Malicious code within model detection method based on model similarity by Degang WANG, Yi SUN, Chuanxin ZHOU, Qi GAO, Fan YANG

“…The privacy of user data in federated learning is mainly protected by exchanging model parameters instead of source data.However, federated learning still encounters many security challenges.Extensive research has been conducted to enhance model privacy and detect malicious model attacks.Nevertheless, the issue of risk-spreading through malicious code propagation during the frequent exchange of model data in the federated learning process has received limited attention.To address this issue, a method for detecting malicious code within models, based on model similarity, was proposed.By analyzing the iterative process of local and global models in federated learning, a model distance calculation method was introduced to quantify the similarity between models.Subsequently, the presence of a model carrying malicious code is detected based on the similarity between client models.Experimental results demonstrate the effectiveness of the proposed detection method.For a 178MB model containing 0.375MB embedded malicious code in a training set that is independent and identically distributed, the detection method achieves a true rate of 82.9% and a false positive rate of 1.8%.With 0.75MB of malicious code embedded in the model, the detection method achieves a true rate of 96.6% and a false positive rate of 0.38%.In the case of a non-independent and non-identically distributed training set, the accuracy of the detection method improves as the rate of malicious code embedding and the number of federated learning training rounds increase.Even when the malicious code is encrypted, the accuracy of the proposed detection method still achieves over 90%.In a multi-attacker scenario, the detection method maintains an accuracy of approximately 90% regardless of whether the number of attackers is known or unknown.…”
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Beyond the Code- Why Emergency Departments in Pakistan Need Education in Leadership by Saima Ali, Syed Ghazanfar Saleem

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Modulation recognition method based on multiscale convolutional fusion coding networks by LI Guojun, ZHU Siyuan, ZHENG Jianzhong, WANG Jie, YE Changrong

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A note on cohomology and algebraic geometric codes on the curves over rings by Nyamda Francis, Mouaha Christophe

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Using Low-Code Technology to Develop Tasks in an Interactive Testing System by Andrey Kraev, Svetlana Filippova

“…This speeds up work with typical tasks, eliminates repetitive actions, helps minimize the manual coding process and allows users with varying degrees of technical training to create the required solutions. …”
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Ten principles for reliable, efficient, and adaptable coding in psychology and cognitive neuroscience by Johannes Roth, Yunyan Duan, Florian P. Mahner, Philipp Kaniuth, Thomas S. A. Wallis, Martin N. Hebart

“…Despite its critical role, coding remains challenging for many researchers, as it is typically not part of formal academic training. …”
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A Review of the Accuracy of Crash Coding When Applied to Motorcycle Crashes by Liz de Rome, Christopher Hurren, Thomas Brandon

“…The coding of road crashes is designed to identify patterns of contributing factors for the development of countermeasures. …”
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Teachers’ preparedness for implementing the Educational Coding and Robotics curriculum in South Africa by William Zivanayi, Serah Ntombikayise Malinga

“…DBE needs to work closely with the teachers’ training institutions and pedagogical experts to meet the needs of teachers and learners regarding educational coding and robotics curriculum. …”
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Shear Strength Determination in RC Beams Using ANN Trained with Tabu Search Training Algorithm by Alireza Shahbazian, Hamidreza Rabiefar, Babak Aminnejad

“…The shear design equations of ACI-318-2019 were also investigated and compared with Tabu Search Trained ANN model. The analysis of results suggests the superiority of Tabu Search Trained ANNs in comparison to other suggested models in literature and the ACI-318-2019 design code.…”
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NCC—An Efficient Deep Learning Architecture for Non-Coding RNA Classification by Konstantinos Vasilas, Evangelos Makris, Christos Pavlatos, Ilias Maglogiannis

“…In this paper, an efficient deep-learning architecture is proposed, aiming to classify a significant category of RNA, the non-coding RNAs (ncRNAs). These RNAs participate in various biological processes and play an important role in gene regulation as well. …”
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Leveraging Synthetic Data for Improved Manipuri-English Code-Switched ASR by Naorem Karline Singh, Wangkheimayum Madal, Chingakham Neeta Devi, Hoomexsun Pangsatabam, Yambem Jina Chanu

“…Accurately recognizing code-switched speech presents a significant challenge in the field of Automatic speech recognition (ASR), particularly for low-resource regional languages. …”
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Opening the AI Black Box: Distilling Machine-Learned Algorithms into Code by Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelić, Max Tegmark

“…Can we turn AI black boxes into code? Although this mission sounds extremely challenging, we show that it is not entirely impossible by presenting a proof-of-concept method, MIPS, that can synthesize programs based on the automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. …”
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TEDVIL: Leveraging Transformer-Based Embeddings for Vulnerability Detection in Lifted Code by Gary A. McCully, John D. Hastings, Shengjie Xu

“…This research introduces TEDVIL (Transformer-based Embeddings for Discovering Vulnerabilities in Lifted Code), a novel framework which uses transformer-based embeddings to train neural networks to detect vulnerabilities in lifted code. …”
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A Study on Chatbot Development Using No-Code Platforms by People with Disabilities for Their Peers at a Sheltered Workshop by Sara Hamideh Kerdar, Britta Marleen Kirchhoff, Lars Adolph, Liane Bächler

“…No-code (NC) platforms empower individuals without IT experience to create tailored applications and websites. …”
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Distribution and Research of Loop Speed Codes under IATP Fallback Mode by YANGHai-peng, SONGYan

“…Aim to the present condition that signal system for urban rail transits project mostly was based on moving block train trafficcontrol system (CBTC) and appropriate fallback mode was taken into consideration for the CBTC systems in most cities, distributionprinciple of speed codes and speed codes changes in case of various failure modes under IATP fallback mode was studied with specificengineering practice, and detailed design was carried on for speed codes distribution scheme. …”
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Automated Assessment of Student Self-explanation During Source Code Comprehension by Jeevan Chapagain, Lasang Tamang, Rabin Banjade, Priti Oli, Vasile Rus

“…This paper presents a novel method to automatically assess self-explanations generated by students during code comprehension activities. The self-explanations are produced in the context of an online learning environment that asks students to freely explain Java code examples line-by-line. …”
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‘It’s bawdier in Greek’: A.C. Swinburne’s Subversions of the Hellenic Code by Charlotte Ribeyrol

“…This eloquent polyglossia was both an appropriate tribute to his classically-trained French poetic mentor, as well as a means for Swinburne to show off his multifaceted literary skills. …”
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Synaptic learning rules and sparse coding in a model sensory system. by Luca A Finelli, Seth Haney, Maxim Bazhenov, Mark Stopfer, Terrence J Sejnowski

“…The locust olfactory system, in which dense, transiently synchronized spike trains across ensembles of antenna lobe (AL) neurons are transformed into a sparse representation in the mushroom body (MB; a region associated with memory), provides a well-studied preparation for investigating the interaction of multiple coding mechanisms. …”
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Convolutional sparse coding network for sparse seismic time-frequency representation by Qiansheng Wei, Zishuai Li, Haonan Feng, Yueying Jiang, Yang Yang, Zhiguo Wang

“…To address this issue, we propose an interpretable convolutional sparse coding (CSC) network to achieve high TF resolution. …”
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INTERMODAL TRANSPORT AND STANDARDISATION by Marcel LITRA, Stefan IOVAN

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