A Linguistic Features-Based Approach for the Functional Analysis of Disinformation in Spanish by Eduardo Puraivan, Fabian Riquelme, Rene Venegas

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Features of socio-psychological adaptation of young people with emotional disorders to the conditions of professional education by Olga V. Kalashnikova

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FeTT: Class-Incremental Learning with Feature Transformation Tuning by Sunyuan Qiang, Yanyan Liang

“…Then, we propose the feature transformation tuning (FeTT) model, which concurrently alleviates the inadequacy of previous PTM-based CIL in terms of stability and plasticity. …”
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Transforming 3D MRI to 2D Feature Maps Using Pre-Trained Models for Diagnosis of Attention Deficit Hyperactivity Disorder by Elahe Hosseini, Seyyed Ali Hosseini, Stijn Servaes, Brandon Hall, Pedro Rosa-Neto, Ali-Reza Moradi, Ajay Kumar, Mir Mohsen Pedram, Sanjeev Chawla

“…<b>Methods:</b> Leveraging the ADHD200 dataset, which encompasses demographic information and anatomical MRI scans collected from a diverse ADHD population, our study focused on developing modern deep learning-based diagnostic models. The data preprocessing employed a pre-trained Visual Geometry Group16 (VGG16) network to extract two-dimensional (2D) feature maps from three-dimensional (3D) anatomical MRI data to reduce computational complexity and enhance diagnostic power. …”
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A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity by Ryan L'Abbate, Anthony D'Onofrio, Samuel Stein, Samuel Yen-Chi Chen, Ang Li, Pin-Yu Chen, Juntao Chen, Ying Mao

“…On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. …”
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Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media by Imran Shafi, Sadia Din, Zahid Hussain, Imran Ashraf, Gyu Sang Choi

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A Real-Time Road Scene Semantic Segmentation Model Based on Spatial Context Learning by Xiaomei Xiao, Jialiang Tang, Xiaoyan Lu, Zhengyong Feng, Yi Li

“…During training, a multi-scale strategy is used to group semantic regions, and a Channel Aggregation Block (CAB) is designed to dynamically capture semantic groups through a mechanism of feature separation and fusion, thereby aggregating multi-level pixel features to generate the final segmentation results. …”
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The features of changes in orientations to employment among bachelor’s graduates by A. V. Merenkov, D. G. Sandler, V. S. Shavrin

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Unsupervised Visual-to-Geometric Feature Reconstruction for Vision-Based Industrial Anomaly Detection by Dinh-Cuong Hoang, Phan Xuan Tan, Anh-Nhat Nguyen, Duc-Thanh Tran, Van-Hiep Duong, Anh-Truong Mai, Duc-Long Pham, Khanh-Toan Phan, Minh-Quang Do, Ta Huu Anh Duong, Tuan-Minh Huynh, Son-Anh Bui, Duc-Manh Nguyen, Viet-Anh Trinh, Khanh-Duong Tran, Thu-Uyen Nguyen

“…Specifically, we use pre-trained 2D and 3D models to extract visual features from color images and geometric features from 3D point clouds. …”
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Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection by Zhen Gao, Xiaowen Chen, Jingning Xu, Rongjie Yu, Heng Zhang, Jinqiu Yang

“…Experiments show that the CLIP pre-trained model more accurately extracts facial and behavioral features from driver video frames, improving the model’s AUC by 7% over the ImageNet-based pre-trained model. …”
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E-LEARNING TRAINING USING EDMODO BASED ON TEACHERS OF SMK NU UNGARAN by Subiyanto Subiyanto, Iin Supriyati, Nisaun Markamah

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Fault Diagnosis of Gearbox Bearings of High-speed Train Based on the SVD-MOMEDA by Dan ZHU, Yanchen SU, Chunguang YAN

“…Aiming at problems of high-speed train gearbox bearing fault signals being difficult to detect under strong noise background, and the problem that the multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) method was affected by the order of filter and the period of impulse signal, an improved MOMEDA method for bearing fault diagnosis based on singular value decomposition(SVD) was proposed. …”
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Research on Automatic Train Operation System Based on Fuzzy Adaptive PID Algorithm by ZHOU Ruilin, LEI Chengjian, LIU Ze, SU Huiliang

“…The traditional PID algorithm used in the currently existing automatic train operation system is limited due to fixed parameters, making it difficult to achieve excellent control effects in actual operation scenes featuring strong coupling and high nonlinearity, which is mainly attributed to difficulties in overcoming nonlinear disturbances.In light of this, this paper proposes an automatic train operation approach that relies on a fuzzy adaptive PID algorithm, which can adjust the PID parameters in real time according to the preset fuzzy rules, thus improving the PID controller's performance in speed tracking and leading to an improved train control effect. …”
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Research on license plate recognition based on graphically supervised signal-assisted training by Dianwei Chi, Zehao Jia, Lizhen Liu

“…An auxiliary training branch is added, utilizing these graphical signals to guide the model in learning improved image features. …”
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ATBShellFinder: A Bytecode-Level Webshell Detector Based on Adversarial Training by Yuqin Xie, Yuan Zhang, Daofeng Li, Guoren Xiong

“…To address these challenges, this study proposes ATBShellFinder, an enhanced detection framework based on adversarial training. ATBShellFinder applies adversarial training techniques from computer vision to the embedding layer of the Bidirectional Encoder Representations from Transformers (BERT) to generate adversarial word embeddings. …”
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Development of IoT-based pulse rate detection bracelet for volleyball endurance training by Nur Ahmad Muharram, Budiman Agung Pratama, Pungky Indarto

“…Objective: This research aims to develop an IoT-based pulse rate detection bracelet designed specifically for endurance training in volleyball. …”
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Graph-based vision transformer with sparsity for training on small datasets from scratch by Peng Li, Lu Huang, Jin Li, Haiyan Yan, Dongjing Shan

“…To overcome this low-rank bottleneck in attention heads, we employ talking-heads technology based on bilinear pooled features and sparse selection of attention tensors. …”
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Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors by Yaojung Shiao, Premkumar Gadde, Chun-Yu Liu

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A Lightweight Single-Image Super-Resolution Method Based on the Parallel Connection of Convolution and Swin Transformer Blocks by Tengyun Jing, Cuiyin Liu, Yuanshuai Chen

“…To address these problems and better leverage both local and global information, this paper proposes a super-resolution reconstruction network based on the Parallel Connection of Convolution and Swin Transformer Block (PCCSTB) to model the local and global features of an image. …”
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Cross-Domain Feature Enhancement-Based Password Guessing Method for Small Samples by Cheng Liu, Junrong Li, Xiheng Liu, Bo Li, Mengsu Hou, Wei Yu, Yujun Li, Wenjun Liu

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