Testing the functionality and contact error of a GPS‐based wildlife tracking network by Melanie J. Davis, Sravan Thokala, Xinyu Xing, N. Thompson Hobbs, Michael W. Miller, Richard Han, Shivakant Mishra

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Automated Dead Chicken Detection in Poultry Farms Using Knowledge Distillation and Vision Transformers by Ridip Khanal, Wenqin Wu, Joonwhoan Lee

“…Then, a deep learning classifier, enhanced through knowledge distillation, confirms whether the detected stationary object is indeed a chicken. EfficientNet-B0 is employed as the teacher model, while DeiT-Tiny functions as the student model, balancing high accuracy and computational efficiency. …”
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Leveraging large language models for automated detection of velopharyngeal dysfunction in patients with cleft palate by Myranda Uselton Shirk, Catherine Dang, Jaewoo Cho, Hanlin Chen, Lily Hofstetter, Jack Bijur, Claiborne Lucas, Andrew James, Ricardo-Torres Guzman, Andrea Hiller, Noah Alter, Amy Stone, Maria Powell, Matthew E. Pontell, Matthew E. Pontell

“…BackgroundHypernasality, a hallmark of velopharyngeal insufficiency (VPI), is a speech disorder with significant psychosocial and functional implications. Conventional diagnostic methods rely heavily on specialized expertise and equipment, posing challenges in resource-limited settings. …”
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MSSA: multi-stage semantic-aware neural network for binary code similarity detection by Bangrui Wan, Jianjun Zhou, Ying Wang, Feng Chen, Ying Qian

“…Binary code similarity detection (BCSD) aims to identify whether a pair of binary code snippets is similar, which is widely used for tasks such as malware analysis, patch analysis, and clone detection. …”
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DETECTIVE STORY: TO THE PROBLEM OF VARIABILITY OF THE MAIN EVENT AND CHARACTERS (BY THE CASE OF A. SARAKHOV’S STORIES) by I. A. KAZHAROVA

“…The functionality of stereotypes of perception and «memory of the genre» is briefly presented, which manifests itself in the history of understanding a domestic detective story as a constant appeal to the foreign sources of the genre. …”
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Employing SAE-GRU deep learning for scalable botnet detection in smart city infrastructure by Usman Tariq, Tariq Ahamed Ahanger

“…These findings enhance the understanding of IoT security by offering a scalable and resource-efficient solution for botnet detection. The functional investigation establishes a foundation for future research into adaptive security mechanisms that address emerging threats and highlights the practical potential of advanced deep learning techniques in safeguarding next-generation smart city ecosystems.…”
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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection by Sheng Deng, Yaping Wan

“…To tackle the issues of small target sizes, missed detections, and false alarms in aerial drone imagery, alongside the constraints posed by limited hardware resources during model deployment, a streamlined object detection approach is proposed to enhance the performance of YOLOv8s. …”
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Quantifying the spatial impact of an invasive Acacia on ecosystem functioning using remote sensing by André Große-Stoltenberg, Christiane Werner, Christine Hellmann, Jens Oldeland, Jan Thiele

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GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm by Xiangqiang Kong, Guangmin Liu, Yanchen Gao

“…Printed circuit boards (PCBs) are an indispensable part of electronic products, and their quality is crucial to the operational integrity and functional reliability of these products. Currently, existing PCB defect detection models are beset with issues such as excessive model size and parameter complexity, rendering them ill-equipped to meet the requirements for lightweight deployment on mobile devices. …”
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Study on Point Spread Function of Perovskite Fast Neutron Scintillation Imaging Screen by FENG Zhelin1, LIU Linyue2, , SONG Yan2, DUAN Baojun2, BAO Zizhen3, OUYANG Xiaoping2

“…Additionally, the limited availability of experimental machines for fast neutron imaging and the high cost of imaging systems hinders the efficient detection of large number of materials by using common fast neutron sources. …”

A powerful molecular marker to detect mutations at sorghum LOW GERMINATION STIMULANT 1 by Adedayo O. Adeyanju, Patrick J. Rich, Gebisa Ejeta

“…The LGS1 marker is useful for both detecting sources of lgs1 and introgressing Striga resistance into new genetic backgrounds.…”
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Specific detection of tartaric acid chiral isomers based on centrosymmetric terahertz metamaterial sensors by Xujun Xu, Zhen Sun, Guocui Liu, Jianjun Liu, Yong Du

“…Traditional detection methods, such as fluorescence detection, have problems such as destructive and non-specific characters. …”
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RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images by Yansong Wang, Xuanxi Yang, Haoyu Wang, Huihua Wang, Zaiqing Chen, Lijun Yun

“…Furthermore, to optimize the detection performance under hardware resource constraints, we apply knowledge distillation to RSWD-YOLO, thereby further improving the detection accuracy. …”
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The Relevance of Osteoscintigraphy Technique in Early Detection of Bone Metastatic Lesions: a Systematic Review by E. A. Litvinenko, I. V. Burova

“…OSG is an effective and informative technique for early detection of bone metastases, allowing to assess the functional state of the tumor and its surrounding tissues, even before the appearance of structural disorders visible by other diagnostic methods. …”
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Automatic detection of human gait events: a simple but versatile 3D algorithm by Théo Vancanneyt, Camille Le Moal, Maxence Blard, Juliette Lenoir, Nicolas Roche, Céline Bonnyaud, Fabien Dubois

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A lightweight UAV target detection algorithm based on improved YOLOv8s model by Fubao Ma, Ran Zhang, Bowen Zhu, Xirui Yang

“…Furthermore, the original loss function is replaced with SIoU to enhance detection accuracy. …”
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Soil Moisture Time Series Using Gamma‐Ray Spectrometry Detection Representing a Scale of Tens‐of‐Meters by Mie Andreasen, Steven Van derVeeke, Han Limburg, Ronald Koomans, Majken Caroline Looms

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YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

“…Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. …”
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Research on underwater disease target detection method of inland waterway based on deep learning by Tao Yu, Yu Xie, Jinsong Luo, Wei Zhu, Jie Liu

“…Abstract Aiming at the problems of low detection accuracy and poor generalization ability of underwater disease targets in inland waterways, an underwater disease target detection algorithm for inland waterways based on improved YOLOv5 is designed, which is denoted as YOLOv5-GBCE. …”
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