Heterogeneous Graph Neural Network Framework for Session-Based Cyberbullying Detection by Munkhbuyan Buyankhishig, Thanda Shwe, Israel Mendonca, Masayoshi Aritsugi

“…Consequently, critical patterns and insights within sessions may be overlooked. To address this problem, we initially modeled social media sessions and their interactions as heterogeneous graphs. …”
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Tomato detection in natural environment based on improved YOLOv8 network by Wancheng Dong, Yipeng Zhao, Jiaxing Pei, Zuolong Feng, Zhikai Ma, Leilei Wang, Simon Shemin Wang

“… In this paper, an improved lightweight YOLOv8 method is proposed to detect the ripeness of tomato fruits, given the problems of subtle differences between neighboring stages of ripening and mutual occlusion of branches, leaves, and fruits. …”
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FEATURES OF PHYSICAL DEVELOPMENT AND TOOTH DECAY MORBIDITY IN CHILDREN WHO LIVE IN ENVIRONMENTALLY ADVERSE CONDITIONS by L.S. Lagoda, H.G. Musji-Sementsiv, T.Y Lisak

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Adaptive Circular Receptive Field for Remote Sensing Image Target Detection by Ning Wen, Yun Ge, Xiaoyu Yang, Meigen Yu, Ting Wang

“…However, general methods are prone to incorporating background information and have problems with rotation angles when detecting circular and square-like targets. …”
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Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm by ZHU Yumin, SUN Guangling, MIAO Fei

“…In view of the problems of inaccurate positioning and insufficient detection accuracy in pedestrian detection in fisheye images in existing target detection algorithms, an improved YOLOv8 algorithm for fisheye image detection is proposed. …”
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Deep learning-assisted terahertz intelligent detection and identification of cancer tissue by Xingyu Wang, Yafei Xu, Rong Wang, Nuoman Tian, Zhengpeng Zhu, Shuting Fan, Liuyang Zhang, Ruqiang Yan, Xuefeng Chen

“…Traditional cancer prescreening is not only cumbersome but also heavily reliant on sufficient expert knowledge, which inevitably increases the complexity of cancer diagnosis and limits early cancer diagnosis. To overcome this problem, recent terahertz (THz) technology, as an unconventional bio-friendly detection approach, has emerged with great potential in human disease diagnosis due to its non-ionic and high-resolution features. …”
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Lightweight defect detection algorithm of tunnel lining based on knowledge distillation by Anfu Zhu, Jiaxiao Xie, Bin Wang, Heng Guo, Zilong Guo, Jie Wang, Lei Xu, SiXin Zhu, Zhanping Yang

“…Secondly, in the distillation process, the feature and output dimension results are fused to improve the detection accuracy, and the mask feature relationship is learned in the space and channel dimension to improve the real-time detection. …”
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Improved YOLOv8 Model for Multi-type Lung Nodule Detection by BAO Qiangqiang, TANG Siyuan, LI Qingqian, WANG Naiyu, YANG Min, GU Yu, ZHAO Jinliang, GAO Jingbo, WANG Jiaxin, QU Yuhan

“…Secondly, the ECLA-HSFPN module is proposed to reconstruct the feature fusion part of the model to improve the scale-invariant lung nodule detection accuracy. …”
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Lightweight and hybrid transformer-based solution for quick and reliable deepfake detection by Geeta Rani, Atharv Kothekar, Shawn George Philip, Vijaypal Singh Dhaka, Ester Zumpano, Ester Zumpano, Eugenio Vocaturo, Eugenio Vocaturo

“…Moreover, it utilizes the unique features of transformer and Linformer models to enhance the robustness and generalization of deepfake detection techniques. …”
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A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks by ÙAfsaneh Banitalebi Dehkordi, MohammadReza Soltanaghaei, Farsad Zamani Boroujeni

“…DDoS (Distributed Denial-of-Service) attacks are among the cyberattacks that are increasing day by day and have caused problems for computer network servers. With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of attack can be much more difficult for them, ignoring effective parameters such as port and Source IP in detecting attacks, providing costly solutions which are effective in increasing CPU load, and low accuracy in detecting attacks are of the problems of previously presented methods in detecting DDoS attacks. …”
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FoT: an efficient transformer framework for real-time small object detection in football videos by Wentao Zhang, Yaocong Yang

“…Meanwhile, Transformer-based methods face challenges in capturing fine-grained target information due to their high computational cost and slow training convergence. To address these problems, we propose a novel end-to-end detection framework–Football Transformer (FoT). …”
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Complex dark environment-oriented object detection method based on YOLO-AS by Bin Ren, Zhaohui Xu, Junwu Zhao, Rujiang Hao, Jianchao Zhang

“…Abstract For object detection in complex dark environments, the existing methods generally have problems such as low detection accuracy, false detection and missed detection. …”
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Detection and Geolocation of Peat Fires Using Thermal Infrared Cameras on Drones by Temitope Sam-Odusina, Petrisly Perkasa, Carl Chalmers, Paul Fergus, Steven N. Longmore, Serge A. Wich

“…In areas with limited accessibility and periods of thick haze and fog, these fires are difficult to detect, localize, and tackle. To address this problem, thermal infrared cameras mounted on drones can provide a potential solution since they allow large areas to be surveyed relatively quickly and can detect thermal radiation from fires above and below the peat surface. …”
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Hyperspectral Anomaly Detection Based on Intrinsic Image Decomposition and Background Subtraction by Jiao Jiao, Longlong Xiao, Chonglei Wang

“…In the anomaly detection of hyperspectral images, the salient feature map and spatial-spectral features are not effectively used, which greatly limits the detection performance. …”
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Research on multi dimensional feature extraction and recognition of industrial and mining solid waste images based on mask R-CNN and graph convolutional networks by Shuqin Wang, Na Cheng, Yan Hu

“…Abstract Aiming at the problems of traditional methods for multi-dimensional feature extraction of industrial and mining solid waste images, such as single feature extraction, difficult fusion, missing high-order features, weak generalization ability and low computational efficiency, an innovative solution combining Mask R-CNN with Graph Convolutional Networks (GCN) was proposed to achieve automatic, multi-dimensional and efficient feature extraction. …”
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Pulmonary Nodules Detection Algorithm Combining Multi-view and Attention Mechanism by LIU Yu-bo, LIU Guo-zhu, SHI Cao, XU Can-hui

“…To solve the problems of low detection rate and high false positive of nodules in low-dose lung CT images by traditional computer-aided diagnosis system, a two-stage pulmonary nodules detection model based on U-Net network and attention mechanism was proposed. …”
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A novel lightweight model for tea disease classification based on feature reuse and channel focus attention mechanism by Junjie Liang, Renjie Liang, Dongxia Wang

“…The FEMBCM solves the problem of detail feature loss caused by traditional convolution downsampling. …”
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DMSF-YOLO: Cow Behavior Recognition Algorithm Based on Dynamic Mechanism and Multi-Scale Feature Fusion by Changfeng Wu, Jiandong Fang, Xiuling Wang, Yudong Zhao

“…Finally, the Dynamic Head detection head is introduced to enhance the model’s scale, spatial location, and perception of different tasks, enhance the capacity to extract similar behavioral features of cows, and improve the model’s performance in detecting cow multi-scale behaviors in complex environments. …”
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FPGA-oriented lightweight multi-modal free-space detection network by Feiyi Fang, Junzhu Mao, Wei Yu, Jianfeng Lu

“…For autonomous vehicles, free-space detection is an essential part of visual perception. …”
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Detection of SSL/TLS protocol attacks based on flow spectrum theory by Shize GUO, Fan ZHANG, Zhuoxue SONG, Ziming ZHAO, Xinjie ZHAO, Xiaojuan WANG, Xiangyang LUO

“…Network attack detection plays a vital role in network security.Existing detection approaches focus on typical attack behaviors, such as Botnets and SQL injection.The widespread use of the SSL/TLS encryption protocol arises some emerging attack strategies against the SSL/TLS protocol.With the network traffic collection environment that built upon the implements of popular SSL/TLS attacks, a network traffic dataset including four SSL/TLS attacks, as well as benign flows was controlled.Considering the problems that limited observability of existing detection and limited separation of the original-flow spatiotemporal domains, a flow spectrum theory was proposed to map the threat behavior in the cyberspace from the original spatiotemporal domain to the transformed domain through the process of “potential change” and obtain the “potential variation spectrum”.The flow spectrum theory is based on a set of separable and observable feature representations to achieve efficient analysis of network flows.The key to the application of flow spectrum theory in actual cyberspace threat behavior detection is to find the potential basis matrix for a specific threat network flow under the condition of a given transformation operator.Since the SSL/TLS protocol has a strong timing relationship and state transition process in the handshake phase, and there are similarities between some SSL/TLS attacks, the detection of SSL/TLS attacks not only needs to consider timing context information, but also needs to consider the high-separation representation of TLS network flows.Based on the flow spectrum theory, the threat template idea was used to extract the potential basis matrix, and the potential basis mapping based on the long-short-term memory unit was used to map the SSL/TLS attack network flow to the flow spectrum domain space.On the self-built SSL/TLS attack network flow data set, the validity of the flow spectrum theory is verified by means of classification performance comparison, potential variation spectrum dimensionality reduction visualization, threat behavior feature weight evaluation, threat behavior spectrum division assessment, and potential variation base matrix heatmap visualization.…”
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