Reviewerly/exHarmony huggingface dataset:
A Real-Time Image Encryption Method of Networked Inverted Pendulum Visual Servo Control System

In the real-time control of networked inverted pendulum visual servo control system (NIPVSCS), how to achieve safe and efficient image transmission is an important and challenging problem. Generally, the inverted pendulum images are directly transmitted via the network, while these images are not secure because they may be attacked by the hacker. To solve the problem, a fast image encryption method by combining image scaling and improved self-diffusion image encryption (ISDIE) algorithm is firstly proposed, which is employed to encrypt the captured images after they are scaled. The security performance of the proposed algorithm, the characteristics of different delays, and the control performance of the NIPVSCS are then analyzed. Finally, simulation and real-time control experiments confirm the feasibility and effectiveness of the proposed method.
Differentiable Causal Backdoor Discovery

Discovering the causal effect of a decisionis critical to nearly all forms of decisionmaking. In particular, it is a key quantityin drug development, in crafting governmentpolicy, and when implementing a real-worldmachine learning system. Given only observational data, confounders often obscure thetrue causal effect. Luckily, in some cases, it ispossible to recover the causal effect by usingcertain observed variables to adjust for theeffects of confounders. However, without access to the true causal model, finding this adjustment requires brute-force search. In thiswork, we present an algorithm that exploitsauxiliary variables, similar to instruments, inorder to find an appropriate adjustment bya gradient-based optimization method. Wedemonstrate that it outperforms practical alternatives in estimating the true causal effect,without knowledge of the full causal graph.
HOW DOES BLOCKCHAIN PARTICIPATION AFFECT THE CRYPTOCURRENCY MARKET?

Taking the unique advantage of the cryptocurrency market setting, this paper examines the relationships between blockchain participation and returns, trading volume and realized volatility of main cryptocurrencies (i.e., Bitcoin, Ethereum and Litecoin). Dissimilar to previous theoretical studies that model the influencing factors on participation, we employ the number of unique from addresses 1 as the proxy for cryptocurrency investors’ blockchain participation and further explore the impact of such participation. By using vector autoregressive (VAR) model, we find that the blockchain participation has a significant and positive impact on the next day’s trading volume and realized volatility for the main cryptocurrencies. Our results are robust to the Granger causality test and alternative measure for blockchain participation.
Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller

We present a hierarchical fuzzy logic system for precision coordination of multiple mobile agents for simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning LiDAR to make movement decisions based on local distance and bearing information. Two solution approaches are considered and compared. Both of them are structured around a hierarchical arrangement of control modules to enable synchronisation of the agents' arrival times while avoiding collision with obstacles. The proposed control module controls both moving speeds and directions of the robots to achieve the simultaneous target reaching task. The control system consists of two levels: the lower-level individual navigation control for obstacle avoidance and the higher-level coordination control to ensure the same time of arrival for all robots at their target. The first approach is based on cascading fuzzy logic controllers, and the second approach considers the use of a Long Short-Term Memory recurrent neural network module alongside fuzzy logic controllers. Parameters of all the controllers are optimised using the particle swarm optimization algorithm. To increase the scalability of the proposed control modules, an interpolation method is introduced to determine the velocity scaling factors and the searching directions of the robots. A physics-based simulator, Webots, is used as a training and testing environment for the two learning models to facilitate the deployment of codes to hardware which will follow in the next phase of our research.
Learning to Caricature via Semantic Shape Transform

Caricature is an artistic drawing created to abstract or exaggerate facial features of a person. Rendering visually pleasing caricatures is a difficult task that requires professional skills, and thus it is of great interest to design a method to automatically generate such drawings. To deal with large shape changes, we propose an algorithm based on a semantic shape transform to produce diverse and plausible shape exaggerations. Specifically, we predict pixel-wise semantic correspondences and perform image warping on the input photo to achieve dense shape transformation. We show that the proposed framework is able to render visually pleasing shape exaggerations while maintaining their facial structures. In addition, our model allows users to manipulate the shape via the semantic map. We demonstrate the effectiveness of our approach on a large photograph-caricature benchmark dataset with comparisons to the state-of-the-art methods.
Steganographic Method Based on Interpolation and Cyclic LSB Substitution of Digital Images

In the era of the Internet, many files (text, image, audio, and video) are shared through the Internet. Security is the main issue, during sharing information. To resolve the security problem, two techniques are used to make our information secure, i.e., cryptography and steganography. Cryptography is used for encryption but does not hide the information. So, it is not too much secure. The emerging Internet technology has led to the need for a high level of security which is provided by steganography. Steganography is used to hide the existence of a secret message. As a result, unauthorized users cannot access secret information in different multimedia files. This paper gives a brief overview of steganography, type of steganography, insertion technique used in image steganography search such as LSB, GLM, parity checking, and interpolation. It also includes the application and a set of parameters to test the imperceptibility of an algorithm. In image steganography, the cover medium used for hiding data is an image. The most common technique for image steganography involves the least significant bits (LSBs) of image pixels which are altered with the bits of secret message. There are a lot of limitations in the LSB techniques, due to which many new image steganography techniques are developed. The cyclic LSB technique is such a technique. In this paper, an altered version of the cyclic LSB technique is presented. The advantage of this method is that RGB channels of the image are rotated into the cycle, i.e., red channel is used for hiding first bit, the green channel is used for hiding the second bit, the blue channel is used for hiding third bits, and then this cycle is repeated. In this method, no separate key is used for encryption and decryption, thus avoiding any overhead of managing key. This technique provides enhanced capacity and security over any other image steganography techniques.
An Empirical Analysis of Classifiers Using Ensemble Techniques

Ensemble methods are algorithms that combine various models together to give higher accuracy than individual models. The ensemble methods used here are majority voting, XGBoost, and random forest. Several decision trees are combined using voting classifier, Random forest tree, and XGBoost. These are considered as the best universal models which are used here to compare the accuracies with other models. The datasets are being split randomly 9, 18, and 27 times, respectively. The decision tree model is applied and later combined with voting classifier. The descriptions of the methods are followed by an extensive empirical study over 10 publicly available datasets. An ensemble model with five classifiers is also implemented that give us the accuracy of the model, and later all the accuracies are compared. Finally, a comparison has been done with XGBoost and Random forest classifiers, which shows the effectiveness of the used ensemble methods for classification.
Penggunaan Teknologi Informasi dalam Pembelajaran Online Masa Covid-19

The Covid-19 pandemic affects almost all aspects of life, including education. The impact of the Covid-19 pandemic on the education aspect requires that teaching and learning activities continue even though students are at home. So that educators are required to design learning media as an innovation by utilizing online media. The purpose of writing this article is to study and understand the use of information technology in online learning during the Covid-19 period. The writing of this article uses a qualitative approach with secondary data sources obtained from online observations and literature studies. The results of the study prove that the use of online media during the Covid-19 caused a variety of responses and the impact and change in the learning system that can affect the teaching and learning process as well as the level of student development in responding to the material presented.
Anisotropic Diffusion Combined with Nonconvex Functional for Noise Image Segmentation

Aiming at the problems of low segmentation accuracy of noise image, poor noise immunity of the existing models and poor adaptability to complex noise environment, a noise image segmentation algorithm using anisotropic diffusion and nonconvex functional was proposed. First, focusing on the “staircase effect”, a nonconvex functional was introduced into the energy functional model for smooth denoising. Second, the validity and accuracy of the model were established by proving that there was no global minimum in the solution space of the nonconvex energy functional model; the improved model was then used to obtain a smooth and clear image edge while maintaining the edge integrity. Third, the smooth image obtained from the nonconvex energy functional model was combined with the level set model to obtain the anisotropic diffusion gray level set model. The optimal outline of the target was obtained by calculating the minimum value of the energy functional. Finally, an anisotropic diffusion equation with nonconvex energy functional model was built in this algorithm to segment noise image accurately and quickly. A series of comparative experiments on the proposed algorithm and similar algorithms were conducted. The results showed that the proposed algorithm had strong noise resistance and provided precise segmentation for noise image.
Modelling ethnic differences in the distribution of insulin resistance via Bayesian nonparametric processes: an application to the SABRE cohort study

We analyse data from the Southall And Brent REvisited (SABRE) tri-ethnic study, where measurements of metabolic and anthropometric variables have been recorded. In particular, we focus on modelling the distribution of insulin resistance which is strongly associated with the development of type 2 diabetes. We propose the use of a Bayesian nonparametric prior to model the distribution of Homeostasis Model Assessment insulin resistance, as it allows for data-driven clustering of the observations. Anthropometric variables and metabolites concentrations are included as covariates in a regression framework. This strategy highlights the presence of sub-populations in the data, characterised by different levels of risk of developing type 2 diabetes across ethnicities. Posterior inference is performed through Markov Chains Monte Carlo (MCMC) methods.
Detection of Malicious URLs on Twitter

Twitter is an online social network that is popular for its use of 140-character messages called tweets to exchange information, news and connects the global world. Due to the large audience of people that make use of twitter, malicious users from time to time try to find ways to attack it. This is because the usages of URLs in tweets expose them and make them prone to attacks such as malware distribution, phishing, spam and scam. In this project, a system is developed that detected suspicious URLs on twitter, and the proposed system investigates the correlation of URL redirect chains extracted from various tweets on twitter. Therefore, after a large number of tweets are collected from twitter public timeline, a classifier by naive Bayes machine learning algorithm is built using the data.
Using Multi-UAV for Rescue Environment Mapping: Task Planning Optimization Approach

Rescuing survivors in unknown environment can be extreme difficulty. The use of UAVs to map the environment and also to obtain remote information can benefit the rescue tasks. This paper proposes an organizational system for multi-UAVs to map indoor environments that have been affected by a natural disaster. The robot’s organization is focused on avoiding possible collisions between swarm’s members, and also to prevent searching in locations that have already discovered. This organizational approach is inspired by bees behavior. Thus, the multi- UAVs must search, in a collaborative way, in order to map the scenario in the shortest possible time and, consequently, to travel the shortest reasonable distance. Therefore, three strategies were evaluated in a simulation scenario created in the V-REP software. The results indicate the feasibility of the proposed approach and compare the three plans based on the number of locations discovered and the path taken by each UAV.
A Real-Time Vehicle Logo Detection Method Based on Improved YOLOv2

Confirming the vehicle information in the surveillance video is an important issue in the intelligent transportation system at present. As a key and fixed feature, the vehicle logo can play a role in assisting the discrimination. According to the characteristics of the vehicle logo image, we propose a high-efficiency logo detection method based on the improved YOLOv2, which adopts the strategies of separable convolution, multi-scale channel fusion and automated multi-scale test, compared with the traditional vehicle identification method based on manual extraction features, so the method has the advantages of self-learning features, direct input of images, and the like, and can realize the dual functions of positioning and recognition of the vehicle logo. Experiments show that the model has excellent stability under low-resolution (very small target), illumination effects, angular rotation and noise pollution, and has high accuracy, recall and real-time. On the data set used, our model was trained on the NVIDIA GTX1070 GPU (8G memory) server, which achieved a recall rate of Recall = 0.997, an average accuracy of mAP = 0.990, and a test speed of approximately 21.3 FPS, far exceeding the performance of any other current vehicle logo detection method.
Passive Localization Method Based on Cubature Kalman Filter

In order to improve the performance of passive positioning and make up for the shortcomings of traditional technology, such as less information, slow positioning speed and low positioning accuracy, this paper proposes a pure angle target positioning method based on the cubature Kalman filter(CFK). Firstly, the posterior probability density function is calculated by the determined volume points, without the knowledge of the Jacobian matrix which is complex and hard to be solved. In addition, this method can effectively improve the accuracy of positioning. Simulation results show that, compared with the traditional method, the positioning accuracy of this method is higher.
Multi-Attention Based Ultra Lightweight Image Super-Resolution

Lightweight image super-resolution (SR) networks have the utmost significance for real-world applications. There are several deep learning based SR methods with remarkable performance, but their memory and computational cost are hindrances in practical usage. To tackle this problem, we propose a Multi-Attentive Feature Fusion Super-Resolution Network (MAFFSRN). MAFFSRN consists of proposed feature fusion groups (FFGs) that serve as a feature extraction block. Each FFG contains a stack of proposed multi-attention blocks (MAB) that are combined in a novel feature fusion structure. Further, the MAB with a cost-efficient attention mechanism (CEA) helps us to refine and extract the features using multiple attention mechanisms. The comprehensive experiments show the superiority of our model over the existing state-of-the-art. We participated in AIM 2020 efficient SR challenge with our MAFFSRN model and won 1st, 3rd, and 4th places in memory usage, floating-point operations (FLOPs) and number of parameters, respectively.
Credit Card Fraud Detection Using Machine Learning Techniques

Credit Card Fraud is one of the major moral issues in the public and private bans sector. The effect of this problems leads to the several ethical trouble. The important themes are to notice the distinctive kinds of credit card fraud and to locate different methods that have been used in fraud detection. The sub-point is to suppose about existing and ruin down as of late dispensed discoveries in fraud detection. Probable upon the variety of extortion appeared with the banks or different financial organizations, exceptional measures can be embraced and executed. The work carried out in this paper are usually going to have really beneficial residences as a approaches as expenditure reserve fund and time capability. The cost utilization of the strategies investigated proper right here is in the minimization of credit card fraud. Anyway, there are up to now moral troubles when appropriate credit card customers are unsorted as fraudulent. Credit Card Fraud Detection is an method which will help people for their transaction process in shopping mall and any other transaction process nowadays fraud detection is nothing but an process where the criminals are found and there are many illegal activities are taking place which causes difficulty for people. Here in this paper we are using SMOTE technique to find fraud and this technique will help to sort both the normal transaction and fraud transaction this process can make easy to find fraudulent. And Neural Network KNN are also taken place to find Credit Card Fraud.
AdaBoosted Deep Ensembles: Getting Maximum Performance Out of Small Training Datasets

Even though state-of-the-art convolutional neural networks (CNNs) have shown outstanding performance in a wide range of imaging applications, they typically require large amounts of high-quality training data to prevent over fitting. In the case of medical image segmentation, it is often difficult to gain access to large data sets, particularly those involving rare diseases, such as skull-based chordoma tumors. This challenge is exacerbated by the difficulty in performing manual delineations, which are time-consuming and can have inconsistent quality. In this work, we propose a deep ensemble method that learns multiple models, trained using a leave-one-out strategy, and then aggregates the outputs for test data through a boosting strategy. The proposed method was evaluated for chordoma tumor segmentation in head magnetic resonance images using three well-known CNN architectures; VNET, UNET, and Feature pyramid network (FPN). Significantly improved Dice scores (up to 27%) were obtained using the proposed ensemble method when compared to a single model trained with all available training subjects. The proposed ensemble method can be applied to any neural network based segmentation method to potentially improve generalizability when learning from a small sized dataset.
Comparison of the trend moment and double moving average methods for forecasting the number of dengue hemorrhagic fever patients

Spread of Dengue Hemorrhagic Fever (DHF) is influenced by an increase in air temperature due to changes in weather and population density so that there is a lot of exchange of dengue virus through the bite of the Aedes aegypti mosquito. Forecasting models are needed to predict the number of DHF patients in the future so that monitoring of the number of DHF patients can be carried out as anticipation and consideration of decision making. Forecasting the number of patients is based on actual data within 2 (two) previous years by comparing the two methods, namely trend moment and double moving average. To measure the accuracy of the forecasting results from the two forecasting methods, tracking signal and moving range are used. Based on the test results, it shows that the forecasting results are said to be good because no one has passed the upper control limit and lower control limit values so that the difference between the actual data and the forecasting results is not too significant and the trend moment more recommended because the difference in actual data and forecasting results are approached and shown in the pattern graph by looking at the data difference in each period.
SoK: Transparent Dishonesty: front-running attacks on Blockchain

We consider front-running to be a course of action where an entity benefits from prior access to privileged market information about upcoming transactions and trades. Front-running has been an issue in financial instrument markets since the 1970s. With the advent of the blockchain technology, front-running has resurfaced in new forms we explore here, instigated by blockchains decentralized and transparent nature. In this paper, we draw from a scattered body of knowledge and instances of front-running across the top 25 most active decentral applications (DApps) deployed on Ethereum blockchain. Additionally, we carry out a detailed analysis of Status.im initial coin offering (ICO) and show evidence of abnormal miners behavior indicative of front-running token purchases. Finally, we map the proposed solutions to front-running into useful categories.
Research on Feature Extraction of Tai Le Recognition

Feature extraction is one of the key technologies in pattern recognition, which has great influence on the accuracy of text recognition. In this paper, The Tai Le are extracted with coarse grid features, directional line element feature and penetration number feature, evaluated using BP neural network classifier. The results show that the direction line element feature on the Tai Le print recognition data set YDH2019.1 has higher recognition accuracy than the other two features, is more suitable for the Tai Le recognition.
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph

Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However, in practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. Our dataset involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions. We address these challenges by proposing a multi-graph structure that is able to represent the original graph information more comprehensively. Furthermore, we also incorporate aggregation methods that learn to extract the rich graph information. Extensive experiments demonstrate the effectiveness of our model architecture.