Survey on automated vulnerability mining techniques for IoT device firmware by LIU Hangtian, GAN Shuitao, ZHANG Chao, ZHANG Hongqi, SUN Wenhou, GAO Zicong, ZHAO Min, BAI Xue

“…The security analysis of IoT device firmware has been conducted, with a focus on its black-box nature, network characteristics, and customization features. …”
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Investigating the diversity and stylization of contemporary user generated visual arts in the complexity entropy plane by Seunghwan Kim, Byunghwee Lee, Wonjae Lee

“…Informatizing 149,780 images curated on the DeviantArt and Bēhance platforms from 2010 to 2020, we analyze the relationship between local information in the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. …”
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NRGS-Net: A Lightweight Uformer with Gated Positional and Local Context Attention for Nighttime Road Glare Suppression by Ruoyu Yang, Huaixin Chen, Sijie Luo, Zhixi Wang

“…Additionally, channel attention is introduced within the Local Context-Aware Feed-Forward Network (LCA-FFN) to enable adaptive adjustment of feature weights, effectively suppressing irrelevant and interfering features. …”
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Regional dynamics and mechanisms behind SARS-CoV-2 XDV.1 prevalence in Chongqing via genomic surveillance and molecular insights by Jin Yan, Fangyuan Liu, Sihan Hu, Junyi Pan, Qi Yan, Lu Yao, Huhao Jin, Xiaofeng Chen, Jiuhong He

“…XDV.1 exhibits structural features suggestive of potential immune evasion mechanisms, including conformational shifts and novel hydrogen-bond networks that could interfere with antibody recognition. …”
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Automated Graphic Divergent Thinking Assessment: A Multimodal Machine Learning Approach by Hezhi Zhang, Hang Dong, Ying Wang, Xinyu Zhang, Fan Yu, Bailin Ren, Jianping Xu

“…The findings advance automated cognitive evaluation methodologies by demonstrating the complementary value of visual-textual feature fusion in creativity assessment.…”
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UNIFIED MULTIMODAL BIOMETRICS FUSION USING DEEP LEARNING FOR SECURING IOT by Prabhjot Kaur, Chander Kaur

“…This work taps into the expansive collection of face and iris images present in the WVU-Multimodal dataset for evaluation purposes. Our proposed approach employs “Convolutional Neural Network (CNN)” architectures, notable for their efficacy in computer vision tasks, to extract potent discriminative features from the input images. …”
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Coal and gas outburst prediction based on data augmentation and neuroevolution. by Wenbing Shi, Ji Huang, Gaoming Yang, Shuzhi Su, Shexiang Jiang

“…ANEAT realizes the high-precision mapping of feature parameters and outburst risk with a lightweight network architecture, which can be well applied to CGO prediction.…”
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Predicting Insemination Outcome in Holstein Dairy Cattle using Deep Learning by Mohammad Alishahi, Mahdi Ravakhah

“…In the problem of predicting the results of artificial insemination of livestock, the presented LSTM neural network model shows the best performance based on the stated evaluation criteria, and then the XGBoost-based classifier has better performance than MLP.…”
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Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas by Yue Hu, Xin Cao, Hongyi Chen, Daoying Geng, Kun Lv

“…The biological interpretability of selected features reflects distinct neuroplasticity patterns between LGGs and HGGs, advancing understanding of glioma-network interactions.…”
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Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting by Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Leen Al‐Omran, Duminda N. Wijeysundera, Mohamad A. Hussain, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Graham Roche‐Nagle, Mohammed Al‐Omran

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COMPARITATIVE ANALYIS OF IPVE & IPV6 INTENDED FOR LEARNING OBJECT REPOSITORY TO SETUP AN E-LEARNING ENVIRONMENT by Imran Ullah Khan

“…IPv6 not only overcomes the issue of depletion of network addresses but also provides various other features such as automation, scalability, security, and others such as multicasting etc. …”
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Machine learning based disruption prediction using long short-term memory in KSTAR by Jeongwon Lee, Jayhyun Kim, Jinsu Kim, Sang-hee Hahn, Hyunsun Han, Giwook Shin, Yong-Su Na, Yong Un Nam

“…The architecture combines a multi-input LSTM and a fully connected neural network, using 30 features sampled over a 1 s window. …”
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Acoustic Signal-Based Deep Learning Approach and Device for Detecting Interfacial Voids in Steel–Concrete Composite Structures by Wei Tian, Hang Yuan, Xinpeng You, Hehui Zheng, Bazhou Li, Chengming Peng

“…The reliable synergy between steel and concrete is an important evaluation criterion for the safety and long-term use of steel–concrete composite structures (SCCSs). …”
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Online Semisupervised Learning Approach for Quality Monitoring of Complex Manufacturing Process by Weng Weiwei, Mahardhika Pratama, Andri Ashfahani, Edward Yapp Kien Yee

“…This paper proposes Parsimonious Network++ (ParsNet++) as an online semisupervised learning approach being able to handle extreme label scarcity in the quality monitoring task. …”
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2D Instance-Guided Pseudo-LiDAR Point Cloud for Monocular 3D Object Detection by Rui Gao, Junoh Kim, Kyungeun Cho

“…A new feature extraction network was designed using the transformer module to extract pillar features. …”
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VDMNet: A Deep Learning Framework with Vessel Dynamic Convolution and Multi-Scale Fusion for Retinal Vessel Segmentation by Guiwen Xu, Tao Hu, Qinghua Zhang

“…Firstly, we introduce the Fast Multi-Head Self-Attention (FastMHSA) module to effectively capture both global and local features, enhancing the network’s robustness against complex backgrounds and pathological interference. …”
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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…The framework can be divided into four major processes: Network big data collection, intelligent evaluation of network big data, AIGC image fine-tuning model construction, and AI-aided design generation. 1) Network big data collection: Obtain datasets related to the required landscape architecture segmentation scenarios through online social platforms for evaluation and fine-tuning model training. 2) Intelligent evaluation of network big data: Analyze and categorize image data, and filter out the scenario images with excellent user evaluation based on text sentiment evaluation and subsidiary information analysis. 3) AIGC image fine-tuning model construction: Utilize the high-quality image dataset obtained in the previous stage to conduct fine-tuning model training based on a mature pre-trained general model, and inject relevant knowledge and experience from the sub-scenarios of landscape architecture in a cost-effective manner, thereby enhancing the model’s generative capabilities. 4) AI-aided design generation: Employ the fine-tuning model obtained through training to assist in generating scenario images according to the needs of design practice, and based on the intensity of control over the generated content, divide the aided scenario generation into “weakly controlled” and “strongly controlled” aided design scenarios. …”
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A Simple Predictive Enhancer Syntax for Hindbrain Patterning Is Conserved in Vertebrate Genomes. by Joseph Grice, Boris Noyvert, Laura Doglio, Greg Elgar

“…<h4>Background</h4>Determining the function of regulatory elements is fundamental for our understanding of development, disease and evolution. However, the sequence features that mediate these functions are often unclear and the prediction of tissue-specific expression patterns from sequence alone is non-trivial. …”
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FREEDOM FRANCHISING AS AN ALTERNATIVE TO THE CLASSIC FRANCHISING by L. A. Solovova

“…The aim of the article is to systemize and enrich the knowledge in the sphere of the franchising model evolution. The author’s task was to identify the key features of the freedom franchising model, to compare the freedom franchising with classic franchising and to formulate the conditions under which the freedom franchising model can be developed. …”
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Underwater Image Enhancement Based on Transformer, Attention, and Multi-Color-Space Inputs by Liqiong Lu, Dong Wu, Liuyin Wang, Wanzhen Zhang, Tonglai Liu

“…First, transformer block is embedded into ResNet-50 as the backbone network. Combining this backbone network and multi-scale feature fusion forms the fundamental framework of our method. …”
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