Reviewerly/exHarmony huggingface dataset:
A Traceable Vaccine Supply Management System

Everyone should be vaccinated, but the eligibility and safety of the vaccine are always overlooked by most people. The outbreak of COVID-19 has led many countries to intensify the development and production of the COVID-19 vaccine. and some countries have even required universal vaccination against this epidemic. However, such popularization of vaccination has also exposed various flaws in vaccine management that existed in the past, and vaccinators have become more concerned about the effectiveness of their vaccinations. In this paper, we propose a blockchain-based traceable vaccine management system. First, the system uses smart contracts to store the records generated during the whole process, from vaccine production to vaccination. Second, the proposed scheme uses the Edwards-curve digital signature algorithm (EdDSA) to guarantee the security and integrity of these data. Third, the system participants can access the corresponding data according to their authority to ensure the transparency of the whole system operation process. Finally, this paper will also conduct a security analysis of the whole system to ensure that the system can resist potential attacks by criminals.
Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT

Automatic liver segmentation is a prerequisite for hepatoma treatment; however, the low accuracy and stability hinder its clinical application. To alleviate this limitation, we deeply mine the context information of different scales and combine it with deep supervision to improve the accuracy of liver segmentation in this paper.We proposed a new network called MAD-UNet for automatic liver segmentation from CT. It is grounded in the 3D UNet and leverages multi-scale attention and deep supervision mechanisms. In the encoder, the downsampling pooling in 3D UNet is replaced by convolution to alleviate the loss of feature information. Meanwhile, the residual module is introduced to avoid gradient vanishment. Besides, we use the long-short skip connections (LSSC) to replace the ordinary skip connections to preserve more edge detail. In the decoder, the features of different scales are aggregated, and the attention module is employed to capture the spatial context information. Moreover, we utilized the deep supervision mechanism to improve the learning ability on deep and shallow information.We evaluated the proposed method on three public datasets, including, LiTS17, SLiver07, and 3DIRCADb, and obtained Dice scores of 0.9727, 0.9752, and 0.9691 for liver segmentation, respectively, which outperform the other state-of-the-art (SOTA) methods.Both qualitative and quantitative experimental results demonstrate that the proposed method can make full use of the feature information of different stages while enhancing spatial data's learning ability, thereby achieving high liver segmentation accuracy. Thus, it proved to be a promising tool for automatic liver segmentation in clinical assistance.
Abandon Locality: Frame-Wise Embedding Aided Transformer for Automatic Modulation Recognition

Automatic modulation recognition (AMR) has been considered as an efficient technique for non-cooperative communication and intelligent communication. In this work, we propose a modified transformer-based method for AMR, called frame-wise embedding aided transformer (FEA-T), aiming to extract the global correlation feature of the signal to obtain higher classification accuracy as well as lower time cost. To enhance the global modeling capability of the transformer, we design a frame-wise embedding module (FEM) to aggregate more samples into a token in the embedding stage to generate a more efficient token sequence. We also present the optimal frame length by analyzing the representation ability of each transformer layer for a better trade-off between the speed and the performance. Moreover, we design a novel dual-branch gate linear unit (DB-GLU) scheme for the feed-forward network of the transformer to reduce the model size and enhance the performance. Experimental results on RadioML2018.01A datasets demonstrate that the proposed method outperforms state-of-the-art works in terms of recognition accuracy and running speed.
Receding Horizon Control Using Graph Search for Multi-Agent Trajectory Planning

It is hard to find the global optimum of general nonlinear and nonconvex optimization problems in a reasonable time. This article presents a method to transfer the receding horizon control approach, where nonlinear, nonconvex optimization problems are considered, into graph-search problems. Specifically, systems with symmetries are considered to transfer system dynamics into a finite-state automaton. In contrast to traditional graph-search approaches where the search continues until the goal vertex is found, the transfer of a receding horizon control approach to graph-search problems presented in this article allows to solve them in real time. We prove that the solutions are recursively feasible by restricting the graph search to end in accepting states of the underlying finite-state automaton. The approach is applied to trajectory planning for multiple networked and autonomous vehicles. We evaluate its effectiveness in simulation and experiments in the Cyber-Physical Mobility Lab, an open-source platform for networked and autonomous vehicles. We show real-time capable trajectory planning with collision avoidance in experiments on off-the-shelf hardware and code in MATLAB for two vehicles.
Unexpected Dynamic Obstacle Monocular Detection in the Driver View

Dynamic obstacle detection is important for environmental perception in self-driving cars. Instance segmentation using a camera is a major trend in obstacle detection. However, unexpected dynamic obstacles are difficult to detect as their classes are unlabeled in the model. In this study, we combine an understanding of a road scene; optical flow movement tracking; and low-cost online visual tracking to build a system for detecting unexpected dynamic obstacles. To monitor the pixel movement, a mobile recurrent pairwise decoding optical flow deep neural network is employed to rapidly track the pixel flows between two frames. To filter background noises and leave the active region on the road, a mobile DABNet detects the targets (only roads and vehicles) in the scene. To reduce the load on the GPU, a cluster-matching tracker employs multi-tensor CPU resources to follow the estimated unexpected dynamic obstacles extracted by processing based on the road understanding and optical flows and tracks such obstacles one by one in the following frames. A real-time system properly splits the usage of GPU and CPU resources to maximize the performance of the system platform. To evaluate the efficiency, a driver view video dataset is recorded for evaluating real-world obstacles on the urban road scene. Then, animal crash videos are collected from YouTube to evaluate unexpected/rarely labeled objects. Furthermore, a mobile robot platform is used to test the proposed system to avoid obstacles in a complicated indoor scene.
General Incremental Learning with Domain-aware Categorical Representations

Continual learning is an important problem for achieving human-level intelligence in real-world applications as an agent must continuously accumulate knowledge in response to streaming data/tasks. In this work, we consider a general and yet under-explored incremental learning problem in which both the class distribution and class-specific domain distribution change over time. In addition to the typical challenges in class incremental learning, this setting also faces the intra-class stability-plasticity dilemma and intra-class domain imbalance problems. To address above issues, we develop a novel domain-aware continual learning method based on the EM framework. Specifically, we introduce a flexible class representation based on the von Mises-Fisher mixture model to capture the intra-class structure, using an expansion-and- reduction strategy to dynamically increase the number of components according to the class complexity. Moreover, we design a bi-level balanced memory to cope with data imbalances within and across classes, which combines with a distillation loss to achieve better inter- and intra-class stability-plasticity trade-off. We conduct exhaustive experiments on three benchmarks: iDigits, iDomainNet and iCIFAR-20. The results show that our approach consistently outperforms previous methods by a significant margin, demonstrating its superiority.
A Rate-and-Trust-Based Node Selection Model for Block Transmission in Blockchain Networks

Blockchain-enabled Internet of Things (IoT) has been receiving growing attention. However, IoT nodes are usually resources heterogeneous and subject to malicious attacks, such as the intentional delay of block verification and the transmission of invalid blocks. As a result, a random node-selection model for block transmission may lead to a low transmission rate and serious security risk. To solve the problem, this article proposes a rate-and-trust-based node selection model for block transmission. In our model, we calculate the block transmission rate of a node by the latency and connectivity among nodes and its trust value by its historical transmission and verification behaviors. On this basis, we propose a PageRank-based optimization algorithm for node selection that makes a tradeoff between the transmission rate and the security risk. Extensive experimental results show that the proposed model can achieve better performance than the state-of-the-art methods, including Bitcoin network, Ethereum network, and BlockP2P-EP protocol.
Lexicon-based Non-Compositional Multiword Augmentation Enriching Tweet Sentiment Analysis

One of the benefits of recognizing a slang, an id-iom or an abbreviation in a tweet is the ability to help in finding certain sentiment in a concise and understandable manner. However, a lack of adequate annotated “idiomatic tweets” makes classification challenging. We propose a pliable augmen-tation technique to improve the classification of idiomatic tweets with tiny training samples. For classification, we evaluate the performance of fine-tuning version of a pre-trained embedding model at different flavors. During the augmentation process, we deduce the intrinsic propositional meaning of the idiomatic ex-pression from IBM's SliDE (Sentiment Lexicon of IDiomatic Expressions) and another lexicon we built. The empirical results show that the proposed method is beneficial in concealing the actual intent of the tweet and advantageous to tackle the prob-lem of overfitting caused by smaller training sets. The experi-ment shows that using data augmentation of the idiomatic ex-pressions has reduced the classification error rate with 16%.
ScalaCert: Scalability-Oriented PKI with Redactable Consortium Blockchain Enabled "On-Cert" Certificate Revocation

As the voucher for identity, digital certificates and the public key infrastructure (PKI) system have always played a vital role to provide the authentication services. In recent years, with the increase in attacks on traditional centralized PKIs and the extensive deployment of blockchains, researchers have tried to establish blockchain-based secure decentralized PKIs and have made significant progress. Although blockchain enhances security, it brings new problems in scalability due to the inherent limitations of blockchain’s data structure and consensus mechanism, which become much severe for the massive access in the era of 5G and B5G. In this paper, we propose ScalaCert to mitigate the scalability problems of blockchain-based PKIs by utilizing redactable blockchain for "on-cert" revocation. Specifically, we utilize the redactable blockchain to record revocation information directly on the original certificate ("on-cert") and remove additional data structures such as CRL, significantly reducing storage overhead. Moreover, the combination of redactable and consortium blockchains brings a new kind of attack called deception of versions (DoV) attack. To defend against it, we design a random-block-node-check (RBNC) based freshness check mechanism. Security and performance analyses show that ScalaCert has sufficient security and effectively solves the scalability problem of the blockchain-based PKI system.
Bitcoin's Adoption as Legal Tender: A Tale of Two Developing Countries

The Central African Republic and El Salvador are the only two countries in the world that have adopted bitcoin as legal tender. This article examines the stated motivations of bitcoin's adoption as a legal tender in these two countries and looks at their implementation strategies.
Automatic Dense Annotation of Large-Vocabulary Sign Language Videos

Recently, sign language researchers have turned to sign language interpreted TV broadcasts, comprising (i) a video of continuous signing and (ii) subtitles corresponding to the audio content, as a readily available and large-scale source of training data. One key challenge in the usability of such data is the lack of sign annotations. Previous work exploiting such weakly-aligned data only found sparse correspondences between keywords in the subtitle and individual signs. In this work, we propose a simple, scalable framework to vastly increase the density of automatic annotations. Our contributions are the following: (1) we significantly improve previous annotation methods by making use of synonyms and subtitle-signing alignment; (2) we show the value of pseudo-labelling from a sign recognition model as a way of sign spotting; (3) we propose a novel approach for increasing our annotations of known and unknown classes based on in-domain exemplars; (4) on the BOBSL BSL sign language corpus, we increase the number of confident automatic annotations from 670K to 5M. We make these annotations publicly available to support the sign language research community.
Spectral Adversarial Training for Robust Graph Neural Network

Recent studies demonstrate that Graph Neural Networks (GNNs) are vulnerable to slight but adversarially designed perturbations, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adversarial examples</i> . To address this issue, robust training methods against adversarial examples have received considerable attention in the literature. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adversarial Training (AT)</i> is a successful approach to learning a robust model using adversarially perturbed training samples. Existing AT methods on GNNs typically construct adversarial perturbations in terms of graph structures or node features. However, they are less effective and fraught with challenges on graph data due to the discreteness of graph structure and the relationships between connected examples. In this work, we seek to address these challenges and propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> pectral <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> dversarial <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> raining (SAT), a simple yet effective adversarial training approach for GNNs. SAT first adopts a low-rank approximation of the graph structure based on spectral decomposition, and then constructs adversarial perturbations in the spectral domain rather than directly manipulating the original graph structure. To investigate its effectiveness, we employ SAT on three widely used GNNs. Experimental results on four public graph datasets demonstrate that SAT significantly improves the robustness of GNNs against adversarial attacks without sacrificing classification accuracy and training efficiency.
Joint sensor registration and multi-object tracking with PHD filter in distributed multi-sensor networks

This paper addresses the joint sensor registration and multi-object tracking in distributed sensor networks wherein the accurate positions of sensors are unknown. Each local sensor runs the probability hypothesis density filter (PHD) in its local coordinate system, having no knowledge about the relative positions of other sensors in the network. The generalized covariance intersection (GCI) fusion rule is employed to fuse the local densities. To tackle the sensor registration problem, a cost function expressing the information discrepancy between local densities is constructed based on the GCI divergence, then the relative positions of sensors are estimated by minimizing such a cost function. As the constructed cost function leads to a high-dimension optimization problem, for computational efficiency, a pairwise sensor registration strategy is proposed which is proved equally sound with the high-dimension registration method but more computationally efficient. Lastly, we demonstrate that the formulated registration method involves a non-convex problem. To solve such a non-convex problem, a two-step optimization method is proposed which can transform the non-convex problem into a convex one. The efficacy of the proposed method is demonstrated in a challenging tracking scenario via numerical experiments.
Resolvable Group Target Tracking via Multi-Bernoulli Filter and Its Application to Sensor Control Scenario

In this paper, we address the problem of resolvable group target tracking (RGTT) and the application of RGTT in the sensor control scenarios. The aim of our approach is to further improve the tracking performance of multi-Bernoulli (MB) filter by exploiting the group structure information. Firstly, a single-target state transition function (SSTF) is derived from the direct integral solution of the single-target state stochastic differential equation (SDE), which is used to revise the prediction process of the standard MB filter. Then, combing the Gaussian mixture (GM) implementation, the prediction process of the proposed SSTF-based MB (SSTF-MB) filter is detailed. Next, considering the benefits and importance of group structure in the sensor control scenarios, the proposed SSTF-MB filter is applied into a mobile sensor platform whose control actions (e.g., motion direction) are uncertain. Specifically, the proposed SSTF-MB filter is merged into a sensor control strategy where the Cauchy-Schwarz (CS) divergence is selected as the objective function to compute the discrepancy between two first-order moment approximations of MBs. Finally, the efficiency and performance of the proposed SSTF-MB filter and its application to sensor control scenario is well demonstrated in the simulation experiments.
A Compositional Theory of Linearizability

Compositionality is at the core of programming languages research and has become an important goal toward scalable verification of large systems. Despite that, there is no compositional account of linearizability, the gold standard of correctness for concurrent objects. In this paper, we develop a compositional semantics for linearizable concurrent objects. We start by showcasing a common issue, which is independent of linearizability, in the construction of compositional models of concurrent computation: interaction with the neutral element for composition can lead to emergent behaviors, a hindrance to compositionality. Category theory provides a solution for the issue in the form of the Karoubi envelope. Surprisingly, and this is the main discovery of our work, this abstract construction is deeply related to linearizability and leads to a novel formulation of it. Notably, this new formulation neither relies on atomicity nor directly upon happens-before ordering and is only possible because of compositionality, revealing that linearizability and compositionality are intrinsically related to each other. We use this new, and compositional, understanding of linearizability to revisit much of the theory of linearizability, providing novel, simple, algebraic proofs of the locality property and of an analogue of the equivalence with observational refinement. We show our techniques can be used in practice by connecting our semantics with a simple program logic that is nonetheless sound concerning this generalized linearizability.
Deep Feature Engineering in Colposcopy Image Recognition: A Comparative Study

Feature fusion techniques have been proposed and tested for many medical applications to improve diagnostic and classification problems. Specifically, cervical cancer classification can be improved by using such techniques. Feature fusion combines information from different datasets into a single dataset. This dataset contains superior discriminant power that can improve classification accuracy. In this paper, we conduct comparisons among six selected feature fusion techniques to provide the best possible classification accuracy of cervical cancer. The considered techniques are canonical correlation analysis, discriminant correlation analysis, least absolute shrinkage and selection operator, independent component analysis, principal component analysis, and concatenation. We generate ten feature datasets that come from the transfer learning of the most popular pre-trained deep learning models: Alex net, Resnet 18, Resnet 50, Resnet 10, Mobilenet, Shufflenet, Xception, Nasnet, Darknet 19, and VGG Net 16. The main contribution of this paper is to combine these models and then apply them to the six feature fusion techniques to discriminate various classes of cervical cancer. The obtained results are then fed into a support vector machine model to classify four cervical cancer classes (i.e., Negative, HISL, LSIL, and SCC). It has been found that the considered six techniques demand relatively comparable computational complexity when they are run on the same machine. However, the canonical correlation analysis has provided the best performance in classification accuracy among the six considered techniques, at 99.7%. The second-best methods were the independent component analysis, least absolute shrinkage and the selection operator, which were found to have a 98.3% accuracy. On the other hand, the worst-performing technique was the principal component analysis technique, which offered 90% accuracy. Our developed approach of analysis can be applied to other medical diagnosis classification problems, which may demand the reduction of feature dimensions as well as a further enhancement of classification performance.
FuzzyGAN: Fuzzy generative adversarial networks for regression tasks

Generative Adversarial Networks (GANs) are well-known tools for data generation and semi-supervised classification. GANs, with less labeled data, outperform Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) in classification tasks. The success of GANs in classification tasks motivates the development of GAN-based techniques for semi-supervised regression tasks. However, developing GANs for regression introduces two major challenges: (1) inherent instability in the GAN formulation and (2) performing regression and achieving stability simultaneously. This paper introduces techniques that show improvement in the GANs' regression capability. We bake a differentiable fuzzy logic system at multiple locations in a GAN. The fuzzy logic takes the output of either the generator or the discriminator to predict the output, y, and evaluate the generator's performance. We outline the results of applying the fuzzy logic system across multiple GANs and summarize each approach's efficacy. This paper shows that adding a fuzzy logic layer can enhance GAN's ability to perform regression; the most desirable injection location is problem-specific, and we show this through experiments over various datasets. Besides, we demonstrate empirically that the fuzzy-infused GANs are competitive with the DNNs.