Infrastructure as Code for Cybersecurity Training by Rui Pinto, Rolando Martins, Carlos Novo

“…An approach closely related to virtualization and containerization as the code's underlying infrastructure helps lay down this burden. …”
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Optimizing Pre-Trained Code Embeddings With Triplet Loss for Code Smell Detection by Ali Nizam, Ertugrul Islamoglu, Omer Kerem Adali, Musa Aydin

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Adversarial training driven malicious code detection enhancement method by Yanhua LIU, Jiaqi LI, Zhengui OU, Xiaoling GAO, Ximeng LIU, Weizhi MENG, Baoxu LIU

“…To solve the deficiency of the malicious code detector’s ability to detect adversarial input, an adversarial training driven malicious code detection enhancement method was proposed.Firstly, the applications were preprocessed by a decompiler tool to extract API call features and map them into binary feature vectors.Secondly, the Wasserstein generative adversarial network was introduced to build a benign sample library to provide a richer combination of perturbations for malicious sample evasion detectors.Then, a perturbation reduction algorithm based on logarithmic backtracking was proposed.The benign samples were added to the malicious code in the form of perturbations, and the added benign perturbations were culled dichotomously to reduce the number of perturbations with fewer queries.Finally, the adversarial malicious code samples were marked as malicious and the detector was retrained to improve its accuracy and robustness of the detector.The experimental results show that the generated malicious code adversarial samples can evade the detector well.Additionally, the adversarial training increases the target detector’s accuracy and robustness.…”
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An Automatic Generation Method of Train Network Equipment Code Based on Configurable Hardware by XIONG Yan, ZHANG Bao, YANG Feng, HUANG He, LIN Lei

“…For this reason, this paper proposed a method of automatic code generation, and designed a set of automatic code generation tools based on this method to realize the automatic code generation. …”
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Radial Basis Neural Network Construction and Training for Telegraph-Code Structure Reception by D. A. Chistoprudov, V. A. Kozlov, M. R. Bibarsov, D. A. Potyagov, N. Ya. Karasik

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Successful Handling of Patient Complaints Requires Effective Training of Specialists: A Description of the CODE Training Model for Patient Complaint Excellence by Richard M. Elias MBBS, MPH, Jennifer R. Bond MSHCA, Brooke L. Werneburg MA, Sheila K. Stevens MSW, Jonathan A. Leighton MD

“…This paper outlines the development and implementation of a novel training program, termed CODE (Compassion, Operational Support, De-escalation, Empowerment), for patient experience complaint management staff. …”
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A constructivist approach towards shaping coding skills in teachers by Katarzyna ZIĘBAKOWSKA-CECOT

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Effect of Noises and GSM Codings on Pre-Trained Speaker Embedding Models in Forensic Voice Comparison by Mohammed Hamzah Alsalihi, David Sztaho

“…Results show that environmental noise and GSM coding significantly degrade speaker verification performance. …”
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More than just training: rethinking the paediatric code cart design for better emergency care outcomes by Katherine Innis, Jillian Lewis, Russell B. Pierre, Simone Dundas Byles, Oluwayomi Olugbuyi

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Coding for Young Learners: Enhancing Computational Thinking and Creativity in Elementary Education by Hilmiyah, Abd Muhith, Syamsul Bahri

Subjects: “…coding education, computational thinking, elementary education, teacher training, digital learning…”
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DS4NN: Direct training of deep spiking neural networks with single spike-based temporal coding by Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh

“…Backpropagation is the foremost prevalent and common algorithm for training conventional neural networks with deep construction. …”
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Validation of group process assessment for youth who misuse substances: Group level coding by Anthony Coetzer-Liversage, Aradhana Srinagesh, Shayna Bassett, Manshu Yang, Mary Clair-Michaud, Rosemarie A. Martin, Damaris Rohsenow, Christopher Kahler, Peter Monti, Warren Hurlbut, L.A.R. Stein

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Neurons throughout the brain embed robust signatures of their anatomical location into spike trains by Gemechu Bekele Tolossa, Aidan M Schneider, Eva Dyer, Keith B Hengen

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Epigenetic Impacts of Non‐Coding Mutations Deciphered Through Pre‐Trained DNA Language Model at Single‐Cell Resolution by Zhe Liu, An Gu, Yihang Bao, Guan Ning Lin

“…Abstract DNA methylation plays a critical role in gene regulation, affecting cellular differentiation and disease progression, particularly in non‐coding regions. However, predicting the epigenetic consequences of non‐coding mutations at single‐cell resolution remains a challenge. …”
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A spiking photonic neural network of 40 000 neurons, trained with latency and rank-order coding for leveraging sparsity by Ria Talukder, Anas Skalli, Xavier Porte, Simon Thorpe, Daniel Brunner

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Fluctuation scaling in neural spike trains by Shinsuke Koyama, Ryota Kobayashi

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Cracking the code for maintaining quality training in Olympic distance triathlon: lessons learnt from a squad of elite Scandinavian athletes by Eleanor Boyle, Corrie Myburgh, Tobias Hansen, Anders Holm Beck

“…Objective High-quality training is a key determinant of performance in the Olympic distance triathlon and is potentially influenced by a unique array of context-specific biopsychosocial factors. …”
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Adapting to change: International maritime education and training for future seafarers – focusing on the comprehensive review of the STCW convention and code of the 10th session of the IMO HTW by Song-Yi Yi, Min Jung, Sang-Il Lee

“…The Sub-Committee on Human Element, Training and Watchkeeping (HTW) of the IMO is currently undertaking steps towards a comprehensive review of the STCW Convention and its Code, with completion targeted for 2025 and full adoption expected by 2027. …”
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Building Training Methodology: Preparing Invigilators for Active, In-person, Exam Management by Tammy L. Cameron, Adriana C. Salvia, Nazlin Z. Hirji

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Generative-assisted multi-stage integrated network: Tackling extreme noise in image denoisingThe source code and trained models are made publicly available at: by Taeyong Park, Muhammad Sohail Ibrahim, Minseok Kim, Zahyun Ku, Yunsang Kwak

“…These findings underscore the potential of GainNet for real-world applications in aerospace imaging, autonomous navigation, and medical diagnostics. The source code and trained models are made publicly available at https://github.com/AVIP-laboratory/Generative-assisted_multi-stage_integrated_network.…”
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