Reviewerly/exHarmony huggingface dataset:
Overview of the Application of Knowledge Graph in Anomaly Detection and Fault Diagnosis

Knowledge graph is a branch of artificial intelligence, which uses graph model to describe the relationship between knowledge and things. Using the technology of knowledge extraction, knowledge fusion and knowledge processing in knowledge graph, a large-scale knowledge base with semantic and open knowledge can be established quickly for a specific domain. Knowledge graph has gradually become an important means of knowledge management and application in various fields. This paper reviews the development history of knowledge graphs, introduces key technologies of knowledge graphs, and the application, rules and characteristics of knowledge graphs in anomaly detection and fault diagnosis. Furthermore, this paper summarizes the application of knowledge graph technology in anomaly detection and fault diagnosis in various industries, analyzes the applicability of knowledge graph in the field of anomaly detection and fault diagnosis, discusses the challenges faced in the application process, and proposes future development trends.
VitPose: Multi-view 3D Human Pose Estimation with Vision Transformer

Fusion of features from multi-view is one of the effective means to improve multi-view 3D pose estimation. It relies on robust and accurate 2D pose estimation, and effective fusion methods. If we treat the same fusion weights for predictions with different accuracy. In this case, “Bad-prediction” can have a bad effect on “Well-prediction” during the fusion process. To address these issues, inspired by previous vision transformer work, we propose a transformer framework for multi-view 3D pose estimation named VitPose, aiming to strengthen the mutual constraint of human articulation points by enhancing the large-scale information of image prediction, rather than calibrating after prediction. In addition, we design simple feedback for training fusion weights, which is used to avoid the interference of “Bad-prediction” to “Well-prediction”. We add multi-view geometric calibration that introduces the spatial information of the view into the transform structure, which is used to strengthen the connection between two views. We conducted extensive experiments on Human3.6M, which showed that our approach achieved competitive results. Specifically, We achieve 17.0 mm Mean Per Joint Position Error(MPJPE) on Human3.6M on 384×384 resolution, which is the State-of-The-Art(SOTA) method with vanilla triangulation.
Pengaruh Implementasi Content Management System Terhadap Kecepatan Kinerja Menggunakan One Way Anova

This study analyzes the performance speed measures of the Content Management System (CMS), namely Concrete, Drupal, Joomla and Wordpress. The purpose of this study was to determine whether there is a difference or not in the implementation of the four CMS. The data used are categorical and numeric types, categorical data is the CMS type and numerical data is obtained from observations made by recording the CMS installation on an offline server using the Active Presenter application, then calculating the installation time, admin system and database migration. The data obtained are then processed using the One Way ANOVA method in Statistical Product and Services Solution (SPSS) version 23. The indicator used for data sampling is the performance speed of the CMS which is measured in seconds. From data processing and analysis using One Way Anova, it can be concluded that from the CMS used (Concrete, Drupal, Joomla and Wordpress) there is no significant difference to the speed of CMS performance. Each CMS has advantages, but in terms of speed of performance there is no significant difference.
Breakthrough of error correction in quantum computing

By using a superconducting processor, Google Quantum AI group demonstrated that a logic qubit realized by surface code of quantum error correction performs better when the number of physical qubits increases. Two surface codes to encode logic qubits for scaling are realized experimentally, with multiple cycles of quantum error correcting assisted by ancillary qubits. This result can be considered as an important step toward fault-tolerant quantum computers. In this review paper, we introduce briefly the mechanism of quantum error correction. As an example, Shor’s nine-qubit error correction code is explained. Then, the new experiments of Google quantum AI group are introduced to show their significance in scaling. The advances in other quantum error correction experiments are also reviewed. Finally, the development of quantum computers is discussed.
Divide-and-conquer Bayesian inference in hidden Markov models

Divide-and-conquer Bayesian methods consist of three steps: dividing the data into smaller computationally manageable subsets, running a sampling algorithm in parallel on all the subsets, and combining parameter draws from all the subsets. These methods use the combined parameter draws for efficient posterior inference in massive data settings. Existing divide-and-conquer methods have a major limitation in that their first two steps assume that the observations are independent. We address this problem by developing a divide-and-conquer method for Bayesian inference in parametric hidden Markov models, where the state space is known and finite. Our main contributions are two-fold. First, we partition the data into smaller blocks of consecutive observations and modify the likelihood on every time block. For any time block, the posterior distribution computed using the modified likelihood is such that its variance has the same asymptotic order as that of the true posterior. Second, suppose the number of subsets is chosen appropriately depending on the mixing properties of the hidden Markov chain. In that case, we show that the subset posterior distributions defined using the modified likelihood are asymptotically normal as the subset sample size tends to infinity. This result facilitates using any existing combination algorithm in the third step. We show that the combined posterior distribution obtained using one such algorithm is close to the true posterior distribution in 1-Wasserstein distance under widely used regularity assumptions. Our numerical results show that the proposed method provides an accurate approximation of the true posterior distribution than its competitors in simulation studies and a real data analysis.
Acceptance of an initial coin offering for investment in a developing economy

Initial coin offerings (ICOs), or token offerings, play a critical role in the digital economy as they are blockchain-based smart contracts used to raise funds by issuing cryptocurrency tokens (Momtaz, 2020). This research aims to study the factors affecting the acceptance of ICOs for investment and to study whether cognition in ICO investment affects decision-making in investment in Thailand. In this quantitative study, the data were collected from online questionnaires completed by a sample group of 402 Thai investors who currently invest in financial instruments, and then the data were analyzed using binary logistic regression. The findings revealed that income, the value of investing in financial instruments, saving, perception towards media, and cognition in investment have an influence on the acceptance of ICOs. The paper suggests that investors who are interested in investing in ICOs should study the risks of investment thoroughly before investing. Besides, relevant government agencies should clearly set the rules and regulations in order to protect the inventors and, in the meanwhile, the fundraising could not be obstructed as well.
A Survey of Decision Support Mechanisms for Negotiation

This paper introduces a dependency analysis and a categorization of conceptualized and existing economic decision support mechanisms for negotiation. The focus of our survey is on economic decision support mechanisms, although some behavioural support mechanisms were included, to recognize the important work in that area. We categorize support mechanisms from four different aspects: (i) economic versus behavioral decision support, (ii) analytical versus strategical support, (iii) active versus passive support and (iv) implicit versus explicit support. Our survey suggests that active mechanisms would be more effective than passive ones, and that implicit mechanisms can shield the user from mathematical complexities. Furthermore, we provide a list of existing economic support mechanisms.
A Shallow Convolutional Neural Network Model for Breast Cancer Histopathology Image Classification

Identification of malignancy using histopathology image processing is a crucial method for cancer diagnosis. A model to classify images based on deep convolutional neural networks (CNNs) attains a promising performance in the computer vision for a large dataset but creates a computational complexity for a comparatively small histopathology dataset. A shallow CNN model is proposed in this paper, which classifies the histopathology images of breast tissues. The images are classified as either malignant or benign by the model. It is tested on two publicly available, benchmark breast cancer histopathology datasets. According to the result, the proposed model achieves (95.19 ± 8.50) accuracy and requires a less no. of parameters (8, 052,062) for training as compared to the existing deep CNN models implemented on the same protocol.
Sub-pixel Convolution and Edge Detection for Multi-view Stereo

The deep multi-view stereo (MVS) approaches generally construct a cost volume pyramid in a coarse- to- fine manner to regularize and regress the depth or disparity, which is often built upon a feature pyramid encoding geometry or an image pyramid. A pyramid is an excellent approach to reducing memory, and many papers said even low-resolution images or features contain enough information for estimating low-resolution depth maps. However, recent papers show that the higher the image resolution, the better the output depth map, which means the resolution of depth maps in each stage cause effect on the final outputs. Therefore, we think the low-resolution depth map may not be enough for the high-resolution depth map. In this paper, we propose a sub-pixel upsampling module for post-processing the cost volume to generate a big resolution depth map at each stage. Besides, we also proposed an edge-weighted loss function for optimizing those inaccurate depth values in the edge regions of objects. Finally, we implement them on CasMVSNet, showing the effectiveness of our proposed method. The content of abstract.
A Research on Data Secure Access Control Mechanism Based on Zero Trust and Attribute Encryption in Medical Cloud

With the rapid development of cloud computing, a new health information exchange mode of personal health record (PHR) on cloud has become a hot topic. Many medical services are outsourced or provided by the third-party service provider, which is bound to lose direct control of the PHR files, while the cloud service provider may collude with other unauthorized users to disclose legitimate users' privacy in order to gain profits. At present, many scholars apply attribute encryption to cloud storage platform to ensure that PHR files are not transmitted in plaintext during the whole access process, but it cannot solve the semi-trust problem of cloud service providers and key distribution servers themselves. Therefore, in order to solve problem of data security and privacy protection of PHR data sharing scheme on semi-trusted servers, this paper introduces the security concept of “zero trust” to construct a data security access control framework based on zero trust and attribute encryption in the medical cloud. This framework can realize real-time monitoring and trust measurement of the behavior of the client and the server, and can dynamically adjust the access permissions of PHR user resources according to the measurement results. The security analysis results show that the proposed scheme can achieve more secure and reliable data access control in medical cloud.
A Novel Framework for Online Knowledge Distillation

Traditional knowledge distillation transfers the capabilities of a large network to a smaller network through a two-stage training. Recent Online knowledge distillation uses aggregated intermediate predictions of multiple peer models as the goal of the peer model. Although this approach can get rid of large teacher models, the use of simple aggregation functions makes the problem of inter-peer homogenization severely, which affects the effectiveness of distillation. In this paper, we propose a novel online knowledge distillation strategy, which resists the homogeneity problem by augmenting the inputs randomly. Specifically, we construct a multi-network model, and intervene in the training process by implementing a set pattern of image enhancements, allowing the network to benefit from different enhancements during the training process, as a way to enrich the diversity among peers. Experimental results on CIFAR-10/CIFAR-100 show the significant improvement on several structures.
A Lightweight and Robust Cross-Domain Authentication Scheme Based on Master-Slave Blockchain

In the era of network informatization, the Internet of Things has more and more complex requirements for information interaction across trust domains, and cross-domain identity authentication has become the focus of research and attention by security personnel. However, IoT devices often lack computing power and lack of security protection measures. The existing authentication schemes are complex and have poor robustness, which is not suitable for the IoT environment. to address this problem, this paper proposes a lightweight and robust cross-domain authentication scheme based on master-slave blockchain. Our method adopts the master-slave blockchain mode for cross-domain identity authentication, uses the trust evaluation mechanism to measure the reputation value of nodes, and selects nodes with high reputation values as cross-domain communication nodes. Experimental results show that our method is not only more robust than similar methods but also takes less time to complete the authentication.
CNN or RNN: Review and Experimental Comparison on Image Classification

Image classification is widely used in different domains such as autonomous driving and medical care, etc. In this paper, we focus on using deep learning model to identify image. We also analysis the performance of CNN-based model and RNN-based model on image classification. Lots of relevant datasets are applied to image classification such as ImageNet dataset and MINIST dataset. In this paper, we conduct experiments on these two datasets. The experimental results of training and testing on those two datasets shows that CNN model is better than RNN model in image classification.
Location-Based Scene Reconstruction for Long-Tail Recognition

Real-world data often exhibit a long-tailed distribution with severe class imbalance. In such cases, the majority class dominates the deep learning training, which changes the decision boundary of the minority class and reduces the classification accuracy. In this paper, we propose a novel location-based scene reconstruction(LSR) data augmentation method for long-tail recognition. This approach uses a gradient localization method to increase the scenes of tail class samples and enhance the discrimination of the model between head and tail classes, thus the accuracy of long-tail recognition is improved. Experiments on two benchmark datasets show that the LSR method achieves state-of-the-art performance on the long-tail recognition task. More importantly, our method can be easily combined with other classification methods and improves the performance of these traditional classification methods
Dental Detection and Classification of YOLOv3-SPP based on Convolutional Block Attention Module

The purpose of this research paper is to implement the tooth target detection task by deep convolutional neural networks. In order to solve the problems of low accuracy of target detection due to the high similarity between teeth and complex tooth textures, an improved tooth detection method with YOLOv3-SPP model is proposed in this paper. This method incorporates the convolutional block attention module (CBAM) in the YOLOv3-SPP algorithm framework, and improves the performance of the network features by adding the channel attention mechanism and spatial attention mechanism to the feature extraction network to enhance the saliency of the tooth target region in the image. Secondly, CIoU border regression loss is introduced to improve the localization accuracy. In addition, a Non-maximum suppression (NMS) method is used to solve the candidate frame overlap problem. Through experiments, it is shown that the mAP of the modified YOLOv3-SPP target detection model is improved to 86.8 percent, which indicates that the improved model can be applied to the detection of teeth.
UAV Tracking by Identification Using Deep Convolutional Neural Network

In recent years, the frequent occurrence of UAV black flight and intrusion incidents has made their effective detection and tracking a research hotspot. This paper focuses on the problem of poor detection and tracking when traditional detection and IMM tracking algorithms are used to detect and track UAVs, we improved the tracking accuracy by introducing the flight pattern recognition probability into the IMM to assisted adjusting the model update probability. Firstly, a multi-layer deep learning UAV detection and identification algorithm is proposed to detect and identify the presence and flight patterns of UAVs. Secondly, an IMM algorithm based on flight pattern assisted adjustment (FAMP-IMM) is proposed by introducing the flight pattern recognition probability from neural networks as auxiliary knowledge into the IMM algorithm and continuously correcting the model update probability. The experimental results demonstrate that the detection and recognition accuracy reaches as higher to 99% with the multi-stage DL approach, the F AMP-IMM algorithm greatly decrease the model switching lag and tracking performance compared with the IMM algorithm.
LBFP: A Lightweight Bitcoin-Based Fair Payment for Outsourced Computation

The widespread application of outsourced computation meets the growing computing demands. However, due to the payment issues and data security, achieving fair interaction between clients and workers in a zero-trust environment has become a bottleneck of restricting the development of outsourced computation. Most existing solutions, including blockchain-based and traditional e-cash system, may compromise the client's or the worker's fairness. To deal with this issue, several fair payment schemes based on trusted third parties (TTP) have been proposed in the literature. However, the involvement of TTP reduced the feasibility of these schemes. In order to remove the aforementioned limitations, we propose a lightweight bitcoin-based fair payment (LBFP) scheme for outsourced computation environment. LBFP is able to achieve the robust fairness and compatibility by integrating cryptographic components (including one-way accumulator, bitcoin-based timed commitment, and symmetric encryption). In addition, we give the security analysis of LBFP and make a comparison with the related schemes which show that LBFP has a better application prospect.
Identification of Modulation Type Based on Time-Fequency Analysis and Neural Networks

Modulation type recognition of signals belongs to the category of signal recognition technology, which can provide a basis for radio monitoring and band resource management, and plays an important role in applications such as signal detection, communication countermeasures and cognitive radio. Compared to traditional modulation recognition methods, deep learning based modulation recognition can automatically learn the characteristics of the modulated signals and improve the recognition performance in complex environments. Therefore, this paper proposes an identification method based on SPWVD transform and deep residual network for the identification of modulation types. The proposed method first uses the SPWVD transform to extract the features of the signals and generate a grey-scale map. Secondly, the grey-scale map is compressed and transformed into a network. ResNet is used to learn the time-frequency characteristics of the signals to identify 2ASK, 2FSK, 2DPSK, LFM, VFM and FH six modulation types. Compared with existing algorithms, this method can effectively reduce the influence of noise interference and cross terms; increase the depth of the network by using the characteristics of residual blocks can improve the recognition performance of the network. Experimental results demonstrate the identification accuracy of 94% or better was acquired at SNR of 0 dB.
Unified Modeling of Path Planning and Tracking Control Based on Improved Genetic Algorithm

Vehicle path planning and tracking control are the key to achieving autonomous driving. In this paper, a combined algorithm based on artificial potential field algorithm and genetic algorithm is proposed. Based on information about the vehicle's driving environment, establishing potential field functions in different environments. And the initialized populations in the genetic algorithm are optimized using the established artificial potential fields. Planning a reliable driving path. Using model predictive control algorithms. Tracking control of the planned path. Unified modeling was achieved. Experimental results show that the improved path planning algorithm and tracking control method are able to plan and track the path well.
Multimodal Sentiment Analysis of Social Media Based on Top-Layer Fusion

With the diversification of social media, users tend to express their feelings and opinions in the form of images and text. Compared with single-modal data, multimodal data can express users' feelings and sentiments more vividly and interestingly. Based on these, in this paper, we propose a top-layer fusion strategy and we construct a multimodal sentiment analysis model based on this strategy. The model can take into account the advantages of different fusion methods and can take full advantage of the superior performance of the single-modal sentiment analysis model. The experimental results on the MVSA-Single dataset show that the proposed model outperforms strong baseline models by large margins, demonstrating the effectiveness of the proposed model.
Open-Set Recognition of LPI Radar Signals Based on Deep Class Probability Output Network

Radar signal waveform recognition is one of the key parts of Radar Emitter Identification (REI). With the developing of low probability of intercept (LPI) radar signals, there have been many researches on LPI signal waveform recognition. Method based on time-frequency analysis (TFA) and Deep Learning (DL) network that can automatically extract features from images is becoming popular. However, the most existing DL-based methods of signal waveform recognition are time-consuming in preprocessing process and more importantly, is invalid in open-set scenario. To solve these problems, this paper proposes an open-set radar signal recognition method named Deep Class Probability Output Network (DCPON) which combines the great feature extraction ability of DL and the interpretability of CPON fitting probability distribution. It can recognize new unknown signal waveforms while classifying known ones and is more reliable than previous DL-based methods. In addition, the one-dimensional residual network (1D ResNet) is used as the backbone network, and the cross-entropy loss and center loss are used for training. We compare the different preprocessing methods and set the real part and imaginary part of Discrete Fourier Transform (RIODFT) of signals as the input of two channels respectively. Several simulated experiments demonstrate that the proposed method has good performance in the open electronic environment.