Research progress of abnormal user detection technology in social network by Qiang QU, Hongtao YU, Ruiyang HUANG

“…In social networks,the problem of anomalous users detection is one of the key problems in network security research.The anomalous users conduct false comments,cyberbullying or cyberattacks by creating multiple vests,which seriously threaten the information security of normal users and the credit system of social networks ,so a large number of researchers conducted in-depth study of the issue.The research results of the issue in recent years were reviewed and an overall structure was summarized.The data collection layer introduces the data acquisition methods and related data sets,and the feature presentation layer expounds attribute features,content features,network features,activity features and auxiliary features.The algorithm selection layer introduces supervised algorithms,unsupervised algorithms and graph algorithms.The result evaluation layer elaborates the method of data annotation method and index.Finally,the future research direction in this field was looked forward.…”
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A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study by Maria Habib, Victor Vicente-Palacios, Pablo García-Sánchez

“…This paper introduces a multi-objective bio-inspired, AI-based optimization approach for the automated detection of voice disorders. Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. …”
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Automatic Registration and Error Detection of Multiple Slices Using Landmarks by Hans Frimmel, Lars Egevad, Christer Busch, Ewert Bengtsson

“…This leads to severe registration problems. In this paper, a method for automatic registration and error detection of slices using landmark needles has been developed. …”
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A privacy-enhanced framework with deep learning for botnet detection by Guangli Wu, Xingyue Wang

“…Based on this problem, this article proposes a privacy-enhanced framework with deep learning for botnet detection. …”
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Robust Symbol Detection in Large-Scale Overloaded NOMA Systems by Hiroki Iimori, Giuseppe Thadeu Freitas De Abreu, Takanori Hara, Koji Ishibashi, Razvan-Andrei Stoica, David Gonzalez G., Osvaldo Gonsa

“…The framework is built upon a novel compressed sensing (CS) regularized maximum likelihood (ML) formulation of the discrete-input detection problem, in which the <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-norm is introduced to enforce adherence of the solution to the prescribed discrete symbol constellation. …”
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Comparative Study of Machine Learning Techniques for Insurance Fraud Detection by Navin Duwadi, Anita Sharma

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Deep encoder-decoder networks for belt longitudinal tear detection by Lei You, Minghua Luo, Xinglin Zhu, Bin Zhou

“…An improved encoder-decoder network was proposed to solve the longitudinal tear detection problem. This method utilizes a line structured light system for image acquisition. …”
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Spectral characterization of hierarchical network modularity and limits of modularity detection. by Somwrita Sarkar, James A Henderson, Peter A Robinson

“…However, there exists no algorithm-independent metric to characterize hierarchical modularity in a complex system. …”
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Wavelet neural network algorithm for hybrid GA in infrared CO2 gas sensor by Jun Wang, Yuanxi Wang

“…Nevertheless, as a representative of greenhouse gases, CO2 gas detectors are susceptible to environmental temperature fluctuations, which impairs the accuracy of detection. To address this issue, the research team innovatively combined the genetic algorithm (GA) and the wavelet neural network (WNN) to develop a solution for the temperature compensation problem of the infrared CO2 gas sensor. …”
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Defect detection based on extreme edge of defective region histogram by Zouhir Wakaf, Hamid A. Jalab

“…Automatic thresholding problem has been addressed well by the commonly used Otsu method, which provides suitable results for thresholding images based on a histogram of bimodal distribution. …”
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Syntactic–Semantic Detection of Clone-Caused Vulnerabilities in the IoT Devices by Maxim Kalinin, Nikita Gribkov

“…This paper addresses the problem of IoT security caused by code cloning when developing a massive variety of different smart devices. …”
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A lightweight personnel detection method for underground coal mines by Shuai WANG, Wei YANG, Yuxiang LI, Jiaqi WU, Wei YANG

“…Compared with YOLOv5s, the mAP is improved by 7.3%, the computation amount is reduced by 27.6%, and the FPS is improved by 6.3. The proposed algorithm significantly improves the accuracy of personnel detection in underground coal mines, and efficiently solves the problem of difficult personnel detection caused by low brightness and uneven illumination.…”
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Three-Dimensional Autonomous Obstacle Avoidance Algorithm for UAV Based on Circular Arc Trajectory by Jiandong Guo, Chenyu Liang, Kang Wang, Biao Sang, Yulin Wu

“…Firstly, information on irregular obstacles is obtained by an onboard detection system; this information is then transformed into standard convex bodies, which are used to generate circular arc avoidance trajectories, and the obstacle avoidance problem is turned into a trajectory tracking strategy. …”
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The analysis of fraud detection in financial market under machine learning by Jing Jin, Yongqing Zhang

“…Traditional fraud detection methods based on rules and statistical analysis are difficult to deal with increasingly complex and evolving fraud methods, and there are problems such as poor adaptability and high false alarm rate. …”
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Research on formant estimation algorithm for high order optimal LPC root value screening by Hua LONG, Shumeng SU

“…Objectives: The existing linear prediction (LP) formant estimation algorithms are difficult to locate formant precisely because of the pseudo root interference and interaction between poles.Because of the low order fitting formant of LP prediction,the accuracy of formant extraction is fundamentally limited.It is difficult to remove false roots and spectrum aliasing caused by pole interaction in the formant extraction of high-order LP.In order to solve the problem of large error of LP formant detection,a formant estimation algorithm based on high-order LP coefficient root value screening was proposed. …”
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A YOLO-Based Method for Head Detection in Complex Scenes by Ming Xie, Xiaobing Yang, Boxu Li, Yingjie Fan

“…Traditional object detection algorithms struggle to address the challenge of small object detection. …”
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Research on Calibration Algorithm of Camera and LiDAR for Transport Vehicles in Open-pit Mines by XU Yanghan, LIN Jun, KANG Gaoqiang, YOU Jun, YUE Wei, TONG Hao

“…In the intelligent driving system of transport vehicles in open-pit mines, the data fusion of camera and LiDAR plays a vital role in the detection, recognition and tracking of obstacles. Aiming at the problem of camera and LiDAR spatial coordinate unity, that is establishing the correspondence between 2D coordinate system of image and 3D coordinate system of point cloud, this paper proposes a camera and LiDAR calibration method based on chessboard calibration board. …”
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Noncoherent multiple symbol detection of CPFSK based on decision-feedback by Qiang CHEN, Guo-sheng RUI, Wen-jun SUN, Wen-biao TIAN, Yang ZHANG

“…Considering these problems, an improved algorithm based on decision feedback was proposed. …”
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Detecting phishing gangs via taint analysis on the Ethereum blockchain by Kangrui Huang, Weili Chen, Zibin Zheng

“…As phishers often use multiple accounts to commit phishing scams and money laundering, detecting phishing gangs in the blockchain ecosystem is a real and critical problem. …”
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Hard-coded backdoor detection method based on semantic conflict by Anxiang HU, Da XIAO, Shichen GUO, Shengli LIU

“…The current router security issues focus on the mining and utilization of memory-type vulnerabilities, but there is low interest in detecting backdoors.Hard-coded backdoor is one of the most common backdoors, which is simple and convenient to set up and can be implemented with only a small amount of code.However, it is difficult to be discovered and often causes serious safety hazard and economic loss.The triggering process of hard-coded backdoor is inseparable from string comparison functions.Therefore, the detection of hard-coded backdoors relies on string comparison functions, which are mainly divided into static analysis method and symbolic execution method.The former has a high degree of automation, but has a high false positive rate and poor detection results.The latter has a high accuracy rate, but cannot automate large-scale detection of firmware, and faces the problem of path explosion or even unable to constrain solution.Aiming at the above problems, a hard-coded backdoor detection algorithm based on string text semantic conflict (Stect) was proposed since static analysis and the think of stain analysis.Stect started from the commonly used string comparison functions, combined with the characteristics of MIPS and ARM architectures, and extracted a set of paths with the same start and end nodes using function call relationships, control flow graphs, and branching selection dependent strings.If the strings in the successfully verified set of paths have semantic conflict, it means that there is a hard-coded backdoor in the router firmware.In order to evaluate the detection effect of Stect, 1 074 collected device images were tested and compared with other backdoor detection methods.Experimental results show that Stect has a better detection effect compared with existing backdoor detection methods including Costin and Stringer: 8 hard-coded backdoor images detected from image data set, and the recall rate reached 88.89%.…”
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