain and Off-Chain Blockchain for Sharing Economy in Large-Scale Wireless Networks

In the future sharing economy, billions of underutilized IoT devices will be deployed to enable a powerful and large-scale sharing market that produces economic, environmental, and social benefits. Given the fact that communications in numerous IoT devices through wireless links are unreliable, blockchain technology, as a promising solution, has emerged to achieve reliable and secure sharing services in a decentralized manner. However, applying blockchain in large-scale wireless networks confronts scalability challenges. This motivates us to propose a real-time, trusted data interactive, and fine-grained transaction supportable sharing framework, the core of which is a novel two-layer scaling blockchain design. In the on-chain layer, sharing-oriented sharding is employed to enable secure and efficient processing of macro-transactions on the chain. In the off-chain layer, cross-zone off-chain channels are set up to provide real-time sharing transactions with high-frequency micro-trading scenarios. Finally, a proof-of-concept case study of electric vehicle sharing data is implemented with experimental results to demonstrate the feasibility of our framework.
MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.
Axial Attention MLP-Mixer: A New Architecture for Image Segmentation

Recently, the MLP-Mixer model has received much attention in vision problems. The advantage of this model is that by using only multi-layer perceptron (MLP) blocks, the model could build well the long-range dependencies of the input patches when pre-trained on huge data sets. Recognizing the importance of information positions in patch processing and the advantage of using MLPs, in this study, we proposed an Axial Attention MLP-Mixer model, shorted as AxialAtt-MLP-Mixer for image segmentation problem. In particular, inspired by advanced attention mechanisms along with position embedding, we proposed a new token layer that replaces the token mixing in the MLP-Mixer model to make the model more aware of global information. In addition, we propose a new model using MLP-Mixer architecture and an axial attention token layer. Through evaluation on two datasets: GlaS and Data Science Bowl 2018, we indicate the superiority of the proposed method along with the ability to get good results right on small datasets without pre-training.
Coarse-to-fine few-shot classification with deep metric learning

Few-shot classification predicts the labels of unseen samples using only a few labeled samples, and employs the samples of the classes disjoint with unseen classes for model training. It faces two primary challenges, i.e., handling sample pairs with different similarity degrees by single classifier, and learning discriminant patterns from very few labeled samples per class. To address them, this work presents a Coarse-to-Fine few-shot classification framework under the guidance of Metric-based Auxiliary learning, abbreviated as CFMA. In particular, it discriminates the image pairs with large difference by capturing global features, and models the similarity relation of the image pairs with small difference by mining the local region of interests. Moreover, CFMA adopts deep metric learning to improve the model adaptivity on the set of limited samples, and generates pseudo labels to dynamically guide the coarse learning in iteration. Empirical studies on several benchmark databases, including mini-ImageNet, tiered-ImageNet, and CUB, demonstrate that our method achieves more promising classification performance compared to many state-of-the-art alternatives.
Privacy Preserving Attribute-Focused Anonymization Scheme for Healthcare Data Publishing

Advancements in Industry 4.0 brought tremendous improvements in the healthcare sector, such as better quality of treatment, enhanced communication, remote monitoring, and reduced cost. Sharing healthcare data with healthcare providers is crucial for harnessing the benefits of such improvements. In general, healthcare data holds sensitive information about individuals. Hence, sharing such data is challenging because of various security and privacy issues. According to privacy regulations and ethical requirements, it is essential to preserve the privacy of patients before sharing data for medical research. State-of-the-art literature on privacy preserving studies either uses cryptographic approaches to protect the privacy or uses anonymizing techniques regardless of the type of attributes, this results in poor protection and data utility. In this paper, we propose an attribute-focused privacy preserving data publishing scheme. The proposed scheme is two-fold, comprising a fixed-interval approach to protect numerical attributes and an improved l-diverse slicing approach to protect the categorical and sensitive attributes. In the fixed-interval approach, the original values of the healthcare data are replaced with an equivalent computed value. The improved l-diverse slicing approach partitions the data both horizontally and vertically to avoid privacy leaks. Extensive experiments with real-world datasets are conducted to evaluate the performance of the proposed scheme. The classification models built on anonymized dataset yields approximately 13% better accuracy than benchmarked algorithms. Experimental analyses show that the average information loss which is measured by normalized certainty penalty (NCP) is reduced by 12% compared to similar approaches. The attribute focused scheme not only provides data utility but also prevents the data from membership disclosures, attribute disclosures, and identity disclosures.
Dependency graph enhanced interactive attention network for aspect sentiment triplet extraction

Aspect sentiment triplet extraction is an extremely daunting task designed to identify the triplets from comments, where each triplet is composed of an aspect term, the related opinion term, and the sentiment between them. Existing research efforts majorly construct a novel tagging scheme to avoid the disadvantages of pipeline methods. However, the improvement is limited due to neglecting the implicit grammatical relationships among the three elements in a triplet. To cope with this limitation, we put forward an innovative Dependency Graph Enhanced Interactive Attention Network, which explicitly introduces the syntactic and semantic relationships between words. Specifically, an interactive attention mechanism is conceived to jointly consider both the contextual features learned from Bi-directional Long Short-Term Memory and the syntactic dependencies learned from the correspondent dependency graph in an iterative interaction manner. In addition, we notice that words with different Part-of-Speech categories have different contributions to the semantic expression of sentences. Accordingly, the information of different Part-of-Speech categories is recognized during the modeling process to properly capture the semantic relationships. Experiments on the benchmark datasets originally derived from SemEval Challenges illustrate that our presented approach has superiority over strong baselines.
Encrypting Multiple Images With an Enhanced Chaotic Map

A multiple-image encryption scheme based on an enhanced chaotic map is proposed. This scheme combines multiple grayscale images into three planes. An amplified sine map is used to generate a dynamic permutation table and a chaotic sequence. The initial parameters of the amplified sine map are obtained from an elliptic curve coordinate, computing using elliptic curve point multiplication between the input image hash value and the seed value of an elliptic curve. The dynamic permutation table performs cyclic shift transformation on the input image, diffusing it horizontally and vertically. The diffused image is converted to a cipher image through an XOR operation with the chaotic sequence generated by the amplified sine map. Using an amplified sine map provides larger key space, more extensive chaotic range, more extensive control parameters, better sensitive initial values and enhanced security against cryptanalysis. The experiment results indicate that the proposed scheme is fast, secure, efficient and can resist certain cryptographic attacks. Compared to other recent schemes, the proposed scheme is shown to be secure and efficient.
Analysis on the evolution of shipping logistics service supply chain market structure under the application of blockchain technology

With the digital transformation of the shipping industry, the business potential of shipping companies is gradually expanding. In recent years, shipping companies have begun operating freight forwarding businesses powered by the advantages of cost and network. As a result, their business expansion and gradually occupying the market share of agency services. At the same time, the development requirements of digitization are further highlighted, while the problems of opaque information and inaccurate data in the operation of the shipping logistics service supply chain have become more prominent. Thus, the shipping industry has begun to apply Blockchain Technology (BCT) to the shipping logistics. However, the promotion of BCT, an emerging technology in the shipping logistics service market, is not systematic. The members of the supply chain do not have a deep understanding of it, which have led to changes in market demand. We take the shipping logistics service supply chain composed of shipping companies and freight forwarders as the research object in order to investigate the vertical competition between shipping companies and freight forwarders as well as the impact of BCT application on market structure changes. From the perspectives of freight rate competition and the promotion of BCT, the Stackelberg game theory is applied to create a mathematical model that can be used to discuss the evolution of the shipping logistics service market. The results indicate that, the impact of freight rate competition on market evolution is reduced after the application of BCT. Shipping companies should actively promote BCT to reduce the impact of freight competition on the market. Furthermore, freight forwarders can actively participate in the initial stage of application to increase market demand, and the cost of promoting BCT should be reduced in the later stage.
Improving neural machine translation with POS-tag features for low-resource language pairs

Integrating linguistic features has been widely utilized in statistical machine translation (SMT) systems, resulting in improved translation quality. However, for low-resource languages such as Thai and Myanmar, the integration of linguistic features in neural machine translation (NMT) systems has yet to be implemented. In this study, we propose transformer-based NMT models (transformer, multi-source transformer, and shared-multi-source transformer models) using linguistic features for two-way translation of Thai-to-Myanmar, Myanmar-to-English, and Thai-to-English. Linguistic features such as part-of-speech (POS) tags or universal part-of-speech (UPOS) tags are added to each word on either the source or target side, or both the source and target sides, and the proposed models are conducted. The multi-source transformer and shared-multi-source transformer models take two inputs (i.e., string data and string data with POS tags) and produce string data or string data with POS tags. A transformer model that utilizes only word vectors was used as the first baseline model for comparison with the proposed models. The second baseline model, an Edit-Based Transformer with Repositioning (EDITOR) model, was also used to compare with our proposed models in addition to the baseline transformer model. The findings of the experiments show that adding linguistic features to the transformer-based models enhances the performance of a neural machine translation in low-resource language pairs. Moreover, the best translation results were yielded using shared-multi-source transformer models with linguistic features resulting in more significant Bilingual Evaluation Understudy (BLEU) scores and character n-gram F-score (chrF) scores than the baseline transformer and EDITOR models.
DeepThy‐Net: A Multimodal Deep Learning Method for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Cancer

Papillary thyroid cancer (PTC) accounts for more than 80% of thyroid cancers, and ultrasound (US) imaging is the preferred method for the diagnosis of PTC. However, accurate prediction of different patterns of cervical lymph node metastasis (CLNM) in PTC continues to be a challenge. Herein, US images and clinical factors of PTC patients from three hospitals for more than 11 years are collected, and a multimodal deep learning model called DeepThy‐Net is then developed to predict different CLNM patterns. The proposed model not only uses the convolutional features extracted by deep learning but also integrates traditional clinical factors that are highly related to lymph node metastasis. Finally, the model is tested in two independent test sets, and the experimental results show that the area under curve (AUC) is between 0.870 and 0.905, indicating clinical applicability. The proposed method provides an important reference for the treatment and management of PTC. Moreover, for PTC cases involving an active surveillance strategy, the proposed method can serve as an important CLNM early warning tool.
CA‐Unet++: An improved structure for medical CT scanning based on the Unet++ Architecture

Currently, deep learning has become more and more mature in the field of medical image segmentation. Through using the computer, the deep learning models established can completely help doctors to perform medical image segmentation. Most of the current deep learning models are based on Unet. The U-shaped structure and the skip connection layer of Unet can effectively achieve precise image segmentation. However, for complicated images, the network structure of Unet is not sufficient enough. In response to this problem, some scholars have designed Unet++ by adding a denser skip connection layer to U-Net. Compared to U-Net, Unet++ is more effective in dealing with complex images, but it has drawbacks in many aspects, and there is still a large loss of eigenvalues in the skip connection and up-sampling processes. To address these issues, this paper uses the channel and attention mechanism to improve the Unet++ model to obtain better image segmentation efficiency and accuracy. Meanwhile, based on Unet++, this paper designs a new model called CA-Unet++. The proposed model uses the channel module and the attention module to solve the eigenvalues loses in the long-distance skip connection process and the up-sampling process, respectively. The experimental results and data analysis shows that our proposed CA-Unet++ can achieve better performance in medical computed tomography scan image segmentation.
RestainNet: A self-supervised digital re-stainer for stain normalization

Color inconsistency is an inevitable challenge in computational pathology, which harms the pathological image analysis methods, especially the learning-based models. A series of approaches have been proposed for stain normalization. However, most of them are lack of flexibility in practice. In this paper, we formulated stain normalization as a digital re-staining process and proposed a self-supervised learning model, which is called RestainNet. Our network is regarded as a digital re-stainer which learns how to re-stain an unstained (grayscale) image. Two digital stains, Hematoxylin (H) and Eosin (E), were extracted from the original image by Beer–Lambert’s Law. We proposed a staining loss to maintain the correctness of stain intensity during the re-staining process. Our RestainNet outperforms existing approaches and achieves outstanding performance with regard to color correctness and structure preservation. We further conducted experiments on the segmentation and classification tasks and the proposed RestainNet achieved outstanding performance compared with SOTA methods. • Our network is trained in a self-supervised manner. • Our network follows the inherent optical imaging properties of pathological images. • Our network can be trained on a single Whole Slide Image. • Our network achieves structure-preserving stain normalization.
SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos

Tracking devices that can track both players and balls are critical to the performance of sports teams. Recently, significant effort has been focused on building larger broadcast sports video datasets. However, broadcast videos do not show the entire pitch and only provides partial information about the game. On the other hand, other camera perspectives can capture the whole field in a single frame, such as fish-eye and bird-eye view (drone) cameras. Unfortunately, there has not been a dataset where such data has been publicly shared until now. This paper proposes SoccerTrack, a dataset set consisting of GNSS and bounding box tracking data annotated on video captured with a 8K-resolution fish-eye camera and a 4K-resolution drone camera. In addition to a benchmark tracking algorithm, we include code for camera calibration and other preprocessing. Finally, we evaluate the tracking accuracy among a GNSS, fish-eye camera and drone camera data. SoccerTrack is expected to provide a more robust foundation for designing MOT algorithms that are less reliant on visual cues and more reliant on motion analysis. The dataset and related project code is available at https://github.com/AtomScott/SoccerTrack <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Non-linear Motion Estimation for Video Frame Interpolation using Space-time Convolutions

Video frame interpolation aims to synthesize one or multiple frames between two consecutive frames in a video. It has a wide range of applications including slow-motion video generation, video compression and developing video codecs. Some older works tackled this problem by assuming per-pixel linear motion between video frames. However, objects often follow a non-linear motion pattern in the real domain and some recent methods attempt to model per-pixel motion by non-linear models (e.g., quadratic). A quadratic model can also be inaccurate, especially in the case of motion discontinuities over time (i.e. sudden jerks) and occlusions, where some of the flow information may be invalid or inaccurate. In our paper, we propose to approximate the per-pixel motion using a space-time convolution network that is able to adaptively select the motion model to be used. Specifically, we are able to softly switch between a linear and a quadratic model. Towards this end, we use an end-to-end 3D CNN encoder-decoder architecture over bidirectional optical flows and occlusion maps to estimate the non-linear motion model of each pixel. Further, a motion refinement module is employed to refine the non-linear motion and the interpolated frames are estimated by a simple warping of the neighboring frames with the estimated per-pixel motion. We show that our method outperforms state-of-the-art algorithms on four datasets.
The Effect of Improving Annotation Quality on Object Detection Datasets: A Preliminary Study

In this study, we partially reannotate conventional benchmark datasets for object detection and check whether there is performance improvement/drop compared with the original annotations. Recent studies on the annotation qualities of ImageNet for image classification revealed some issues of how to associate only a single label to each image accurately. Object detection, on the other hand, should have other nontrivial issues because there are multiple objects in a single image, and realizing consistency among bounding boxes is challenging. A team of professional annotators was formed for MS COCO and Google Open Images datasets. To realize highly-consistent an-notations, we prepared carefully designed guidelines for each category and selected quality inspectors who checked the annotation quality of each annotator. Finally, we applied conventional object detection methods for reannotated parts of each dataset. We found mixed results: whether the performance dropped or improved depended on each category and dataset.