High Accuracy Network Cardinalities Estimation by Step Sampling Revision on GPU

Host cardinality estimation is an important research field in network management and network security. The host cardinality estimation algorithm based on the linear estimator array is a common method. Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host. This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy. The cardinality estimating algorithm is a kind of random algorithm, and there is a deviation between the estimated results and the actual cardinalities. The deviation is affected by some systematical factors, such as the random parameters inherent in linear estimator and the random functions used to map a host to different linear estimators. These random factors cannot be reduced by merging multiple estimators, and existing algorithms cannot remove the deviation caused by such factors. In this paper, we regard the estimation deviation as a random variable and proposed a sampling method, recorded as the linear estimator array step sampling algorithm (L2S), to reduce the influence of the random deviation. L2S improves the accuracy of the estimated cardinalities by evaluating and remove the expected value of random deviation. The cardinality estimation algorithm based on the estimator array is a computationally intensive algorithm, which takes a lot of time when processing high-speed network data in a serial environment. To solve this problem, a method is proposed to port the cardinality estimating algorithm based on the estimator array to the Graphics Processing Unit (GPU). Experiments on real-world highspeed network traffic show that L2S can reduce the absolute bias by more than 22% on average, and the extra time is less than 61 milliseconds on average.     

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.     

Identifying and Verifying Vulnerabilities through PLC Network Protocol and Memory Structure Analysis

Cyberattacks on the Industrial Control System (ICS) have recently been increasing, made more intelligent by advancing technologies. As such, cybersecurity for such systems is attracting attention. As a core element of control devices, the Programmable Logic Controller (PLC) in an ICS carries out on-site control over the ICS. A cyberattack on the PLC will cause damages on the overall ICS, with Stuxnet and Duqu as the most representative cases. Thus, cybersecurity for PLCs is considered essential, and many researchers carry out a variety of analyses on the vulnerabilities of PLCs as part of preemptive efforts against attacks. In this study, a vulnerability analysis was conducted on the XGB PLC. Security vulnerabilities were identified by analyzing the network protocols and memory structure of PLCs and were utilized to launch replay attack, memory modulation attack, and FTP/Web service account theft for the verification of the results. Based on the results, the attacks were proven to be able to cause the PLC to malfunction and disable it, and the identified vulnerabilities were defined.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

A Novel Method of Heart Failure Prediction Based on DPCNNXGBOOST Model

The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients, then the XGBOOST algorithm was used to construct the prediction model of heart failure. An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018. The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3% and 31% compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.     

Complementary Kalman Filter as a Baseline Vector Estimator for GPS-Based Attitude Determination

The Global Positioning System (GPS) offers the interferometer for attitude determination by processing the carrier phase observables. By using carrier phase observables, the relative positioning is obtained in centimeter level. GPS interferometry has been firstly used in precise static relative positioning, and thereafter in kinematic positioning. The carrier phase differential GPS based on interferometer principles can solve for the antenna baseline vector, defined as the vector between the antenna designated master and one of the slave antennas, connected to a rigid body. Determining the unknown baseline vectors between the antennas sits at the heart of GPS-based attitude determination. The conventional solution of the baseline vectors based on least-squares approach is inherently noisy, which results in the noisy attitude solutions. In this article, the complementary Kalman filter (CKF) is employed for solving the baseline vector in the attitude determination mechanism to improve the performance, where the receiversatellite double differenced observable was utilized as the measurement. By using the carrier phase observables, the relative positioning is obtained in centimeter level. Employing the CKF provides several advantages, such as accuracy improvement, reliability enhancement, and real-time assurance. Simulation results based on the conventional method where the least-squares approach is involved, and the proposed method where the CKF is involved are compared and discussed.     

Empirical Analysis of Agricultural Cultural Resources Value Evaluation under DEA Model

Agricultural culture is a productive activity about education and management. It aims at high efficiency and high quality, uses technology as its means, and takes nature as its carrier. Agricultural cultural resources are the product of the rapid development of modern economy. It promotes the development of the national economy and profoundly affects people's production and life. DEA model, also known as data envelope analysis method, is an algorithm that uses multiple data decision units for input and output training to obtain the final model. This article explains the concept and basic characteristics of agricultural culture. Through questionnaire surveys and expert interviews, we collected development data, screened human, material, and financial data, and calculated information on economic and social resources. On this basis, this paper establishes the evaluation index of agricultural culture based on DEA model. Then, through empirical analysis from a specific perspective, it can be concluded that increasing human, material and financial input can achieve economic and social benefits. Generally speaking, cultural investment can promote the development of the industry. The research results of this paper laid a theoretical foundation for the development of agricultural culture, and put forward a development model focusing on technology development, improving investment efficiency, and investing in material resources.     

Multi-Purpose Forensics of Image Manipulations Using Residual- Based Feature

The multi-purpose forensics is an important tool for forge image detection. In this paper, we propose a universal feature set for the multi-purpose forensics which is capable of simultaneously identifying several typical image manipulations, including spatial low-pass Gaussian blurring, median filtering, re-sampling, and JPEG compression. To eliminate the influences caused by diverse image contents on the effectiveness and robustness of the feature, a residual group which contains several highpass filtered residuals is introduced. The partial correlation coefficient is exploited from the residual group to purely measure neighborhood correlations in a linear way. Besides that, we also combine autoregressive coefficient and transition probability to form the proposed composite feature which is used to measure how manipulations change the neighborhood relationships in both linear and non-linear way. After a series of dimension reductions, the proposed feature set can accelerate the training and testing for the multipurpose forensics. The proposed feature set is then fed into a multi-classifier to train a multi-purpose detector. Experimental results show that the proposed detector can identify several typical image manipulations, and is superior to the complicated deep CNN-based methods in terms of detection accuracy and time efficiency for JPEG compressed image with low resolution.     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

MOOC Learner’s Final Grade Prediction Based on an Improved Random Forests Method

Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC data. In order to solve these two problems, an improved random forests method is proposed in this paper. First, a hybrid indicator is defined to measure the importance of the features, and a rule is further established for the feature selection; then, a Clustering-Synthetic Minority Over-sampling Technique (SMOTE) is embedded into the traditional random forests algorithm to solve the class imbalance problem. In experiment part, we verify the performance of the proposed method by using the Canvas Network Person-Course (CNPC) dataset. Furthermore, four well-known prediction methods have been applied for comparison, where the superiority of our method has been proved.