bluuebunny/crossref_metadata_2025 huggingface dataset:
Codes in Rosenbloom-Tsfasman metric: A Survey
Authors: Gopinadh Bodigiri Sai , Marka Venkatrajam
Published: April 2021

This paper gives a systematic survey of research carried out in the theory of codes equipped with Rosenbloom-Tsfasman metric. In classical coding theory setting, codes are investigated with respect to the Hamming metric which can efficiently address the communication problems arising from channels in which channel noise generates equiprobable errors. But however, not all the real world channels are of that nature, especially, when the possible errors form patterns of a specific shape. Rosenbloom and Tsfasman introduced a non-Hamming metric, called Rosenbloom-Tsfasman metric (RT-metric, in short) that can address the problem of reliable information transmission over parallel noisy channels. Martin, Stinson and Skriganov independently introduced the same metric in the context of the theory of uniform distributions. As this metric happened to be a generalization of the classical Hamming metric, it has attracted so much attention from the coding theory research community and as a result a lot of work has been done in this line of research over the past 3 decades. In this paper we would like to present the key developments in the field of codes with RT-metric.
Investigation on half-metallic ferromagnetism in Phosphorous doped SmN-Band structure study
Authors: Muthumari M , Manjula M , Sundareswari M , Viswanathan E
Published: April 2021

Rare earth nitrides with electronic and magnetic properties would be advantages for the development of spintronic devices. Structural, electronic, magnetic and elastic properties of rare earth nitrides namely SmN and SmN0.5P0.5 compounds are studied by using the spin-polarized full potential linearized augmented plane wave (FP-LAPW) method based on density functional theory (DFT). A supercell scheme is used to carry out the ground state calculations of SmN0.5P0.5. The structural parameters, band structure, density of states and charge densities have been analysed by generalized gradient approximation (GGA) for ferromagnetic phase and the results are verified by using charge density plots.
Ab-initio investigations of electronic, optical, mechanical and thermal properties of Ca0.875Ba0.125Te
Authors: Manjula M , Malliga P , Viswanathan E , Muthumari M
Published: April 2021

The ground-state properties and optical, mechanical, elastic and thermal properties of the Ca0.875Ba0.125Te alloy have been studied by using the full potential linearized augmented plane wave (FP-LAPW) scheme based on the density functional theory in the frame of generalized gradient approximation (GGA). In order to model Ca1-xBaxTe alloy, 16-atoms supercell of the type 2 × 2 × 2 is employed. The lattice structure of Ca0.875Ba0.125Te alloy is obtained by replacing one Ca atom by one Ba atom in the crystal lattice of CaTe. The charge density plot, electronic structure and density of states plots are made and discussed for the alloy. The lower valence band maxima (VBM) and the upper conduction band minima (CBM) of Ca0.875Ba0.125Te alloy is locaed at Γ point, ensuing in a direct band gap, whereas in case of parent element CaTe the nature of the band gap is indirect. The characteristic properties of Ca0.875Ba0.125Te alloy is dominated by Te 5p electrons (below the Fermi level) and Ba 4d and Ca 3d electrons (above the Fermi level).
Synthesis, growth and characterization of NLO single crystals for Remote Sensing applications
Authors: Malliga P , Manjula M , Arul Pragasam A Joseph , Dharshan V , Deepika V
Published: April 2021

A novel semiorganic optical material, L-alanine doped urea thiourea magnesium chloride (ATUMC) single crystals has been synthesised by evaporating the mixture of solvents slowly at a temperature of 34 °C. XRD data of crystals AUTMC crystals show a slight change in the values of lattice parameters and volume in comparison with the pure urea thiourea magnesium chloride (UTMC) crystal indicating the inclusion of dopant alanine. The vaious interaction among elements present in the grown sample was elucidated using FT-IR studies. n UV studies has confirmed an absorption between 292 –1000 nm region. By adding alanine as dopant the absorption region lies in the same region. NLO property is also analysed for the grown crystal using Kurtz Perry method. The TG/DTA analyses show that the thermal stability is considerably increased with the addition of alanine dopant in the pure TUMC crystal.
Polarized modulation and receivers for wireless communication
Authors: Raja Kumar R. , Pandian R. , Puja N.K. , Indumathi P.
Published: April 2021

Achieving increase in spectral efficiency has always been a major aspect in communication systems. Our work emphasises the application of dual polarized modulation in a wireless environment. Our main goal is to increase the throughput of the system. We aim to increase the spectral efficiency and throughput without an increment of radiated energy without any Channel State Information at Transmitter (CSIT) and feedback at the transmitter The proposed dual Polarized Modulation (DPMOD) scheme exploits the polarization diversity reducing the required SNR (Eb/N0) and adding an extra bit and achieves the same Bit Error Rate (BER) carrying extra information.
An EOQ model for deteriorating products with backorder and fixed transportation cost
Authors: Girija Bai H. , Mohamed Ismail A. , Kaviya D. , Muniappan P.
Published: April 2021

This paper deals buyer-vendor EOQ Inventory model for deteriorating product with fixed transportation cost. Optimum technique is used to find optimum order quantity and also developed for equal benefits of both buyer-vendor with co-ordination system. In order to reduce the total inventory cost, optimum order quantity and backorder levels are determined. We develop a production inventory model with screening and disposed cost for deteriorating products. shortages are not allowed in the inventory system. The buyer takes decision(disposed or screened)for damaged product. The proposed model is illustrated through numerical models.
Design and Analysis Of an Optimization Model for Allocating the upcoming trains to the Platforms
Authors: Maheswari S , Keerthi S
Published: April 2021

In railways, day by day assigning trains to the platforms is becoming a task by using the available resources. In this paper, an optimization model is proposed to minimize the waiting time of the upcoming trains to get into the platform on time. The waiting time of a train is minimized by reducing the overtime of the platform and service completion time of the existing train in the platform. The objective of the problem is minimize the overtime of the platform and service completion time for each trains. The solution of the model is to obtain the optimum scheduling decision of trains which leads to the result of Mixed Integer Programming problem.
An Optimum EOQ model for buyer-vendor with price breaks and fixed holding cost
Authors: Muniappan P. , Kirubhashankar C.K. , Mohamed Ismail A.
Published: April 2021

In this paper, an EOQ model is developed for deteriorating product where constant demand depends on the buyer-vendor inventory level with price breaks. In traditional inventory problems, it is considered that the holding cost is fixed and setup cost is not subject to control. An inventory problem is formulated for deteriorating items, further, the optimal solutions are derived. Numerical examples presented to demonstrate the developed model. An algorithm is presented to determine the optimal inventory level while minimizing the present value of the total cost.
Design of single-phase online uninterruptible power supply based on STM32
Authors: Xiong Yi
Published: April 2021

According to the principle of UPS, an AC sine wave online uninterruptible power supply based on STM32 is designed. The system adopts mains power, outputs corresponding DC power through isolation transformer, autotransformer and rectifier module, and then connects to single-phase full-bridge inverter circuit after boosting through booster module to supply stable AC voltage to the load, which significantly improves the load regulation rate, The voltage sampling module collects the output voltage of the boost circuit in real time and feeds it back to the single-chip microcomputer, and uses PID algorithm to realize the voltage closed-loop control, which improves the reliability of the uninterruptible power supply system. The system has functions such as overcurrent protection, instant power switching, and battery undervoltage alarm.
Progress in the Application of Geostatistics Downscaling Methods in the Field of Public Health
Authors: Liu Xin , Lv Xiao , Zhang Zhijie
Published: April 2021

With the continuous development of the two emerging fields of medical geography and spatial epidemiology, Geostatistical downscaling plays an important role in high-precision disease mapping and incidence prediction in a range of public health application This article combines the application cases of different diseases and lists a series of geostatistical downscaling methods, including Area-to-point and Area-to-Area Kriging, Area-to-point and Area-to-area Poisson Kriging, Area-to-point and Area-to- Area regression Kriging, Area-to-point and Area-to-Area co-kriging, Area-to-point and Area-to-Area binomial kriging, etc. By introducing the application progress of different methods in the field of public health, the advantages and disadvantages are compared. Finally, trends and some basic questions of methods proposed recently are discussed in the conclusion.
CNN Fusion Based Brain Tumor Detection from MRI images using Active Contour Segmentation Techniques
Authors: Rajesh Babu K , Sairam Nadipalli L. S. P. , Sai Tejaswini C. , Bharath Kumar G. , Vasantha P.
Published: April 2021

Early diagnosis of a brain tumor may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. This manuscript presented the performance analysis of clustered based and fusion-based segmentation techniques intended to detect the tumor from human brain MRI images in an efficient manner. Four primary steps are involved in this work such as pre-processing, clustering, segmentation, and fusion techniques. The main clustering methods such as K-means and fuzzy c-means (FCM) were first applied to the pre-processed MRI images, and then, the clustered images were segmented directly using the active contour segmentation techniques such as chan-vese (C-V) and level set method (LSM). Then in the next step, the clustered images were fused by using the non-sub sampled contour transform (NSCT) and convolution neural network (CNN) fusion methods, and then, the fused images were segmented by using the C-V and LSM segmentation methods again. The results of both clustered based and fusion-based segmentation in terms of structural similarity index measure (SSIM), dice coefficient (DC), computational time, sensitivity, precision, and segmentation accuracy revealed that CNN fusion-based C-V segmentation performs better than without fusion (clustered based or direct segmentation) to detect the tumors from the MRI images. The results indicate that C-V performs better with CNN as compared with the LSM. Finally, the fusion-based segmentation is an efficient approach to detect the tumor from the MRI images with minimal information loss and high segmentation accuracy over the clustered based segmentation.
Review on Vibration Signal Analysis of Rotating Machinery Based on Deep Learning
Authors: Sun Yingqian , Feng Tang , Jin Zhenzhen
Published: April 2021

Rotating machinery is widely used in industrial systems, and its operation state is directly related to the working performance of the system. Research on equipment condition monitoring and fault diagnosis based on vibration signal analysis is of great significance to ensure the safe and stable operation of equipment. In recent years, the field of deep learning has developed rapidly, and many researchers have applied it to the vibration signal analysis of rotating machinery equipment. Firstly, the development history of deep learning is reviewed. Then, the principles of deep learning models such as convolutional neural network, deep belief network, stacked auto-encoder and their applications in vibration signal analysis of rotating machinery are introduced. Finally, the future development trend of deep learning is discussed.
The impact of implementing auditory intellectually repetition (air) learning model based on learning community for students’ creative thinking skills
Authors: Hobri  , Sahnawi  , Susanto  , Ridlo Z R
Published: April 2021

The aim of the research is to know the impact of implementing Auditory Intellectually Repetition (AIR) learning model based on learning community for student’s creative thinking skill. The research subjects were Indonesian second-grade students of junior secondary school in class VIIIB as the experimental class and class VIIIA students as the control class. Quasi Experimental is used as quantitative research designs. Data collection methods are observation, pre-test, and post-test. Instrument testing and analysis were carried out before conducting tests to measure discriminatory power, difficulty level of test items, reliability, and validity. Analysis of the data in the affective and psychomotor domains used descriptive techniques, cognitive domains using prerequisite testing, similarity of initial ability averages, and hypothesis testing. The results show that the learning instrument has reached values above 4.0 so that they are declared valid and feasible to be used for the learning process with the aim of exploring students’ creative thinking skills. The application of the AIR learning model based on learning community give a positive effect on the students’ creative thinking skill. The average achievement aspect of student’s creative thinking skills in the experimental class is higher than the control class.
Performance optimization method for distributed storage system based on hybrid storage device
Authors: Wu Chen-han
Published: April 2021

In the era of big data and artificial intelligence, the storage data of data centers is growing rapidly. Big data storage systems with high-performance computing require high-speed data access and greater storage capacity. However, traditional storage technology based on hard disk media (HDDs) has been difficult to meet the current demand for high-performance storage, solid state drives (SSDs)’ cost will be a lot for storing massive amounts of data. Therefore, in response to the above problems, this paper proposes a hierarchical hybrid storage technology for distributed file system, including an adaptive file migration strategy and client caching mechanism. Through experimental verification, the proposed technology fully combines the high I/O throughput characteristics of SSDs and the large capacity characteristics of disks, thereby providing a cost-effective high-performance distributed storage solution, and when processing real-time data, it has higher access performance.
Optimization based on LLVM global instruction selection
Authors: Zhufeng Huang , Jiandong Shang
Published: April 2021

Instruction selection is a key component of code generation. High-quality instruction selection has a great impact on the size and quality of the generated code. The existing instruction selection technology is mostly limited to a single statement or a single basic block, and the global instruction selection based on LLVM degrades the entire function in the form of SSA. Global instruction selection optimization based on LLVM is implemented on Shenwei platform, including global instruction merge optimization based on cost model, register bank selection optimization and instruction locality optimization. Through the test of SPEC CPU2006, Experimental results show that the average speed-up ratio before and after the optimization of global instruction selection based on LLVM is 1.08, and the maximum speed-up ratio is 1.36. In addition, when the quality of the generated code is equivalent, the global instruction selection is compared with the default instruction selection, the LLC compilation speed is increased by an average of 20%, and the entire compilation cycle is increased by an average of 6%-8%.
Research on timbre classification based on BP neural network and MFCC
Authors: Meng Zhihang
Published: April 2021

Music exists in all aspects of life. With the development of deep learning and neural network technology in recent years, music information retrieval has become an emerging field that has received widespread attention. In the past, musicians often relied on manual labeling to classify music. This method is not only time-consuming and labor-intensive, but also inaccurate. Although some researchers have tried to extract music features for automatic classification, it is difficult to extract appropriate audio features due to the interaction between the fundamental wave and harmonics of the music itself. In this paper, the Mel cepstrum coefficient is selected to extract the timbre characteristics, and the improved BP neural network is used to process the characteristics, which overcomes the previous problems and obtains a higher accuracy rate.
Research on IoT Security Defense Based on Honeypot Technology
Authors: Wang Zhenxia , Yu Linlin
Published: April 2021

With the expansion of the Internet and the increase in the number of end users, mobile terminals are vulnerable to hackers, computer viruses and other dangerous attacks. Honeypot technology is a highly deceptive security protection technology, which can run in a variety of networks and terminal systems with characteristic vulnerabilities deliberately. Meanwhile, honeypot technology can induce intruders to launch attacks. Therefore, the source of the attack can be captured to realize security defenses, protecting important system terminals from infringement. Moreover, by inducing the intruder to attack the honeypot terminal, it can not only delay the time to attack the real target, but also timely monitor, collect and analyze the attack behavior to provide support for dealing with unknown threats. So, in order to solve the problems of traditional methods and apply honeypot technology to the process of unknown threat defense of mobile terminal, an intelligent defense method is proposed in the paper for mobile terminal unknown threats based on honeypot technology. The results of comparative experiments prove that the method proposed in the paper has less underreporting of unknown threats, and can fully detect the source information, which can meet the needs of mobile terminals for effective defense against unknown threats.
DRTaint: A Dynamic Taint Analysis Framework Supporting Correlation Analysis Between Data Regions
Authors: Yang Pan , Kang Fei , Zhao Yuntian , Shu Hui
Published: April 2021

Taint analysis is a common technology for software analysis, which is widely used in the region of information security. Aiming at the problem that the existing binary program dynamic taint analysis framework does not support “replay” analysis and the analysis efficiency is low, a taint analysis framework called DRTaint is proposed. DRTaint is a universal taint analysis framework that supports correlation analysis between data regions. Experiments show that DRTaint shows good performance advantages when performing taint analysis many times on one program. On the basis of DRTaint, this paper studies the malicious code behavior extraction method based on taint analysis, and the research finds that DRTaint can provide better support for malicious code behavior extraction.
Synchronous playback technology of airborne network video based on RTP
Authors: An Ying , Hao Zhao , Zhang Guo Wang
Published: April 2021

In the flight test, the airborne video data is an important part of the flight test data. In order to solve the problem of synchronous playback of multiple channels of airborne network video data, a synchronous playback scheme of multiple channels of airborne network video data based on the RTP/RTCP protocol is proposed. The network video data streaming and preprocessing mechanism are used in this paper. Firstly, by identifying the data packets, the network video data packets are extracted from the data recorded on the airborne. Then, after unpacking and time calculation of the RTP-encapsulated H.264 video data, it uses FFmpeg’s good decoding capabilities to decode a frame of image, and uses SDL’s excellent video performance to display YUV data. The synchronization mechanism based on timestamp is adopted to realize the fast synchronized playback of multiple airborne network videos. The software adopts modular and multi-threaded design ideas to improve processing efficiency and ensure functional scalability. At present, the system has been successfully applied to the simultaneous playback of multi-channel airborne network video. The actual application effect shows that the system works stably and can effectively improve the efficiency of model flight tests.
RETRACTED: Opportunities and challenges of digital media art in the era of big data
Authors: Li Jie , Yao Li Juan
Published: April 2021

In the era of big data, science and technology continue to infiltrate into art, providing more new means of expression for art, and expanding the performance space for digital media art. The statistics and analysis of big data can also more clearly analyze the needs of users, which is both an opportunity and a challenge for the development of digital media art.
Research on Cybersecurity Risk Prevention and Control of New Infrastructure
Authors: Zhu Lina , Ma Zhentao , Huang Haibo , Yan Min
Published: April 2021

Currently, the new infrastructure has entered the stage of intensive deployment. While driving the integration of innovative applications of new generation information and communication technologies such as 5G, and accelerating the process of digital transformation, new infrastructure is also facing the challenges of complex and diverse cyber attacks and increasingly severe security risks. This paper systematically analyzes the new infrastructure cybersecurity risks, summarizes the new challenges faced by new infrastructure, and proposes a risk prevention and control model METAD which combines monitoring and early warning, threat intelligence sharing, risk assessment, and intelligent defense.
Flow Identification of Intelligent Wireless Communication Network Based on Optimal Symbol Output Control
Authors: Yu Wenwu , Liu Ruijie , Zhai Yue , Wei Dan
Published: April 2021

Markov decision model is the most suitable decision algorithm for random dynamic system based on Markov process theory. Through its decision set at each node, the path that meets the requirements is selected as the allowed decision set for data forwarding, and it does not completely depend on the historical moment of the entire system. Since multiple paths may be included in the allowed decision set, further optimization is required to select the optimal path. In the paper, Markov routing decision model is applied to comprehensively consider the communication distance matrix, node transfer probability, and the number of node neighbors in the network. Equipped with different weights according to actual application scenarios, the nodes within the communication range are evaluated, and the evaluation results are used as the basis for the optimal symbol output to control the optimal path, thereby improving the identification of wireless communication network traffic, extending the network life cycle, and achieving global routing optimal strategy selection.
Teaching Design and Implementation of Design Mode in the Course of Java Programming in Higher Vocational Colleges
Authors: Zhang Jun
Published: April 2021

Java programming is one of the important courses for computer majors such as software technology in higher vocational colleges. The course has strong logic and abstraction, and is rich in content and practical. When studying this course, students should not only master the Java language and object-oriented programming ideas and methods, and cultivate programming habits, but also learn to use design patterns to efficiently solve real-world problems. In order to achieve the expected teaching goals in the limited class hours, universities must carry out teaching design work. This article will put forward some views on how to optimize the software design model teaching design and teaching activities based on the analysis of related concepts and teaching status.
Surface Roughness Prediction with Machine Learning
Authors: Zhang Wenhe
Published: April 2021

Surface roughness is an exceedingly essential index to measure the quality of materials. For manufacturing and production, it is very significant yet difficult to precisely measure the surface roughness of materials. Based on input features including the rational speed, feed and depth of cutting, we managed to predict the surface roughness with machine learning models, based on a real-world dataset collected in the laboratory. The process and results are discussed in this paper. We demonstrate that the neural network can achieve a better prediction performance, compared with other baseline models. This insight will be of great help to the manufacture industry.
Modulation Recognition of Communication Signals Based on Deep Learning Joint Model
Authors: Li Zongyu
Published: April 2021

In modern communication, it is often required for a non-cooperative party to identify the modulation mode when no prior knowledge is given to facilitate the subsequent demodulation and analysis. However, the traditional modulation recognition process requires cumbersome and uncertain manual signal-feature extraction, making it inapplicable to the complex communication environment. In order to overcome this limitation, this paper proposes a communication-signal modulation recognition model based on the dense connection network (DenseNet) and residual connection network (ResNet). The convolutional block attention mechanism (CBAM) is introduced into the DenseNet and ResNet structures, significantly enhancing the modulation recognition accuracy of the proposed global network model. Besides DenseNet and ResNet, the long short-time memory (LSTM) network is also adopted. The experimental results show the performance of the proposed model.
Research on Application of Computer Aided Mapping in Real Estate Surveying and Mapping
Authors: Li Benli
Published: April 2021

This article analyzes and studies the application advantages of computer-aided drawing. The research content of this paper includes improving the efficiency of drawing, optimizing manual drawing, more perfect graphical output, more convenient storage and management. The author studied the basic data collection, clarifying the principle of layering of drawing elements, determining the form of drawing, editing and processing of collected data, checking of output shape data, graphic plate making and printing and other technical application points. This paper studies the optimization of database content, the improvement of network information sharing, and the optimization of operating systems. The purpose of this article is to optimize the results of real estate surveying and mapping and improve the effect of computer-aided mapping.
Application Analysis of Internet of Things Technology in Large Chain Stores
Authors: Huo Xia
Published: April 2021

The application of the Internet of Things technology in large chain stores can not only accurately locate the location of the goods, understand the information of the goods, and improve the business efficiency of mall operators, but also help consumers quickly find the items they need. At the same time, this can also reduce unnecessary cashier queuing time and help increase the attractiveness and competitiveness of the store. Based on this, this article will analyze how to use the Internet of Things to realize the operation and management of intelligent large-scale chain stores based on the analysis of related concepts. This article studies how to use the Internet of Things business management system to improve business levels and improve operating conditions.
Adaptive Modulation Scheme for Satellite Communication Channel Based on RLNN
Authors: Yu Yongxian , Zhang Luyong
Published: April 2021

This paper discusses the potential applications of reinforcement learning in communication systems. In order to improve the spectral efficiency of wireless communication channels, we propose an adaptive modulation scheme based on reinforcement learning and deep neural network (RLNN) with a average network exploration(AE) strategy. Although the application of machine learning technology in link adaptation has attracted widespread attention, most of the solutions currently proposed are based on offline training algorithms, which are not suitable for real-time operations. Classical reinforcement learning is only effective in discrete actions and states. The proposed technical solution does not depend on the offline training mode. The proposed technical solution uses AE strategy to drive exploration, which can improve the exploration efficiency of the agent. The simulation result shows that we can increase the data transmission rate under limited bandwidth and independently improves link availability, throughput and the efficiency of the wireless communication system.
Network security situation prediction method based on strengthened LSTM neural network
Authors: Dong Zhaowei , Su Xiaoyu , Sun Lihui , Xu Kuikui
Published: April 2021

As an important part of network security situation awareness, network security situation prediction describes the dynamic changes of security situation over time, and predicts future situation values based on historical situation values. In order to improve the accuracy of network security situation prediction, a long- and short-term memory network security situation prediction model based on the Sigmoid weighted reinforcement mechanism is proposed. Firstly, LSTM neural network is used to mine the temporal correlation of network security situation data. Sigmoid weighted linear element is introduced to deal with the gradient problem in the back propagation, and the input value is multiplied by Sigmoid activation function, so as to strengthen the structure of LSTM neural network and improve the accuracy of prediction.Then, the cuckoo search algorithm was used to optimize the super parameters to improve the training time. Finally, the public data set CICIDS2017 was used to verify the model. The simulation experiment results show that the model has a faster convergence rate and smaller errors.
Infrared and visible image fusion based on two-scale decomposition and improved saliency detection
Authors: An Ying , Hao Zhao , Huo Zhaohui
Published: April 2021

Aiming at the problems of poor target saliency, loss of background information and time-consuming in image fusion, a fast image fusion algorithm combining two-scale decomposition and improved saliency detection is proposed. Mean filtering is used to decompose the source image into a base layer and a detail layer. The maximum symmetric surround (MSS) saliency detection algorithm is improved to obtain the dim suppressed MSS algorithm. dim suppressed MSS saliency detection and guided filtering is used to generate fusion rules for each layer. The inverse transformation of two-scale decomposition is used for the fusion sub-image of the base layer and the detail layer to obtain the final fusion result. Experimental results show that the algorithm consumes less time and has better fusion quality, which reflects the feasibility of the proposed algorithm.
Automatic Feature Selection and Ensemble Classifier for Intrusion Detection
Authors: Lin Changjian , Li Aiping , Jiang Rong
Published: April 2021

Anomaly-based Intrusion Detection System (ADS) is one of the technologies widely used in network topology. Although many supervised and unsupervised learning methods in the field of machine learning have been used to improve the efficiency of ADS, achieving good performance is still a challenging problem for existing intrusion detection algorithms. Firstly, there are few public datasets available for evaluation. Secondly, a single classifier may not perform well in detecting each type of attack. Third, some of the existing schemes focus on feature subset selection, while ignoring the design of the classification decision algorithm, or focus on the classification decision algorithm. In order to address this issue, a new intrusion detection framework is proposed by comparing and studying various feature selection technologies and classification decision algorithms in this paper. An automatic parameter adjustment scheme is designed for feature selection and ensemble classification. It avoids the need to obtain the optimal parameters through manual experiments in advance, and can improve the robustness of the parameters and the model. We use the most classic NSL-KDD dataset and the latest CICIDS2018 dataset for comparative experiments. The experimental results demonstrate its efficiency in terms of Accuracy and False Positive Rate.
A Study of Cyber Attack Behavior based on Algebraic Topology
Authors: Zhang Xiaoyu , Yan Huaizhi , Liu Zhen
Published: April 2021

It is inevitable that serious damage will occur once the hosts suffer from cyberattack. Therefore, the study of cyberattacks has become indispensable in the field of cyber security. Host-oriented cyberattacks consist of a series of atomic attacks attacking system objects and metrics on atomic attacks can quantitatively describe the behavior of cyber attacks. When the atomic attack behavior constitutes a manifold topological space, host-oriented assessment of the effects of a cyberattack can be computed using an algebraic topology. The concepts of manifold and other spaces in algebraic topology can be used to model and analyze the system composed of many elements. Therefore, by handling the relationships between metrics appropriately (e.g., complex mappings), the topological space of attack scenarios is constructed. This paper uses a cellular manifold to show the geometric structure of cyberattack behaviors. In this solution, we select the metrics depicting the cyber attack behavior and clear their dependence through the construction of cyberattack behavior sequence graph, and then put forward the cyberattack behavior model based on the relationship between metrics. WannaCry ransomware attack is analyzed by applying our model, which provides a new idea for quantifying cyberattack.
A pseudorandom bit generator based on arctangent function
Authors: Wu Qi
Published: April 2021

In this paper, in addition to translation & scale (already adopted before), we employ another elementary transformation, i.e. fold, to acquire a variant of Arctangent Function. Both Bifurcation Diagram & Lyapunov Exponent Spectrum indicate that the new mapping owns excellent chaotic properties. Afterwards, a pseudorandom bit generator is devised based on it. Pseudorandom tests illustrate that the mapping is the 3rd best one so far. The results show great application prospect.
MBA: A Multimodal Bilinear Attention Model with Residual Connection for Abstractive Multimodal Summarization
Authors: Ye Xia , Yue Zengying , Liu Ruiheng
Published: April 2021

The combination of vision and natural language modalities has become an important topic in both computer vision and natural language processing research communities. Multimodal summarization has received unprecedented attention with the rapid growth of multimodal information. This paper proposes MBA which consists of pre-trained feature extractors, text encoder, image encoder, multimodal bilinear attention fusion module, and summary decoder to complete abstractive multimodal summarization task. A residual network is added to the model to enhance the textual modality information and alleviate the modality-bias problem. Experiments show that the model is better than the baseline models and performs better than text summarization methods that ignore visual modality.
In vivo vascularization and islet function in a microwell device for pancreatic islet transplantation
Authors: Smink Alexandra M , Skrzypek Katarzyna , Liefers-Visser Jolanda A L , Kuwabara Rei , de Haan Bart J , de Vos Paul , Stamatialis Dimitrios
Published: April 2021

Islet encapsulation in membrane-based devices could allow for transplantation of donor islet tissue in the absence of immunosuppression. To achieve long-term survival of islets, the device should allow rapid exchange of essential nutrients and be vascularized to guarantee continued support of islet function. Recently, we have proposed a membrane-based macroencapsulation device consisting of a microwell membrane for islet separation covered by a micropatterned membrane lid. The device can prevent islet aggregation and support functional islet survival in vitro. Here, based on previous modeling studies, we develop an improved device with smaller microwell dimensions, decreased spacing between the microwells and reduced membrane thickness and investigate its performance in vitro and in vivo. This improved device allows for encapsulating higher islet numbers without islet aggregation and by applying an in vivo imaging system we demonstrate very good perfusion of the device when implanted intraperitoneally in mice. Besides, when it is implanted subcutaneously in mice, islet viability is maintained and a vascular network in close proximity to the device is developed. All these important findings demonstrate the potential of this device for islet transplantation.
Enhanced harmony search algorithm for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem
Authors: Niu Wen-jing , Feng Zhong-kai , Jiang Zhi-qiang , Wang Sen , Liu Shuai , Guo Wei , Song Zhen-guo
Published: April 2021

With the merits of superior performance and easy implementation, the harmony search (HS), a famous population-based evolutionary method, has been widely adopted to resolve global optimization problems in practice. However, the standard HS method still suffers from the defects of premature convergence and local stagnation in the complex multireservoir operation problem. Thus, this study develops an enhanced harmony search (EHS) method to improve the HS’s search ability and convergence rate, where adaptive parameter adjustment strategy is used to enhance the global search performance of the swarm, while the elite-learning evolutionary mode is used to improve the converge trajectory of the population. To verify its practicability, EHS is applied to solve numerical optimization and multireservoir operation problems. The results show that EHS can produce better results than several existing methods in different cases. For instance, the mean objective of EHS is improved by about 23.9%, 28.7% and 26.8% compared with particle swarm optimization, differential evolution and gravitational search algorithm in 1998–1999 typical runoff case. Hence, an effective optimizer is developed for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem.
Economic-mathematical Models for Analyzing the Potential for Self-Development of Territorial Systems
Authors: Yakovenko I V , Streltsova E D , Komissarov V D
Published: April 2021

The article is devoted to the construction of tools to support decision-making for the analysis and formation of conditions for self-development of administrative-territorial entities, which contributes to the creation of a competitive environment in them. The Toolkit is based on the use of methods of economic and mathematical modeling in the selection of methods of inter-budgetary regulation in the aspect of activation of functions: stimulating and leveling. The incentive function is aimed at increasing the motivation of the authorities in building up the territorial tax potential and is implemented through the distribution of tax revenues among the budgets vertically according to established standards. The leveling function is performed through financial assistance methods. In order to support decision-making regarding the application of forms of financial policy, the article offers a mathematical model for analyzing territorial units from the point of view of identifying the potential for self-development of territories. The model is based on the application of nonlinear dynamics methods that allow us to get an idea of the qualitative picture of the trend in the dynamics of indicators of development of the territorial system. The presence of an attractor in the dynamics of indicators allows us to judge the potential ability of the territory to self-organization and determines the feasibility of using the stimulating function of inter-budgetary regulation as a prerequisite for the sustainable development of the socio-economic system at the expense of its own revenue sources.
Impact of the Ports of the Northern Sea Route on the Development of Focal Zones of the Russian Eastern Arctic
Authors: Leonov S N , Zaostrovskikh E A
Published: April 2021

An attempt is made in this work to identify the mechanisms of the influence of the ports of the Northern Sea Route (NSR) on increasing the efficiency of the formation of focal zones for the development of the Eastern Arctic. The propulsive potential of the transport infrastructure is considered; the importance of the NSR for the integration of the southern and Arctic regions of the Far East is indicated. The methodology of the work is based on the theory of cumulative growth, in which the ideas of the formation of "growth poles" occupy a decisive position. The work uses methods of grouping, logical and comparative analysis. The information base of the study was the data of Rosstat. It is shown that the state’s activity on the development of the NSR stimulates the development of the North Yakutsk and Chukotka support zones, enhancing the transport and raw materials specialization of the Eastern Arctic as a whole. The paper substantiates that in the Far Eastern Arctic, the traditional view of ports as the most important infrastructure assets, which are the business and economic center of territorial development, is insufficient for the recognition of transport infrastructure as a propulsion industry. Transport is a necessary but insufficient condition for the emergence and accelerated development of local "growth poles" in the Arctic. The propulsive effect of the port economy is low and is mainly of an induced nature, when capacities for product processing are created in the port territories, or when the transport infrastructure stimulates the emergence of transport-intensive extractive industries.
Human Capital Development in the Context of the Fourth Industrial Revolution
Authors: Balog M M , Demidova S E
Published: April 2021

In the context of the fourth industrial revolution, high-quality human capital that creates innovations is becoming the key factor in building the competitiveness of individual companies and the national economy as a whole. The digital transformation of public life has had a significant impact on human capital development. Remote communication, robotics and artificial intelligence technologies have created new opportunities in education, health care and employment. Education and health care have become more individualized and high-tech. Opportunities for remote employment and combining work in several companies have appeared. However, digitization also poses challenges and threats to human capital development. These include the need for continuous upgrading of competencies, increased levels of stress due to the increasing information and emotional load on an individual operating in the digital environment. The general job cuts and decline in the rights of employees who have switched to telecommuting also characterize digital economy. In addition, the introduction of digital tools increases the risks of discrimination against various population groups and growing social and economic inequality. Under these conditions, holders of human capital face the need to acquire new knowledge, improve their professional qualifications and comprehensive personal development throughout their lives. In order to ensure the least painful inclusion of all society members in the digital environment, the state and business must create conditions for increasing the level of digital, financial and legal competence of population.
Microbiota of Aquatic and Terrestrial Habitats of the Dzou Cave
Authors: Mazina S E , Yu Titov A , Kozlova E V , Popkova A V
Published: April 2021

Microbiota of the deep caves has been poorly explored. The most relevant issues of the caves biodiversity are the sources of the microorganisms’ propagules and characteristics of autochthonous microbiota. The investigation presents the assessment of microbiota biodiversity from the Dzou cave (Western Caucasus). The aim of the study was to identify the species composition of micromycetes and phototrophs of the cave and to assess the sanitary-indicative microbiota in different habitats depending on the anthropogenic load. Microscopic and cultural methods were used to identify the microbiota from the entrance zone of the cave, as well as from aquatic and terrestrial habitats of the unlit deepest parts of the cave. The analysis of the phototrophic communities composition developed in the entrance area of the cave was carried out, 22 species were identified, among which cyanobacteria prevailed. The biodiversity of micromycetes was determined: 48 species were identified in the entrance zone, 60 species – in the dark zone of the cave. Comparison of the species composition of the dark and illuminated zones of the cave showed that in addition to speciesHumicola grisea, Hemicarpenteles ornatusandAlternaria sp., all species of entrance area are found in the unlit part of the cave. Representatives of genusPenicilliumandAspergilluswere dominants. Revealed increase of the micromycetes and bacteria number was driving by increase in the level of anthropogenic load, especially in places of tourist camps. It was noted that the propagules of phototrophs are present in the cave substrates even at great depth, excluding some water samples, which may indicate the periodic drift of propagules or anthropogenic factor. The largest number of micromycetes species was detected in the clay deposits and rock samples. The least number of species was isolated from the water streams of the cave. A large number of micromycetes propagules was found in substrates near tourist camps, but their biodiversity was lower than in cave soils.
Design and Analysis of a Signal Denoising and Estimation System
Authors: Naji Hassan Saadallah , Ahmed Hanan Badeea
Published: April 2021

Heart disease is one of the most common causes of death worldwide and electrocardiogram (ECG) signals are among the best tools currently available for diagonising heart disease by offering accurate data about heart performance and the circulatory system. Unfortunately, the linear-quadratic problem often arises, which is a serious problem affecting the estimation of any linear dynamic system’s immediate state due to disruption with white noise due to the linear employment of information about state and white noise corruption. This can theoritecally be addressed by using a Kalman filter as a recursive estimator. However, all biomedical applications that generate electrical signals are disposed to creating different types of noise with fixed coefficients filters, due to the random nature of these signals, and these can cause inaccuracies over time. In this paper, a double Kalman filter is suggested for ECG signal denoising and estimation. Additive White Noise added to the original ECG signal to form a noisy signal, and Kalman filter was used initially to improve the quality of the ECG signal and to offer high performance convergence while increasing the SNR. The second step involved employing Kalman filter to estimate the new denoised ECG signal during a given time period, as accuracy in time is very important in medical applications. The results were satisfactory, offering highly recognisable estimations.