Reviewerly/exHarmony huggingface dataset:
Sharding-Based Proof-of-Stake Blockchain Protocols: Key Components &amp; Probabilistic Security Analysis

Blockchain technology has been gaining great interest from a variety of sectors including healthcare, supply chain, and cryptocurrencies. However, Blockchain suffers from a limited ability to scale (i.e., low throughput and high latency). Several solutions have been proposed to tackle this. In particular, sharding has proved to be one of the most promising solutions to Blockchain's scalability issue. Sharding can be divided into two major categories: (1) Sharding-based Proof-of-Work (PoW) Blockchain protocols, and (2) Sharding-based Proof-of-Stake (PoS) Blockchain protocols. The two categories achieve good performances (i.e., good throughput with a reasonable latency), but raise security issues. This article focuses on the second category. In this paper, we start by introducing the key components of sharding-based PoS Blockchain protocols. We then briefly introduce two consensus mechanisms, namely PoS and practical Byzantine Fault Tolerance (pBFT), and discuss their use and limitations in the context of sharding-based Blockchain protocols. Next, we provide a probabilistic model to analyze the security of these protocols. More specifically, we compute the probability of committing a faulty block and measure the security by computing the number of years to fail. We achieve a number of years to fail of approximately 4000 in a network of 4000 nodes, 10 shards, and a shard resiliency of 33%.
Blockchain-Based Continuous Knowledge Transfer in Decentralized Edge Computing Architecture

Edge computing brings computational ability to network edges to enable low latency based on deploying devices close to the environment where the data is generated. Nevertheless, the limitation of size and energy consumption constrain the scalability and performance of edge device applications such as deep learning, although, cloud computing can be adopted to support high-performance tasks with centralized data collection. However, frequently communicating with a central cloud server brings potential risks to security and privacy issues by exposing data on the Internet. In this paper, we propose a secure continuous knowledge transfer approach to improve knowledge by collaborating with multiple edge devices in the decentralized edge computing architecture without a central server. Using blockchain, the knowledge integrity is maintained in the transfer process by recording the transaction information of each knowledge improvement and synchronizing the blockchain in each edge device. The knowledge is a trained deep-learning model that is derived by learning the local data. Using the local data of each edge device, the model is continuously trained to improve performance. Therefore, each improvement is recorded as the contribution of each edge device immutably in the decentralized edge computing architecture.
Object Detection for Hazardous Material Vehicles Based on Improved YOLOv5 Algorithm

Hazardous material vehicles are a non-negligible mobile source of danger in transport and pose a significant safety risk. At present, the current detection technology is well developed, but it also faces a series of challenges such as a significant amount of computational effort and unsatisfactory accuracy. To address these issues, this paper proposes a method based on YOLOv5 to improve the detection accuracy of hazardous material vehicles. The method introduces an attention module in the YOLOv5 backbone network as well as the neck network to achieve the purpose of extracting better features by assigning different weights to different parts of the feature map to suppress non-critical information. In order to enhance the fusion capability of the model under different sized feature maps, the SPPF (Spatial Pyramid Pooling-Fast) layer in the network is replaced by the SPPCSPC (Spatial Pyramid Pooling Cross Stage Partial Conv) layer. In addition, the bounding box loss function was replaced with the SIoU loss function in order to effectively speed up the bounding box regression and enhance the localization accuracy of the model. Experiments on the dataset show that the improved model has effectively improved the detection accuracy of hazardous chemical vehicles compared with the original model. Our model is of great significance for achieving traffic accident monitoring and effective emergency rescue.
A survey on what users think about SysML

Abstract Systems Modeling Language (SysML) has been applied in the past years to a variety of software and systems engineering projects, by hundreds of researchers, engineers, and other systems and software professionals. Thus, it is expected that all this experience has been described in research articles. Therefore, we propose a survey describing what practitioners and researchers think about this modeling language, the actual use of SysML, and how SysML is used in the software and system engineering life cycle. This article describes a survey on SysML, answering questions such as the most used diagrams for each phase of the development of a system, the most common domains, and other data about the participants. The survey was answered by 343 participants from 38 countries, mostly systems engineers, software and systems architects, and researchers. Industry and academia can use our results (i) for assisting researchers and engineers to select appropriate diagrams for each software and systems development phase, (ii) for a better understanding of which industry domains SysML is most commonly applied, (iii) as a reference for identifying which types of systems are modeled with SysML, (iv) for knowing which software tools are most used, and (v) which other modeling languages are most commonly integrated with SysML for software and systems development.
Distance transform learning for structural and functional analysis of coronary artery from dual-view angiography

The structural and functional analysis of coronary arteries in X-ray angiography (XRA) images has great clinical importance for the intraoperative treatment of coronary artery disease. However, due to the complexity of image surroundings, the non-intuition of functional information, and the complex relationships between structure and function, the structural and functional analysis in XRA images is a challenging task. To address these problems, we are proposing an efficient framework, termed AngioSFA. It can effectively associate structural analysis with functional analysis. The AngioSFA mainly consists of a distance transform segmentation network (DTSN) for structural analysis and a dual-view sequential learning network (DSLN) for functional analysis. Specifically, DTSN exploits distance transform in the encoder–decoder architecture to predict the distance map of the artery structure. It considers the geometric property of coronary artery structure to preserve a proper shape prototype. DSLN exploits a dual-view temporal convolutional network to learn the non-intuitive physiological knowledge related to the functional significance from the radius sequences extracted from dual-view XRA images. Furthermore, a commonality sharing module is designed to map the latent embeddings of radius sequences into a shared latent space through orthogonal mapping. Moreover, we introduce a dual-view interaction module to explore the intra-view attentions and the inter-view correlations among the anatomical features of coronary arteries. Extensive experiments on two clinical datasets show that AngioSFA achieves an overall dice of 91.06% for structural analysis and an overall precision of 89.96% for functional analysis. The experimental results and the discussions demonstrate that AngioSFA has a great potential for the efficient intraoperative treatment of coronary artery disease.
A Data Augmentation Method for English-Vietnamese Neural Machine Translation

The translation quality of machine translation systems depends on the parallel corpus used for training, in particular the quantity and quality of the corpus.However, building a high-quality and large-scale parallel corpus is complex and expensive, particularly for a specific domain parallel corpus.Therefore, data augmentation techniques are widely used in machine translation.The input of the back-translation method is monolingual text, which is available from many sources, and therefore this method can be easily and effectively implemented to generate synthetic parallel data.In practice, monolingual texts can be collected from different sources, in which sources from websites often have errors in grammar and spelling, sentence mismatch or freestyle.Therefore, the quality of the output translation is reduced, leading to a low-quality parallel corpus generated by back-translation.In this study, we propose a method for improving the quality of monolingual texts for back-translation.Moreover, we supplemented the data by pruning translation table.We experimented with an English-Vietnamese neural machine translation using the IWSLT2015 dataset for training and testing in the legal domain.The results showed that the proposed method can effectively augment parallel data for machine translation, thereby improving translation quality.In our experimental cases, the BLEU score is increased by 16.37 points compared to the baseline system.
Coherent Long-Time Integration and Bayesian Detection With Bernoulli Track-Before-Detect

We consider the problem of detecting small and manoeuvring objects with staring array radars. Coherent processing and long-time integration are key to addressing the undesirably low signal-to-noise/background conditions in this scenario and are complicated by the object manoeuvres. We propose a Bayesian solution that builds upon a Bernoulli state space model equipped with the likelihood of the radar data cubes through the radar ambiguity function. Likelihood evaluation in this model corresponds to coherent long-time integration. The proposed processing scheme consists of Bernoulli filtering within expectation maximisation iterations that aims at approximately finding complex reflection coefficients. We demonstrate the efficacy of our approach in a simulation example.
Semi-Supervised Variational User Identity Linkage via Noise-Aware Self-Learning

User identity linkage, which aims to link identities of a natural person across different social platforms, has attracted increasing research interest recently. Existing approaches usually first embed the identities as deterministic vectors in a shared latent space, and then learn a classifier based on the available annotations. However, the formation and characteristics of real-world social platforms are full of uncertainties, which makes these deterministic embedding based methods sub-optimal. Besides, semi-supervised models utilize the unlabeled data to help capture the intrinsic data distribution. However, the existing semi-supervised linkage methods heavily rely on the heuristically defined similarity measurements to incorporate the innate closeness between labeled and unlabeled samples. Such manually designed assumptions may not be consistent with the actual linkage signals and further introduce the noises. To address the mentioned limitations, in this paper we propose a novel Noise-aware Semi-supervised Variational User Identity Linkage (NSVUIL) model. Specifically, we first propose a novel supervised linkage module to incorporate the available annotations. Each social identity is represented by a Gaussian distribution in the Wasserstein space to simultaneously preserve the fine-grained social profiles and model the uncertainty of identities. Then, a noise-aware self-learning module is designed to faithfully augment the few available annotations, which is capable of filtering noises from the pseudo-labels generated by the supervised module. The filtered reliable candidates are added into the labeled set to provide enhanced training guidance for the next training iteration. Empirically, we evaluate the NSVUIL model over multiple real-world datasets, and the experimental results demonstrate its superiority.
Gradient Prior Dilated Convolution Network for Remote Sensing Image Super-Resolution

Super-resolution (SR) aims to recover highresolution (HR) image from a single or multiple low-resolution (LR) images, compensating for the limitations of satellite sensor imaging. Deep convolutional neural networks (CNN) have made great achievement in remote sensing image super resolution. In this letter, we propose a novel gradient prior dilated convolutional network (GPDCN) for remote sensing images SR, which obtainscontextual spatial connections and alleviates structural distortions. The GPDCN comprise a multi-scale feature extraction network and a feature reconstruction network. The former employs a double-path dilated residual block (DPDRB) with dilation convolution to increase a receptive field, a global selfattention module (GSA) to detect long-range reliance among image patches, and a gradient propagation network (GPN) to extract high-level gradient information. The latter uses the MHOA module to reconstruct the feature by collecting the highorder characteristics of multiple frequency bands. Experiments with the Massachusetts_Roads and 3K VEHICLE_SR datasets demonstrate that the GPDCN outperforms recent techniques concerning both quantitative and qualitative measures.
Toward Interactive Self-Supervised Denoising

Self-supervised denoising frameworks have recently been proposed to learn denoising models without noisy-clean image pairs, showing great potential in various applications. The denoising model is expected to produce visually pleasant images without noise patterns. However, it is non-trivial to achieve this goal using self-supervised methods because 1) the self-supervised model is difficult to restore the perceptual information due to the lack of clean supervision, and 2) perceptual quality is relatively subjective to users’ preferences. In this paper, we make the first attempt to build an interactive self-supervised denoising model to tackle the aforementioned problems. Specifically, we propose an interactive two-branch network to effectively restore perceptual information. The network consists of a denoising branch and an interactive branch, where the former focuses on efficient denoising, and the latter modulates the denoising branch. Based on the delicate architecture design, our network can produce various denoising outputs, allowing the user to easily select the most appealing outcome for satisfying the perceptual requirement. Moreover, to optimize the network with only noisy images, we propose a novel two-stage training strategy in a self-supervised way. Once the network is optimized, it can be interactively changed between noise reduction and texture restoration, providing more denoising choices for users. Existing self-supervised denoising methods can be integrated into our method to be user-friendly with interaction. Extensive experiments and comprehensive analyses are conducted to validate the effectiveness of the proposed method.
Breast Cancer Classification Using Equivariance Transition in Group Convolutional Neural Networks

In computer vision, rotation equivariance and translation invariance are properties of a representation that preserve the geometric structure of a transformed input. These properties are achieved in Convolutional Neural Networks (CNNs) through data augmentation. However, achieving these properties remains a challenge. This is because CNNs are not equivariance under rotation. In this study, a novel deep neural network architecture combining a group convolutional neural network (G-CNN) built with a special Euclidean (SE2) motion group and discrete cosine transform (DCT) is proposed. The former is based on the group theory and uses SE2 to guarantee equivariance in 2D images, whereas the latter is used to encode and parameterize the model space. To restore and preserve the equivariance property of the transformed and convoluted images, the DCT was used as a rotation-invariant module. These combined techniques are employed to improve breast cancer classification and data efficiency in the CNNs processing pipeline. The developed model is tested on the rotated MNIST datasets to assess its performance. Finally, the model is applied to mammography images and achieved a high computational performance and improved inference generation in breast cancer classification with an accuracy of 94.84%.
Robot Dynamic Path Planning Based on Improved A* and DWA Algorithms

When the traditional A* algorithm is applied to robot path planning, it has the problems of low efficiency and unable to avoid obstacles dynamically. In order to solve the above problems, a fusion algorithm based on improved A* algorithm and DWA algorithm is proposed. The A* algorithm is improved in three aspects: reducing the search direction of A* algorithm to reduce the search time, adding path information parameters to dynamically adjust the weight of heuristic function, and introducing important node extraction strategy to reduce the number of turns and shorten the path. Finally, the improved A* algorithm is fused with DWA algorithm. The experimental results show that the improved fusion algorithm can realize global optimal path planning and local real-time obstacle avoidance.
Extraction of Validating Shapes from Very Large Knowledge Graphs

Knowledge Graphs (KGs) represent heterogeneous domain knowledge on the Web and within organizations. There exist shapes constraint languages to define validating shapes to ensure the quality of the data in KGs. Existing techniques to extract validating shapes often fail to extract complete shapes, are not scalable, and are prone to produce spurious shapes. To address these shortcomings, we propose the Quality Shapes Extraction (QSE) approach to extract validating shapes in very large graphs, for which we devise both an exact and an approximate solution. QSE provides information about the reliability of shape constraints by computing their confidence and support within a KG and in doing so allows to identify shapes that are most informative and less likely to be affected by incomplete or incorrect data. To the best of our knowledge, QSE is the first approach to extract a complete set of validating shapes from WikiData. Moreover, QSE provides a 12x reduction in extraction time compared to existing approaches, while managing to filter out up to 93% of the invalid and spurious shapes, resulting in a reduction of up to 2 orders of magnitude in the number of constraints presented to the user, e.g., from 11,916 to 809 on DBpedia.
Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems

In a permissioned blockchain, performance dictates its development, which is substantially influenced by its parameters. However, research on auto-tuning for better performance has somewhat stagnated because of the difficulty posed by distributed parameters; thus, it is possible only with difficulty to propose an effective auto-tuning optimization scheme. To alleviate this issue, we lay a solid basis for our research by first exploring the relationship between parameters and performance in Hyperledger Fabric, a permissioned blockchain, and we propose Athena, a Fabric-based auto-tuning system that can automatically provide parameter configurations for optimal performance. The key of Athena is designing a new Permissioned Blockchain Multi-Agent Deep Deterministic Policy Gradient (PB-MADDPG) to realize heterogeneous parameter-tuning optimization of different types of nodes in Fabric. Moreover, we select parameters with the most significant impact on accelerating recommendation. In its application to Fabric, a typical permissioned blockchain system, with 12 peers and 7 orderers, Athena achieves a throughput improvement of 470.45% and a latency reduction of 75.66% over the default configuration. Compared with the most advanced tuning schemes (CDBTune, Qtune, and ResTune), our method is competitive in terms of throughput and latency.
Model for Storing Tokens in Local Storage (Cookies) Using JSON Web Token (JWT) with HMAC (Hash-based Message Authentication Code) in E-Learning Systems

E-learning is a technology that may be used in the learning process to improve not only the distribution of learning materials but also the ability of learners to modify their abilities of various competencies contained in a database. A Web Service is used to integrate the data. Web Service is a set of standards and programming methods for sharing data between different software applications, distributing services over the internet that supports system interoperability. In this digital era, the system that will survive is one that can function on multiple platforms, and one of the options is to use web servicess. Data is exchanged in JSON format, and JSON Web Tokens are used for authentication security (JWT). The use of JWT for token-based authentication on web services can help overcome interoperability issues. JWT is stateless and allows for the inclusion of data in the token authorisation. JWT includes a number of algorithm possibilities, including HMAC. Overall, the adoption of HMAC outperforms the criteria of token generation time, token size, and token transfer speed. Storing JSON Web Token into local storage in the client browser using the HMAC algorithm has been presented in this paper. The proposed work has shown that JWTs do not need to be stored on the server but can be stored on the client browser side using local storage.
Feature Selection in the Data Stream Based on Incremental Markov Boundary Learning

Recent years have witnessed the proliferation of techniques for streaming data mining to meet the demands of many real-time systems, where high-dimensional streaming data are generated at high speed, increasing the burden on both hardware and software. Some feature selection algorithms for streaming data are proposed to tackle this issue. However, these algorithms do not consider the distribution shift due to nonstationary scenarios, leading to performance degradation when the underlying distribution changes in the data stream. To solve this problem, this article investigates feature selection in streaming data through incremental Markov boundary (MB) learning and proposes a novel algorithm. Different from existing algorithms focusing on prediction performance on off-line data, the MB is learned by analyzing conditional dependence/independence in data, which uncovers the underlying mechanism and is naturally more robust against the distribution shift. To learn MB in the data stream, the proposal transforms the learned information in previous data blocks to prior knowledge and employs them to assist MB discovery in current data blocks, where the likelihood of distribution shift and reliability of conditional independence test are monitored to avoid the negative impact from invalid prior information. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of the proposed algorithm.