Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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From Code to Ratings: Converting Programming Data to Enhance Collaborative Filtering in Educational Online Judge Systems by Daniel M. Muepu, Yutaka Watanobe

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Relating sparse and predictive coding to divisive normalization. by Yanbo Lian, Anthony N Burkitt

“…In this paper, we show how sparse coding, predictive coding, and divisive normalization can be described within a unified framework, and illustrate this explicitly within the context of a two-layer neural learning model of sparse/predictive coding. …”
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Scale Is Not All You Need: Revisiting the Biomimetic Roots of Deep Learning to Overcome Fundamental Limitations by Malek Wahidi, Anthony Rizk, Rodrigue Imad

“…Specifically, we explore the emerging topics of Liquid Neural Networks, recurrent vision architectures, and Predictive Coding as representative examples of such solutions. …”
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Dance as mindful movement: a perspective from motor learning and predictive coding by W. Tecumseh Fitch, Rebecca Barnstaple

“…However, to make this conception explicit and testable, we need an empirically verifiable characterization of “mindful movement.” …”
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Long short‐term memory on abstract syntax tree for SQL injection detection by Z. Zhuo, T. Cai, X. Zhang, F. Lv

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Disentangling signal and noise in neural responses through generative modeling. by Kendrick Kay, Jacob S Prince, Thomas Gebhart, Greta Tuckute, Jingyang Zhou, Thomas Naselaris, Heiko H Schütt

“…We end by discussing other situations that may benefit from GSN's characterization of signal and noise, such as estimation of noise ceilings for computational models of neural activity. A code toolbox for GSN is provided with both MATLAB and Python implementations.…”
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Secondary-Structure-Informed RNA Inverse Design via Relational Graph Neural Networks by Amirhossein Manzourolajdad, Mohammad Mohebbi

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Regression analysis and artificial neural networks for predicting pine species volume in community forests by Wenceslao Santiago-García

“…These variables were explicitly incorporated into the ANN structure, enabling the model to learn complex, non-linear interactions. …”
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Architecture-aware minimization (A2M): how to find flat minima in neural architecture search by Matteo Gambella, Fabrizio Pittorino, Manuel Roveri

“…Neural architecture search (NAS) has become an essential tool for designing effective and efficient neural networks. …”
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A neural network-based method for input parameter optimization of edge transport modeling utilizing experimental diagnostics by Y. Luo, S. Xu, Y. Liang, E. Wang, J. Cai, Y. Feng, D. Reiter, A. Knieps, S. Brezinsek, D. Harting, M. Krychowiak, D. Gradic, P. Ren, D. Zhang, Y. Gao, G. Fuchert, A. Pandey, M. Jakubowski, the W7-X Team

“…EMC3-EIRENE simulations using the maximum a posteriori estimates derived from these posterior distributions reproduce experimental measurements with satisfactory accuracy. This neural network-based method significantly reduces computational costs and the need for manual parameter tuning, and it can be generalized to other similar modeling codes.…”
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Modelling the future of cleaner energy: Explainable artificial intelligence model for green hydrogen production rate estimation by Okorie Ekwe Agwu, Saad Alatefi, Ahmad Alkouh

“…Overall, the model can be used in two modes: online, by integrating it into software programs, and offline, by simply entering parameter values into the explicit model without having to run long lines of code.…”
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A Semantically Enhanced Label Prediction Method for Imbalanced POI Data Category Distribution by Hongwei Zhang, Qingyun Du, Shuai Zhang, Renfei Yang

“…Furthermore, most neural network classification models implicitly learn the semantic knowledge of different categories from training datasets, neglecting the explicit semantic information offered by natural language labels. …”
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Suboptimal but intact integration of Bayesian components during perceptual decision-making in autism by Laurina Fazioli, Bat-Sheva Hadad, Rachel N. Denison, Amit Yashar

“…This hypothesis has gained momentum in recent years, partly because it can be implemented both at the computational level, as in Bayesian perception, and at the level of canonical neural microcircuitry, as in predictive coding. However, empirical investigations have yielded conflicting results with evidence remaining limited. …”
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Visuospatial sequence learning without seeing. by Clive R Rosenthal, Christopher Kennard, David Soto

“…Learning sequential associations between non-adjacent visual stimuli (higher-order visuospatial dependencies) can occur either with or without awareness (explicit vs. implicit learning) of the products of learning. …”
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FMRI reveals a dissociation between grasping and perceiving the size of real 3D objects. by Cristiana Cavina-Pratesi, Melvyn A Goodale, Jody C Culham

“…The demonstration of dual coding of an object for the purposes of action on the one hand and perception on the other in the same healthy brains offers a substantial contribution to the current debate about the nature of the neural coding that takes place in the dorsal and ventral streams.…”
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Generating diversity and securing completeness in algorithmic retrosynthesis by Florian Mrugalla, Christopher Franz, Yannic Alber, Georg Mogk, Martín Villalba, Thomas Mrziglod, Kevin Schewior

“…Scientific Contribution: We adapt Depth-First Proof-Number Search (DFPN) (Please refer to https://github.com/Bayer-Group/bayer-retrosynthesis-search for the accompanying source code.) and its variants, which have been applied to retrosynthesis before, to produce a set of solutions, with an explicit focus on diversity. …”
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Large language models for depression recognition in spoken language integrating psychological knowledge by Yupei Li, Shuaijie Shao, Manuel Milling, Manuel Milling, Björn W. Schuller, Björn W. Schuller, Björn W. Schuller, Björn W. Schuller

“…Depression is a growing concern gaining attention in both public discourse and AI research. While deep neural networks (DNNs) have been used for its recognition, they still lack real-world effectiveness. …”
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RTide: Automating the Tidal Response Method by Thomas Monahan, Tianning Tang, Stephen Roberts, Thomas A. A. Adcock

“…Our approach embeds a class of neural networks capable of representing any arbitrary Volterra series—the mathematical basis of the response method—within the classic framework. …”
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