Reviewerly/exHarmony huggingface dataset:
Face mask detection based on improved YOLOv3 algorithm

In the current situation of COVID-19 prevention and control, wearing masks remains an important way to prevent the transmission of the Novel Coronavirus. Aiming at the problem that the detection accuracy of the traditional YOLOv3 algorithm can still be improved, this paper proposes an improved yolov3 algorithm and applies it to the practical problem of detecting whether to wear a mask. Firstly, the algorithm introduces the residual structure of structural reparameterization in the feature extraction network named Darknet53 of YOLOv3 to obtain the input features; Secondly, the SimSPPF (Simplified Spatial Pyramid Pooling-Fast) is introduced to enhance feature extraction; Finally, an improved attention mechanism is introduced to make the model focus on regions with more prominent features. Besides, in order to ensure the accuracy of target detection, CIoU and Focal loss function was used in the training process. The results show that compared with the traditional YOLOv3, the detection accuracy of the improved algorithm for normal face and mask face is improved by 16.98% and 7.30% respectively, and the mAP is improved by 12.14 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , which can meet the requirements of daily use and lay a foundation for rapid face recognition when wearing mask. (Abstract)
A Decentralized Data Crowdsourcing Framework: A Accountability and Confidentiality Perspective

An accountable and confidential data crowdsourcing framework comprised of the trusted execution environment (TEE), blockchain, and a distributed file storage system was proposed in this paper. To begin, participants may register using their true identity message, and only the guardians (or regulators) can securely review their registration information. Then, the requester submits and the executor republishes the crowdsourcing task con-fidentially. Following that, workers collect and share data via their smart devices' Trust Zone, and executors assemble, validate data in their Intel Software Guard Extensions (SGX). Furthermore, the data exchanged between participants is encrypted, and only au-thorized users can view them. According to the analysis and exten-sive experiments, our proposed system outperforms others especially in terms of latency, throughput, and task success rate.
Spectral Feature Selection Via Low Rank Decomposition and Local Preservation

Feature selection is an important step in data mining and pattern recognition tasks. In unsupervised cases, feature selection becomes more difficult due to the lack of labels in the samples. Therefore, this paper proposes a spectral feature selection (JLRDLP) algorithm that combines low rank decomposition and local preservation. First, the model predicts pseudo-labels using spectral clustering, which jointly learns the cluster labels and feature selection matrix, enabling the algorithm to select more discriminative features. In order to realize the selection of data features, the feature selection matrix is guaranteed to be sparse by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{L}_{2,1}$</tex> -norm minimization constraint; the projection matrix is decomposed into the product of two low-rank matrices to reveal the global correlation of data features; The idea is to preserve the local geometry of the data when transforming the original data into a low-dimensional space. Finally, the experimental results on multiple real datasets show that the above has better robustness, thus improving the effect of the classification.
Mobile Robot Path Planning Based on Improved Ant Colony Algorithm

Aiming at the problems of traditional ant colony algorithm(ACO) applied to mobile robot path planning, such as easy to fall into local optimization, low search efficiency, poor convergence, and unsmooth path, an improved ant colony algorithm(LACO) is proposed to solve the above problems. In this paper, the angle steering function is introduced into the state transition probability function to make the robot path smoother; The heuristic function is improved to make its weight change dynamically in the state transition probability function, which accelerates the convergence rate of the algorithm; Parameters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> and β are adjusted adaptively to avoid the algorithm falling into local optimization; Set a pheromone concentration range to avoid the stagnation phenomenon. In order to verify the reliability of the improved ant colony algorithm, the comparisons are made in two different environments. The simulation results show that the improved ACO(LACO) applied to the mobile robot path planning has faster convergence speed, stronger searching ability, smoother path, and better stability.
Session-based Recommendation with Temporal Graph Neural Network and Contrastive Learning

Session-based recommendation aims to predict the next clicked item using the current ongoing session of an anonymous user. Since the user's information is unknown, the available information is limited. In recent years, due to the excellent performance of graph neural networks in many applications, many works have applied graph neural networks to session-based recommendation. However, we found that the conversion of session data into graph-structured data is a lossy graph encoding method, which leads to the loss of item order information in the session. In addition, only one session can be used for recommendation in session-based recommendation. Compared with other recommendations, the problem of data sparsity is more serious. Self-supervised learning can discover ground-truth samples and has great potential in solving the data sparsity problem. Therefore, we propose a session-based recommendation model based on graph neural network and contrastive learning to solve the problem of information loss and data sparsity in graph encoding. Extensive experiments on three benchmark datasets demonstrate that our model outperforms other methods.
Complex-Valued Relative Positional Encodings for Transformer

Recently, the self-attention mechanism (Transformer) has shown its advantages in various natural language processing (NLP) tasks. Since positional information is crucial to NLP tasks, the positional encoding has become a critical factor in improving the performance of the Transformer. In this paper, we present a simple but effective complex-valued relative positional encoding (CRPE) method. Specifically, we map the query and key vectors to the complex domain based on their positions. Hence, the attention weights will directly contain the relative positional information by the dot product between the complex-valued query and key vectors. To demonstrate the effectiveness of our method, we use four typical NLP tasks: named entity recognition, text classification, machine translation, and language modeling. The datasets of these tasks comprise texts of varying lengths. In the experiments, our method outperforms the baseline positional encodings across all datasets. The results show that our method is more effective for long and short texts while containing fewer parameters.
Context-based Image Inpainting with Improved Generative Adversarial Networks

Recent deep learning based approaches especially with generative adversarial networks and their variants have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually plausible image structures and textures, but often create distorted structures or blurry textures inconsistent with surrounding areas. For this problem, we proposed a novel approach which not only can repair the defective image with global and local consistency, but also is extremely lightweight. This model is an improved version of Generative Adversarial Network, which has a global and local context discriminator to guarantee the global and local consistency of generated images, utilizes Wasserstein loss to assist the training of its generator and replaces a part of standard convolution with some dilated convolution to expand the receptive field. After experimentation, we found our approach can generate satisfactory image content on the restoration problem of specific scene images such as human faces and street views.
Global optimization using a combination of differential evolution and modified Powell method

Optimization is one of the most important problems in engineering applications. Substantial algorithms have been proposed to tackle these problems, such as differential evolution(DE), which has excellent global search capability and robustness. However, like most metaheuristics, differential evolution is computationally expensive and difficult to converge to the exact global optimum. The modified Powell(MP) method is a derivative-free local optimization algorithm with fast convergence speed but is prone to fall into the local optimum trap. Combining the advantages of these two algorithms, we propose a hybrid optimization algorithm, which is called the DEMP algorithm. The DEMP algorithm is compared to other three well-known optimization algorithms by a set of benchmark functions. The results show that DEMP is not only notably better than the traditional DE Algorithm, but also highly competitive for other metaheuristic algorithms.
Salient Object Detection Based on Transformer and Multi-scale Feature Fusion

It is difficult to learn global remote semantic information based on convolutional neural network, and it is difficult to obtain multi-scale feature information based on Vision Transformer, Swin Transformer and Pyramid Vision Transformer. However, salient objects maybe involve different scales. This paper introduces Shunted Transformer as the backbone network to extract multi-scale features to achieve salient object detection. Aiming at the problem of ignoring the difference between different features and dilution of high-level features when fusing high-level and low-level features, a decoder for progressive fusion of multi-scale features is designed. In addition, to solve the problem that the boundary features obtained may not match the salient object due to the separation of the boundary prediction structure and the salient object prediction branch, this paper refers to the BIG module and optimizes its feature input. Finally, the validity of the proposed model is verified by experiments on four widely used datasets.
Deep Learning Based Wireless Device Identification Using RF Fingerprint

The wireless network physical layer identity authentication technology has received more and more attention due to its strong resistance to camouflage attacks. This work studies a physical layer method that uses radio frequency fingerprints to distinguish legitimate devices from illegal devices. This paper proposes the constellation trajectory generated by the fusion of three different length differential intervals of short, medium, and long as the device fingerprint to solve the problem that the traditional single differential interval constellation trajectory can not take into account the frequency offset resolution and the application range of frequency offset. The experimental results show that the fingerprint recognition accuracy of devices using multiple differential interval constellation trajectories is better than traditional methods. On this basis, we propose a radio frequency fingerprint identification method based on the twin neural network. Unlike most previous studies that model the identification problem as a classification problem, the algorithm proposed in this paper detects whether the device under test is a legitimate user device and can authenticate the legal device identity. The results show that the radio frequency fingerprint identification method for wireless devices based on the twin neural network can effectively and accurately identify legal users and illegal users.
Enhanced Module design based on ShuffleNetV2

Among the general lightweight deep neural network models, ShuffleNetV2 is a portable and efficient model. To further make the network lighter and more powerful in recognition, an Enhanced Shuffle Block (ESBlock) is proposed, which is a symmetric module consisting of a depth-wise separable convolutional superposition to replace ShuffleNetV2's fundamental module. Then, by re-optimizing the configuration of channels, the expansion factor within the basic block, the layer of each stage, it is possible to improve the performance. And a Portable Attention Module (PAM) is added to enhance feature extraction and model's utilization capability. Experimental findings demonstrate that the enhanced network accomplishes 94.15% and 73.52% recognition accuracy on Cifar10 and Cifar100 datasets with only 0.66 M and 0.77 M parameters, respectively. Compared with the ShuffleNetV2, the accuracy is increased by 1% and 0.75%, and parameters is decreased by 50% and 44% respectively.
Machine Translation Based on Syntactic Structure

Machine translation refers to the process of translating source language sentences into semantically equivalent target language sentences through computers, and is an important research direction in the field of natural language processing. Neural machine translation only needs to use neural networks to achieve end-to-end translation from source language to target language, which has become the mainstream direction of machine translation research. There are also some problems such as fluent but not faithful translation, difficulty in processing rare words, poor performance of low-resource languages, poor cross-domain adaptability, and low utilization of prior knowledge. Inspired by the research of statistical machine translation, it has become a hot topic in the field of neural machine translation to integrate linguistic information into the neural machine translation model and utilize the existing linguistic knowledge to alleviate the inherent difficulties faced by neural machine translation and improve the translation quality. According to the classification system of grammatical units, researches in this field can be divided into three categories: neural machine translation (NMT) integrating word structure information, neural machine translation integrating phrase structure information and neural machine translation integrating syntactic structure information. Current researches also focus on these three aspects. Firstly, the challenges and causes of neural machine translation are described, and then the research status and main results of neural machine translation based on language knowledge are introduced. At last, the questions in existing research are summarized, and the future research direction is prospected.
Online Product Decision Support Using Sentiment Analysis and Fuzzy Cloud-Based Multicriteria Model Through Multiple E-Commerce Platforms

The competitive landscape of multiple e-commerce platforms and the vast amount of product reviews associated with these platforms have supported both consumers’ online shopping decision making and also served as a reference for product attribute performance improvement. This article proposes a sentiment-driven fuzzy cloud multicriteria model for online product ranking and performance to provide purchase recommendations. In this novel model, bidirectional long short-term memory network-conditional random fields, sentiment analysis, and K-means clustering are first integrated to mine product attributes and compute sentiment values based on the reviews from various platforms. Next, considering the confidence of the sentiment value, the cloud model is combined with q-rung orthopair fuzzy sets to define the new concept of the q-rung orthopair fuzzy cloud (q-ROFC) and the interaction operational laws between q-ROFCs are given. The sentiment values of each product attribute from different platforms are cross combined and transformed into a type of q-ROFC, while multiple interactive information matrices are established. To investigate the correlation among homogeneous attributes, the q-ROFC interaction weighted partitioned Maclaurin symmetric mean operator is proposed. Finally, we provide real-world examples of online mobile phone ranking and attribute performance evaluation. The results show that our proposed method offers significant advantages in dealing with customer purchase decisions for online products and problems with performance direction identification. Managerial implications are discussed.
Traffic sign defogging based on conditional adversarial neural network pix2pixHD

A foggy environment has a great impact on the recognition of traffic signs, and it is very difficult to recognize traffic signs in a foggy environment directly. In this paper, we debug and train the conditional generative adversarial network pix2pixHD to generate a fog removal model consistent with an advanced vision task (traffic sign recognition). First, a foggy traffic sign dataset is constructed based on the atmospheric scattering model. Secondly, the idea of the conditional adversarial neural network is introduced to complete the construction and training of the pix2pixHD model. Finally, two image quality evaluation indexes, Peak Signal to Noise Ratio (PSNR) and Structural similarity (SSIM), and the Yolov5 detection algorithm are used to complete the pix2pixHD defogging model to compare with the common defogging models respectively. The experiments prove that the model trained in this paper can effectively improve the quality of foggy images and generate high-resolution images that are more compatible with the recognition of traffic signs.
Strongly Synchronized Redactable Blockchain Based on Verifiable Delay Functions

As one of the crucial features of the blockchain technique, immutability plays the most important role in winning the so-called praise of the “trust machine” for blockchain. However, there are two sides to everything. The property of immutability of blockchain is applied maliciously sometimes, say publishing harmful or even dangerous data and hindering authorities’ law enforcement. To address this issue, authorized redactability of blockchain was introduced to support block modification without lowering the fundamental basis of security and trust that is cherished on the blockchain. During the past years, several techniques of redactable blockchain were proposed, mainly based on the well-known chameleon hashing. Different from existing methodologies, we propose a new redactable blockchain scheme for permissioned settings in this article. We first employ the trapdoor verifiable delay function to attach a time-lapse proof to each block. Moreover, the trapdoor is used to quickly construct a chain fork to redact blocks that are authorized to alter. Our proposal does not need to rollback irrelevant blocks. As a remarkable and unique feature, our proposal realizes the property of strong synchronization of redaction, which means that all nodes in the blockchain will have identical views on the chain even after some blocks are altered. Security analysis shows that the consistency of the chain is guaranteed, and the long-range attack can be resisted effectively. The performance comparison shows that our method is feasible and practical.
Video Visual Relation Detection With Contextual Knowledge Embedding

Video visual relation detection (VidVRD) aims at abstracting structured relations in the form of <inline-formula><tex-math notation="LaTeX">$&lt; $</tex-math></inline-formula> <i>subject-predicate-object</i> <inline-formula><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> from videos. The triple formation makes the search space extremely huge and the distribution unbalanced. Usually, existing works predict the relationships from visual, spatial, and semantic cues. Among them, semantic cues are responsible for exploring the semantic connections between objects, which is crucial to transfer knowledge across relations. However, most of these works extract semantic cues via simply mapping the object labels to classified features, which ignore the contextual surroundings, resulting in poor performance for low-frequency relations. To alleviate these issues, we propose a novel network, termed Contextual Knowledge Embedded Relation Network (CKERN), to facilitate VidVRD through establishing contextual knowledge embeddings for detected object pairs in relations from two aspects: commonsense attributes and prior linguistic dependencies. Specifically, we take the pair as a query to extract relational facts in the commonsense knowledge base, then encode them to explicitly construct semantic surroundings for relations. In addition, the statistics of object pairs with different predicates distilled from large-scale visual relations are taken into account to represent the linguistic regularity of relations. Extensive experimental results on benchmark datasets demonstrate the effectiveness and robustness of our proposed model.
Intercity Rail Transit Platform Anomaly Detection Using Door Tracking-Based Key Frame Extraction and AnoDet Network

The anomalies in the risk space between platform doors and train will threaten the safety of intercity rail transit. However, performing anomaly detection tasks with an accurate grasp of the time gap between the train doors closed and the train departure is challenging. The scarcity of anomalous samples and the lack of prior knowledge of anomalies also pose considerable obstacles to anomaly detection. To solve the aforementioned problems, a method based on key frames extraction and a two-stage Transformer network is proposed. The switch statuses of the train door are extracted by the door tracking algorithm, which effectively provides key frames for subsequent anomaly detection. An end-to-end two-stage Transformer, which is named AnoDet, is proposed for unsupervised anomaly localization and supervised multi-classification. The AnoDet is based on an image masking strategy, dual-channel network, knowledge migration base and anomaly object classification set prediction loss. The unsupervised localization model first utilizes the masking strategy to remove a large number of image patches that may contain anomalies in the inputs. Then, it trains a dual-channel network based on the remaining normal patches to inpaint anomalous inputs into normal images. The supervised classifier proposes a knowledge migration base to provide prior knowledge of anomalies and alleviate the scarcity of anomalous samples. It also introduces a set prediction loss to achieve a natural connection with the unsupervised model. In the intercity rail station anomaly dataset, our method achieves 97.8% precision and 96.6% recall for anomaly localization and obtains 88.9% mAP for multi-classification, outperforming the state-of-the-arts.
Blockchain Data Architecture and Cyber Security

This chapter presents an overview of the architecture of blockchain and its role in the mobile networks of the future. It defines each type of blockchain and shows how blockchain maximizes security using a ledger-based design. We also highlight the advantages of blockchain and its importance in designing data platform architecture and why data architects need to participate in cybersecurity design of data platforms. These new roles and responsibilities will require data architects to develop new skillsets.