Spot the bot: the inverse problems of NLP by Vasilii A. Gromov, Quynh Nhu Dang, Alexandra S. Kogan, Assel Yerbolova

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Sensitivity to visual features in inattentional blindness by Makaela Nartker, Chaz Firestone, Howard Egeth, Ian Phillips

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Lightweight blasthole image detection and positioning method by Shan PAN, Ting YU, Zhongwen YUE, Zijian TIAN, Qingyu JIN

“…In terms of blasthole detection accuracy, the algorithm addresses the issue of false detection caused by the influence of surrounding rock backgrounds and rock shadows, as well as the problem of missed detection due to limited contextual information and identifiable features of blastholes in images. …”
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Optimized DINO model for accurate object detection of sesame seedlings and weeds by Yong Wang, ShunFa Xu, ZhenYuan Ye, KongHao Cheng

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Multiscale feature cross‐layer fusion remote sensing target detection method by Yuting Lin, Jianxun Zhang, Jiaming Huang

“…Finally, a multiscale feature cross‐layer fusion structure (S‐160) is proposed based on YOLOv5, which improves the detection accuracy of each scale target by fusing shallow and deep feature information and introduces new large‐scale features for small target detection to solve the problem that ultrasmall targets in remote sensing images cannot be recognised. …”
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A photovoltaic anomaly data identification method based on image feature detection by QIU Yutao, ZHANG Lei, ZHOU Kaiyun, YAN Min, SUN Jintong, LONG Huan

“…To address this， this paper introduces an anomaly data identification algorithm based on image feature detection and dual-threshold processing. This method maps numerical data to images， transforming the anomaly detection problem into an image processing problem. …”
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Ship detection optimization method in SAR imagery based on multi-feature weighting by Quanhua ZHAO, Xiao WANG, Yu LI, Guanghui WANG

“…Aiming at the problem that the accuracy of traditional ship detection algorithms is not satisfying in complex scene with many false alarm targets,a ship detection optimization method in SAR imagery based on multi-feature weighting was proposed.Firstly,the marker-based watershed algorithm was employed to remove land from SAR amplitude image.Then,the CFAR algorithm based on log-normal distribution was used to obtain candidate targets from no land image.Furthermore,the length to width ratio,the ship area and the contrast ratio of the candidate targets were extracted.Finally,a variance coefficient method was proposed to distribute the weight of the three features,and the confidence levels were calculated by combining the normalized feature vectors of the candidate targets with the feature weight.By determining the best confidence level,false alarm targets among the candidate targets were removed to optimize ship detection results.In order to verify the proposed method,experiments were carried on with the GF-3 SAR images of different complex scenes.The experimental results show that the proposed method is feasible and effective.…”
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A feature-based intelligent deduplication compression system with extreme resemblance detection by Xiaotong Wu, Jiaquan Gao, Genlin Ji, Taotao Wu, Yuan Tian, Najla Al-Nabhan

“…In this paper, we study the problem of utilising the duplicate and resemblance detection techniques to further compress data. …”
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Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining by Shenyu Chen, Meng Li, Weifeng Yang

“…This paper addresses the problem of detecting internet rumors in social media. …”
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Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment by Yulan Xu, Qiaowei Li

“…In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. …”
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Infrared Small Target Detection Based on Density Peak Search and Local Features by Leihong Zhang, Hui Yang, Qinghe Zheng, Yiqiang Zhang, Dawei Zhang

“…In this paper, we propose a target detection method. First, to address the problem that the proximity of targets to high-brightness clutter leads to missed detection of candidate targets, a Gaussian differential filtering preprocessed image is used to suppress high-brightness clutter. …”
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Leaf disease detection and classification in food crops with efficient feature dimensionality reduction. by Khasim Syed, Shaik Salma Asiya Begum, Anitha Rani Palakayala, G V Vidya Lakshmi, Sateesh Gorikapudi

“…This study proposes a computer vision system that integrates BiLSTM with CNN features for image categorization tasks. The system effectively reduces feature dimensionality using learned features, addressing the high dimensionality problem in leaf image data and enabling early, accurate disease identification. …”
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Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion by Yongjun Qi, Hailin Tang, Altangerel Khuder

“…In order to quickly and accurately detect and maintain the fan blades, based on the intelligent big data from the environment, we propose the convolutional neural network model to solve the problem of low recognition rate due to the lack of feature extraction in the fan blade crack image, and the long short-term memory network (Long Short-Term Memory, LSTM) convolutional neural network model, and the dimensionality reduction of the captured image data, which is beneficial to improve the recognition rate of the picture and reduce the loss rate of the picture through the detection model’s suitable recognition of complex background problems such as target occlusion and overlap. …”
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Insulator Surface Defect Detection Method Based on Graph Feature Diffusion Distillation by Shucai Li, Na Zhang, Gang Yang, Yannong Hou, Xingzhong Zhang

“…Aiming at the difficulties of scarcity of defect samples on the surface of power insulators, irregular morphology and insufficient pixel-level localization accuracy, this paper proposes a defect detection method based on graph feature diffusion distillation named GFDD. …”
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DMFFNet: Dual-Mode Multiscale Feature Fusion-Based Pedestrian Detection Method by Ruizhe Hu, Ting Rui, Yan Ouyang, Jinkang Wang, Qunyan Jiang, Yinan Du

“…To address this problem, we propose a dual-modal multi-scale feature fusion network (DMFFNet). …”
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A novel dynamic weighted prediction framework with stability-enhanced dynamic thresholding feature selection for neurodegenerative disease detection using gait features by Diksha Giri, Ranjit Panigrahi, Samrat Singh Bhandari, Moumita Pramanik, Akash Kumar Bhoi, Victor Hugo C. de Albuquerque

“…The DWPF ensemble and SEDT feature selection approach are merged to detect neurodegenerative diseases in gait data. …”
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A Moving Object Detection Method Based on Conditional Information and Feature Deepening by Hongrui Zhang, Luxia Yang

“…In the field of moving object detection methods relying on generative adversarial networks, there are problems such as uncontrollable generation results and incomplete extraction of target details. …”
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Unsupervised detection method of RoQ covert attacks based on multilayer features by Jing ZHAO, Jun LI, Chun LONG, Wei WAN, Jinxia WEI, Kai CHEN

“…To solve the problems that RoQ covert attacks are hidden in overwhelming background traffic and difficult to identify, besides the existing samples are scarce and cannot provide large-scale learning data, an unsupervised detection method of RoQ covert attacks based on multilayer features was proposed under the condition of very little prior knowledge.First, considering that most normal flow might interfere with subsequent results, a classification method based on semi-supervised spectral clustering was studied by flow characteristics, so that the proportion of normal samples in the filtered traffic was close to 100%.Secondly, in order to distinguish the nuance between the hidden attack features and normal flow without relying on the attack samples, an unsupervised detection model based on the n-Shapelet subsequence was constructed by packet characteristics, and the subsequences with obvious difference were used, which enabled detection of RoQ convert attacks.Experimental results demonstrate that with only a small number of learning samples, the proposed method has higher precision and recall rate than existing methods, and is robust to evading attacks.…”
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Multi-stage detection method for APT attack based on sample feature reinforcement by Lixia XIE, Xueou LI, Hongyu YANG, Liang ZHANG, Xiang CHENG

“…Given the problems that the current APT attack detection methods were difficult to perceive the diversity of stage flow features and generally hard to detect the long duration APT attack sequences and potential APT attacks with different attack stages, a multi-stage detection method for APT attack based on sample feature reinforcement was proposed.Firstly, the malicious flow was divided into different attack stages and the APT attack identification sequences were constructed by analyzing the characteristics of the APT attack.In addition, sequence generative adversarial network was used to simulate the generation of identification sequences in the multi-stage of APT attacks.Sample feature reinforcement was achieved by increasing the number of sequence samples in different stages, which improved the diversity of multi-stage sample features.Finally, a multi-stage detection network was proposed.Based on the multi-stage perceptual attention mechanism, the extracted multi-stage flow features and identification sequences were calculated by attention to obtain the stage feature vectors.The feature vectors were used as auxiliary information to splice with the identification sequences.The detection model’s perception ability in different stages was enhanced and the detection accuracy was improved.The experimental results show that the proposed method has remarkable detection effects on two benchmark datasets and has better effects on multi-class potential APT attacks than other models.…”
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Multi-stage detection method for APT attack based on sample feature reinforcement by Lixia XIE, Xueou LI, Hongyu YANG, Liang ZHANG, Xiang CHENG

“…Given the problems that the current APT attack detection methods were difficult to perceive the diversity of stage flow features and generally hard to detect the long duration APT attack sequences and potential APT attacks with different attack stages, a multi-stage detection method for APT attack based on sample feature reinforcement was proposed.Firstly, the malicious flow was divided into different attack stages and the APT attack identification sequences were constructed by analyzing the characteristics of the APT attack.In addition, sequence generative adversarial network was used to simulate the generation of identification sequences in the multi-stage of APT attacks.Sample feature reinforcement was achieved by increasing the number of sequence samples in different stages, which improved the diversity of multi-stage sample features.Finally, a multi-stage detection network was proposed.Based on the multi-stage perceptual attention mechanism, the extracted multi-stage flow features and identification sequences were calculated by attention to obtain the stage feature vectors.The feature vectors were used as auxiliary information to splice with the identification sequences.The detection model’s perception ability in different stages was enhanced and the detection accuracy was improved.The experimental results show that the proposed method has remarkable detection effects on two benchmark datasets and has better effects on multi-class potential APT attacks than other models.…”
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