Reviewerly/exHarmony huggingface dataset:
Structure from Motion with variable focal length: Interconnected fuzzy observer

In this paper, a 3D point feature depth and camera focal length estimation is proposed, using a partially calibrated low cost monocular camera. The camera intrinsic parameters are known, except for the focal length, which may vary across different views. The camera perspective projection model is augmented using dynamic extension approach, then decomposed into two interconnected subsystems. The subsystems are described as quasi-Linear Parameter Varying (qLPV) systems with unmeasured premise variables for which an interconnected fuzzy observer is designed. Necessary and sufficient conditions to ensure the observer existence are presented. The error convergence analysis is performed based on Lyapunov theory associated with Lipchitz condition. Gains that guarantee the asymptotic stability of the estimation error are computed in terms of Linear Matrix Inequalities (LMI) with eigenvalues clustering in LMI region to improve the estimation performance.
Hybrid KGA algorithm For Clustering RBC’s Morphological Abnormalities

This paper proposes a new hybrid algorithm (KGA) of two known algorithms for the data clustering process. These algorithms are the Genetic Algorithm and K-means. In this paper, a comparison of the hybrid KGA algorithm&#x2019;s performance with the original algorithm&#x2019;s Genetic algorithm and K-means. The comparison was based on clustering Red Blood Cells morphological abnormalities features. The RBC&#x2019;s (red blood cells) morphological are represented through three types of features: Shape descriptor features, Shape signatures features and wavelet features. Through this paper, a study of the three features set was to find which was more informative of the RBC&#x2019;s morphological abnormalities. The extracted features obtained from blood film images collected from two known hospitals in Gaza strip. The features with different sizes, processed and normalized. The results Demonstrate clearly that the shape descriptor features were the best, with GA 99.48%, K-means 83.16% and KGA 99.56%.
Proof of Solution: Implementation of Work History as Stake in Blockchain Applications

Blockchain is said to be one of the most prominent inventions since the advent of the internet. Cryptocurrency is one of the most well-known blockchain applications and has shown tremendous growth in recent times. Most cryptocurrency platforms currently utilize Proof of Work as the consensus mechanism. However, this includes a high energy cost in the form of repeated hash computations to keep the blockchain tamper-proof. The proposed work combines the idea of Useful Work and Proof of Stake, where blockchain nodes train machine learning models submitted by users and earn Work Stake Tokens on successful completion, which can be used as stake in the Proof of Stake algorithm. Proof of Solution efficiently utilizes the vast computational resources of miners to perform useful work. The combined approach reduces redundant model training via multiple nodes by half, improving on the drawback of Proof of Useful Work.
Use of Blockchain in IoT Devices Security: A Systematic Mapping Study

Information security in IoT devices is considered a great challenge within the field of computing, which has been growing based on the architecture and the resources that compose it. The implementation of Blockchain has provided improvements in security and reliability for handling data. However, there is no concrete information on techniques, advantages, or results of the use of Blockchain in IoT devices. To identify relevant information on the use of Blockchain in smart devices and present the results as a basis for future research, we promoted a systematic mapping study to classify the selected studies and summarize their content. A limited number of articles was evidenced according to the proposed search chain, only since 2017 an interest in the subject has been observed, the same interest that grows rapidly over time. In addition, disadvantages and shortcomings were identified when applying Blockchain in IoT. Therefore, there is a need to evaluate the performance of Blockchain in different environments and new investigations that provide better results as computer vulnerabilities and their possible effects on devices used in IoT increase.
Analysis of Prediction Data for the Third Wave of COVID-19 in Bogor Regency

The COVID-19 pandemic is far from over. The government has carried out several policies to suppress the development of COVID-19 is no exception in Bogor Regency. However, the public still has to be vigilant especially now we will face a year-end holiday that can certainly be a trigger for the third wave of COVID-19. Therefore, researchers aim to make predictions of the increase in positive cases, especially in the Bogor Regency area to help the government in making policies related to COVID-19. The algorithms used are Gaussian Process, Linear Regression, and Random Forest. Each Algorithm is used to predict the total number of COVID-19 cases for the next 21 days. Researchers approached the Time Series Forecasting model using datasets taken from the COVID-19 Information Center & Coordinationn Center website. The results obtained in this study, the method that has the highest probability of accurate and appropriate data contained in the Gaussian Process method. Prediction data on the Linear Regression method has accurate results with actual data that occur with Root Mean Square Error 1202.6262.
Let’s Re-Sign! Analysis and Equivocation-Resistant Distribution of OpenPGP Revocations

Certificates are commonly used for enabling secure communication. Private, secure and efficient dissemination of their revocation is essential to maintain security properties such as confidentiality. However, managing revocations remains a challenge not only on the Web but also for protocols building on OpenPGP, such as Email. Currently, no dedicated protocol for managing revocations exists. Instead, clients upload or fetch full certificates, which may include a revocation signature. This paper presents a dedicated protocol for disseminating revocations which improves the security over the status quo by providing incentives to the server to behave benignly. The protocol builds on the Certificate Transparency log of the Web to store and retrieve OpenPGP revocations. It thus inherits the property of being resilient against equivocation attacks. Compared to the status quo, the dedicated protocol does not serve the full certificate containing personally identifiable information thus improving privacy. This paper also shows how serving the revocation signature separately from the full certificate improves efficiency.
Research on a New Analysis Algorithm of Virtual Trajectory Cryptography Technology for Password Encryption and Decryption Through Base Expanding

Abstract In the rapidly developing Internet era, network security and data sharing coexist. With the progress of information reform, all walks of life need to ensure the security of data information. Password is a tool to protect their privacy, property, information and important data, and some criminals through technical means to crack the password, peeping and stealing other people’s important information, data, meanwhile, the data caused information and property losses also happen. Information security has become a strategic issue that people must pay attention to, which is related to social stability, economic development and national security. In this paper, we study a new virtual trajectory password technology, analyzes the algorithm, complete the structure design of code disk and solve the problem of random generation of code base in code disk. The method of “base expanding” applied to enhance the strength coefficient of the password, and the random sequence generated in the chaotic encryption algorithm is used for encryption and decryption, and a random reference graph “code disk” is generated as an auxiliary memory method to help people remember some special lengthened passwords that cannot be remembered conventionally.
Multi-class Object Detection in Fusion Remote Sensing Images Based on Deep Learning

Deep learning has been applied successfully in the machine visual field, and it has already surpassed humans in object detection of images. Object detection is a significant content of remote sensing image analysis and is a key link to transform image data into application results. Based on a self-made high-resolution remote sensing image dataset, the Faster RCNN algorithm is used in the two-stage method and the RetinaNet algorithm in the one-stage method to carry out experiments. For the small and dense object in the remote sensing dataset, three improvements are proposed, and finally an object detection model is proposed with strong generalization ability. The results show the proposed model proposed can achieve better results in detection accuracy and computational overhead.
Seismic Analysis

An earthquake is caused by sudden motion of the earth's crust. Every year, tens of thousands of earthquakes of all sizes occur all over the world. Some cause tiny or major tremors, others occur in remote areas where no one lives. This chapter allow readers to find out more about the earth structure as well as earthquake nature. Therefore, to detail the definition and construction of a spectrum, a presentation of earthquake analysis is given. In order to become familiar with this analysis, two applications are presented at the end of the chapter with a detailed solution.
Dynamic Optimal Coding and Scheduling for Distributed Learning over Wireless Edge Networks

This paper proposes a novel framework that can effectively address key challenges for the development of distributed learning over wireless edge networks. In particular, we first introduce a highly effective distributed learning model leveraging the most recent advanced coded distributed computing algorithm together with collaborative computing resources from wireless edge nodes to securely and effectively execute learning tasks. To minimize the average delay of learning tasks, the coding and scheduling policies must be jointly optimized. However, determining the optimal coding scheme together with the optimal edge nodes for different learning tasks is NP-hard due to the dynamics and uncertainty of the wireless environment and straggling problems at the computing nodes. Thus, we develop a highly effective approach utilizing advances of both reinforcement learning algorithms and the dueling network architecture to quickly find the optimal coding scheme together with the best edge nodes for different learning tasks without requiring completed information about the surrounding environment and straggling parameters in advance. Through extensive simulation results, we show that our proposed framework can reduce the average delay for the whole system up to 66&#x0025; compared with other conventional learning and optimization approaches.
Semantic segmentation model for detection of people falling into the water based on DeepLabV3+

For the complex water surface environment, the detection of the person falling into the water needs to identify the person within the field of view, and at the same time, it is necessary to judge the environment in which the person is located, so that it can be comprehensively judged whether the person has fallen into the water. Therefore, in view of the blurring of the target boundary and target loss, this paper proposes a Mobile-DeepLabV3+ semantic segmentation model, which uses the MobilenetV3 network to replace the original improved Xception feature extraction network, and performs feature extraction on the input image; use The spatial pyramid structure of DeepLabV3+ performs multi-scale fusion of images to better show the details of image segmentation of people, ships, and water. The experimental results show that the training accuracy of the Mobile-DeepLabV3+ model on the self-made data set is 89.36%, and in the PSNR comparison experiment, the average improvement is about 7dB compared with other models, which has good accuracy.
SAR Image Classification Algorithm Based on Improved SIFT Features

With the advancement and development of Synthetic Aperture Radar (SAR) technology, target classification and interpretation based on SAR image data has become one of a hot research directions for domestic and foreign scholars. Most early SAR image interpretation work relies on overall features or original intensity values, such as template-based classification methods, but such methods are very sensitive to local changes. So, it is necessary to find a method that can effectively extract the local features of the image. In order to improve the recognition performance of vehicle targets in SAR images, this paper summarizes a method suitable for vehicle target recognition in SAR images by testing key points and local descriptors. First, In the key-point detection module, Difference of Gaussian, Harris corner detection, and Features From Accelerated Segment Test (FAST) algorithms are used to generate key-point. In the descriptor extraction module, different modes are used for descriptor gradient statistics to extract Scale Invariant Feature Transform (SIFT) descriptors; secondly, the SIFT feature points are clustered using K-Nearest Neighbor (KNN) to generate a dictionary (Bag of words), then use the Spatial Pyramid matching (SPM) algorithm to generate the feature vector of the picture and use the Support Vector Machines (SVM) classifier for classification; finally, the detection performance of these algorithms is quantitatively analyzed and the optimal performance module is selected to combine modules as the final classification algorithm. It was verified on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set, and the results showed that the performance of the algorithm has been greatly improved than before.
Impaction of Artificial Intelligence on Interaction Design

In recent years, artificial intelligence (AI) technology has made rapid development. Speech recognition, face recognition, natural language processing, limb recognition and other technologies are becoming more and more mature. The development of human technology has constantly changed the interaction mode between people and computers. For example, the maturity of touch screen technology has brought the popularity of tablet computers. The theme of this study is to investigate the impact of artificial intelligence (AI) technology on interaction design and how to use the interaction mode of AI technology to improve user experience. The research method of this paper is through the investigation of literature and face recognition experiments. The research results show that AI technology has a great impact on interaction design. Chat robot and speech recognition have been applied in many interaction designs. The recognition of facial expressions and human body movements can recognize more detailed feature information (such as expression, mental state, skin state, etc.), open more interaction space and provide users with better user experience.
Full Rotation Hyper-ellipsoid Multivariate Adaptive Bandwidth Kernel Density Estimator

Adaptive bandwidth kernel density estimators (AB-KDEs) have received attention from the academic community due to an analytical promise of increased performance over classical estimators. However, the field is fragmented, and there exists no comprehensive comparison of the existing state-of-the-art AB-KDEs. We provide a comparison of some state-of-the-art and classical AB-KDE methods as well as a computational framework. We also present a novel implementation of a full principal axes rotation hyper-ellipsoid variant of the k-Nearest Neighbours algorithm and a Gaussian extension to K-NN. The extensive experimental results show the fixed bandwidth rule-of-thumb methods achieve satisfactory results. Further, the balloon estimators are shown to be superior in the higher dimensional spaces, with higher modes or data on non-linear manifolds. The sample point estimators show utility when data are scarce in low dimensions. The empirical results show that our full rotation hyper-ellipsoid estimator and our Gaussian K-NN are state-of-the-art and will have a significant positive impact on data analysis algorithms. Especially algorithms which depend upon underlying density estimates on “complex” higher-dimensional data.
Enhancing system robustness of active products using trust based collaboration

A robust collaborative system of active products (a product is called active when its ownership does not get transferred from provider to requestor at the time of its usage) should have an in-built mechanism which can make entities (service provider(s) and requestor(s)) to decide with whom to collaborate. In the absence of such a mechanism, the system is bound to have high job failure rate, resulting in wastage of resources. This paper proposes a Trust based Multi-Agent Framework (TbMAF) for collaborative systems of active products which enable only trustworthy entities to collaborate, safeguarding both users’ sensitive applications and providers’ resources. The trustworthiness of service provider(s) and requestor(s) is computed using Fuzzy Inference System (FIS) and Radial Basis Function Neural Network (RBFNN) methodologies, respectively. A prototype based on the proposed system has been tested using real time data of a collaborative system namely, EGEE (Enabling Grids for E-science). This paper finds evidence that the job failure rate is lower when collaborations take place only between trustworthy entities. Further, the proposed framework is found to be robust against malicious entities and can capture the evolving behavior of entities as well.
Review on Generative Adversarial Network in Computer Vision: Methods and Metrics

Generate adversarial networks (GAN) is a popular method, which can be widely used in numerous research areas, such as computer vision, natural language processing, and time series synthesis. However, few references are proposed to give a comprehensive review on GAN based methods. This paper aims to provide a detailed review of GAN based algorithms on computer vision tasks, such as image style transfer, image/video generation, image matting, and image super-resolution. Furthermore, we conclude the evaluation metrics for those tasks to show the effectiveness of GAN based on method. Our review can help beginners recognize the GAN based methods and give a brief introduction on how to apply them to their tasks.
Fusion Deep Learning and Machine Learning for Multi-source Heterogeneous Military Entity Recognition

Currently, there is less research works on military entity recognition across corpus. Three types of military entity recognition corpus, namely abbreviated, scientific or English name, novel and random, are constructed to improve the construction of sub-scenario datasets, according to the heterogeneous characteristics of military entities in different military data sets, such as military documents, reconnaissance intelligence, simulation training mission conception, military books, military blogs, military reviews and military websites. With respect to the fuzzy boundaries of military heterogeneous entities we improve the entity annotation mechanism for entity with fuzzy boundaries based on related research works. We apply a BERT-BiLSTM-CRF model fusing deep learning and machine learning to recognize military entities, and design multiple types of experiments to verify the practical effects of the model. Experimental results show that the recognition effect of the model keeps improving with the increasing size of the corpus in the multi-data source scenario, with the F-score increasing from 73.56% to 84.53%.
Cooperative Pursuit of UAV Cluster Based on Graph Embedding Reinforcement Learning

Aiming at the cooperative behavior decision-making problem of UAV cluster cooperative pursuit of escaping UAVs, a cooperative pursuit target decision-making process model based on Dec-POMDP is established.Aiming at the uncertain and partially observable scenes caused by the limited perception and communication ability of UAV cluster system state, a dynamic graph embedding method is proposed.The cluster state embedded in the graph is represented as the network input under the AC framework. Through information fusion, the individuals in the cluster system can perceive the information of the surrounding UAVs, so as to produce the global situation.Based on the idea of centralized evaluation and distributed execution, a multi-agent strategy gradient method for double delay depth determination based on empirical playback region reconstruction is proposed.This method can be effectively combined with the graph embedding method to represent the state of cluster system. The above method is applied to the target pursuit of UAV cluster, and the learning process has good convergence.
Ensemble Kalman Filter using Gaussian-Sum Predicted State Probability Density Functions

We propose to use Gaussian-sum predicted state probability density functions (PDFs) in the algorithm of the ensemble Kalman lter (EnKF) to enhance its l tering accuracy. We analyze the EnKF in terms of the moment-matched linearization for the nonlinear observation model and show that the ltering accuracy of the EnKF can be improved by using the Gaussian-sum predicted state PDFs. We numerically con rm the effectiveness of the new lters through simulations using benchmark ltering problems of the vector nonlinear growth model and the satellite reentry.
Continuous Adaptation with Online Meta-Learning for Non-Stationary Target Regression Tasks

Most environments change over time. Being able to adapt to such non-stationary environments is vital for real-world applications of many machine learning algorithms. In this work, we propose CORAL, a computationally efficient regression algorithm capable of adapting to a non-stationary target. CORAL is based on Bayesian linear regression with a sliding window and offline/online meta-learning. The sliding window makes our model focus on the recently received data and ignores older observations. The meta-learning approach allows us to learn the prior distribution of the model parameters. It speeds up the model adaptation, complements the sliding window’s drawback, and enhances the performance. We evaluate CORAL on two tasks: a toy problem and a more complex blood glucose level prediction task. Our approach improves the prediction accuracy for the non-stationary target significantly while also performing well for the stationary target. We show that the two components of our method work in a complementary fashion to achieve this.
A Study on Inheritance Network of Chinese “V/A+QiLai” Construction

초록 · 키워드 목차 오류제보하기 Based on Goldberg"s Cognitive Construction Grammar, this paper investigates the V/A+QiLai constructions which is frequently-used expression of Chinese, The study argues that the V/A+QiLai construction is a stratified inheritance network. the Secondly, an inheritance network of the V/A+QiLai constructions is established. As regards to constructional hierarchies, there are altogether seven levels from the top to the bottom. All these seven levels are connected, with the level above dominating the level below and the level below inheriting the level above, thus the inheritance network formed. four types of inheritance relationships can be identified from the system: instance Links, subpart Links, Polysemy Links, Metaphoric extension Links, instance Links. The former two denote inter-level and vertical constructional relationships while the latter two denotes intra-level and horizontal ones. Lastly, examining the two sentences of V/A+QiLai construction, we have concluded that they typically inherit their form and meaning from two homonymous constructions, which function as Patient-Evaluating Construction and Agent-Evaluating Construction, This part has proved that establishing Constructional Homonymy may add to Cognitive Construction Grammar more descriptive accuracy and explanatory power. #V/A+QiLai #Inheritance Link #Constructional Homonymy #Inheritance Network #Construction #Thematic Collocations 1. 引言2. “V/A起來”句式的特徵及其主要子類3. “V/A起來”相關句式的承繼鏈接4. 結語參考文獻ABSTRACT