Hardcoded vulnerability detection approach for IoT device firmware by Chao MU, Xin WANG, Ming YANG, Heng ZHANG, Zhenya CHEN, Xiaoming WU

“…With the popularization of IoT devices, more and more valuable data is generated.Analyzing and mining big data based on IoT devices has become a hot topic in the academic and industrial circles in recent years.However, due to the lack of necessary detection and protection methods, many IoT devices have serious information security risks.In particular, device hard-coded information is closely related to system encryption and decryption, identity authentication and other functions, which can provide confidentiality protection for core data.Once this information is exploited by malicious attackers, serious consequences such as sensitive information leakage, backdoor attacks, and unauthorized logins will occur.In response to this problem, a multi-type character recognition and positioning scheme was designed and a hard-coded vulnerability detection method in executable files was proposed based on the study of the characteristics of hard-coded vulnerabilities in IoT devices.The proposed method extracted the firmware of IoT devices and filtered all executable files as the source to be analyzed.Then, a solution to identify and locate three types of hard-coded characters was provided.Further, the reachability of the function, where the hard-coded character was located, was analyzed according to the function call relationship.Meanwhile, the instruction heterogeneity was mitigated by an intermediate representation (IR) model.The character and parameter hard-coded values was obtained through a data flow analysis approach.A symbolic execution method was devised to determine the trigger conditions of the hard-coded vulnerabilities, and then the vulnerability detection result was output.On the one hand, the proposed method introduced the method of symbolic execution based on the use of the intermediate representation model, which eliminated the dependency of instruction architecture and reduces the false positive rate of vulnerabilities; On the other hand, this method can integrate characters, files, and cryptographic implementation to realize the different characteristics of three types of hard-coded characters, which increased the coverage of vulnerability detection and improves the versatility of the detection method.The experimental results show that the proposed method can effectively detect three types of hard-coded vulnerabilities of characters, files and cryptographic implementation in various IoT devices, and has good detection accuracy, which can provide certain guidance for the deployment of subsequent security protection technologies.…”
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Detection and tracking of mask wearing based on deep learning by Wang Lin, Nan Gaigai

“…Based on the YOLOv3 network, the spatial pyramid pooling structure is introduced into the detection module to realize the feature fusion at different scales, then the loss function is changed to CIoU loss to reduce the regression error improve detection accuracy, and provides good conditions for the subsequent tracking module. …”
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Weak Signal Detection Algorithm Based on Particle Filtering by Mao Senpeng, Wang Pengfei, Chen Wei, Guo Lei

“…Firstly, by modifying the likelihood ratio function, the influence of clutter on the detection probability is restricted. …”
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Mitochondrial respiratory function induces endogenous hypoxia. by Sara Prior, Ara Kim, Toshitada Yoshihara, Seiji Tobita, Toshiyuki Takeuchi, Masahiro Higuchi

“…Hypoxia influences many key biological functions. In cancer, it is generally believed that hypoxic condition is generated deep inside the tumor because of the lack of oxygen supply. …”
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Analysis of Bridge Health Detection Based on Data Fusion by Ying Chen, Jiuhong Zhang, Yanfeng Li, Jialong Li

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UAV-to-Ground Target Detection Based on YOLO-DSBE by Meng Pengshuai, Wang Feng, Zhai Weiguang, Ma Xingyu, Zhao Wei

“…The algorithm deeply integrates the BiFormer dynamic sparse attention mechanism into the improved feature fusion network, eliminating irrelevant feature information, enhancing the focus on pertinent details, and reducing the missed detection rate. Moreover, the EIoU boundary loss function is incorporated to address the ineffectiveness of the CIoU shape penalty term, enhancing model convergence speed and detection accuracy. …”
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Lightweight wavelet-CNN tea leaf disease detection. by Jing Yang, GaoJian Xu, MengDao Yang, ZhengPei Lin

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Robust Corner Detection Using Local Extrema Differences by Reza Yazdi, Hassan Khotanlou, Hosna Khademfar

“…It achieves better accuracy in corner localization and reduces both missed detections and false positives. …”
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Adaptive weight optimization with large pretraining for pest detection by Kejian Yu, Wenwen Xu, Fuqin Geng, Yunzhi Wu

“…Frequent infestations by agricultural pests reduce crop production and significantly affect economic efficiency. …”
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Improved YOLO for long range detection of small drones by Sicheng Zhou, Lei Yang, Huiting Liu, Chongqin Zhou, Jiacheng Liu, Yang Wang, Shuai Zhao, Keyi Wang

“…Inspired by ARM CPU efficiency optimizations, the model uses depthwise separable convolutions and efficient activation functions to reduce parameter size. The neck structure is enhanced with a collaborative attention mechanism and multi-scale fusion, improving feature representation. …”
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Social functioning of elderly persons with malignant diseases by Berat Svetlana, Nešković-Konstantinović Zora, Nedović Goran, Rapaić Dragan, Marinković Dragan

“…Malignant disease, its treatment and consequences of treatment can often lead to social marginalization and reduced quality of life. The aim of this research was to determine how elderly patients with malignant diseases function in their social environment. …”
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Deep learning-enhanced signal detection for communication systems. by Yang Liu, Peng Liu, Yu Shi, Xue Hao

“…Overall, the research model has achieved significant results in improving detection accuracy, optimizing real-time performance, and reducing resource consumption. …”
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GE-YOLO for Weed Detection in Rice Paddy Fields by Zimeng Chen, Baifan Chen, Yi Huang, Zeshun Zhou

“…It introduces the Neck network with the Gold-YOLO feature aggregation and distribution network to enhance the network’s ability to fuse multi-scale features and detect weeds of different sizes. Additionally, an EMA attention mechanism is used to better learn weed feature representations, while a GIOU loss function provides smoother gradients and reduces computational complexity. …”
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Spatial Shape-Aware Network for Elongated Target Detection by Shaowen Xu, Der-Horng Lee

“…In remote sensing detection, targets often exhibit unique characteristics such as elongated shapes, multi-directional rotations, and significant scale variations. …”
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Colorectal Cancer Detection Tool Developed with Neural Networks by Alex Ede Danku, Eva Henrietta Dulf, Alexandru George Berciu, Noemi Lorenzovici, Teodora Mocan

“…The GUI is composed of two panels, with the best ANN model and the best CNN model being utilized in each. The primary function of the tool is to assist medical personnel in reducing the time required to make decisions and the probability of misdiagnosis.…”
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Image Processing for Laser Impact Detection in Shooting Simulators by José Antonio García Torres, Daniel Guzmán Pérez, Jhon Fredy Rincón Morantes, Daniel Felipe Molina Martínez, Cristian Camilo García Rodríguez, Jhonnatan Eduardo Zamudio Palacios

“…Simulation systems play a crucial role in firearms training by offering advantages such as the progressive improvement of shooting skills, reduced logistical costs, ammunition savings, and decreased need for personnel deployment to shooting ranges. …”
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Optimized spectral indices for camouflage detection in multispectral imagery by Tobias Hupel, Peter Stütz

“…Given the heavy use and utility of such indices, this work introduces a novel generalization of the normalized difference, ratio and difference indices, the linear ratio index (LRI), a ratio of two linear functions of all available bands. In addition, an optimization approach for the LRI is presented, which incorporates complexity reduction strategies that enable optimization using only a subset of all available bands and reduced parameter precision, thereby ensuring parameter readability. …”
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Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging. by Doaa Mousa, Nourhan Zayed, Inas A Yassine

“…Alzheimer's disease (AD) affects the quality of life as it causes; memory loss, difficulty in thinking, learning, and performing familiar tasks. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate and analyze different brain regions for AD identification. …”
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Research on pedestrian detection technology for mining unmanned vehicles by ZHOU Libing, YU Zhengqian, WEI Jianjian, JIANG Xueli, YE Baisong, ZHAO Yexin, YANG Siliang

“…This algorithm decomposed low-light images from the RGB color space into the HSV color space, applied a Logarithm function to enhance the V component, and employed a bilateral filter to reduce noise. …”
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Effects of acute sleep deprivation on the brain function of individuals with migraine: a resting-state functional magnetic resonance imaging study by Shuqing Wang, Longteng Ma, Song Wang, Caohui Duan, Xinyu Wang, Xiangbing Bian, Deqi Zhai, Yin Sun, Siyuan Xie, Shuhua Zhang, Yingyuan Liu, Xiaoxue Lin, Ruobing Wang, Xiu Liu, Shengyuan Yu, Xin Lou, Zhao Dong

“…Conclusions In comparison to HCs, MWoA individuals exhibited significant alterations in brain function after ASD, particularly within the thalamus, and MWoA individuals exhibited a reduced within-network FC in DAN after ASD compared to baseline. …”
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