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Journal articles on the topic 'Code-based masking'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Tian, Qi, Hao Cheng, Chun Guo, Daniel Page, Meiqin Wang, and Weija Wang. "A Code-Based ISE to Protect Boolean Masking in Software." IACR Transactions on Cryptographic Hardware and Embedded Systems 2025, no. 2 (2025): 293–332. https://doi.org/10.46586/tches.v2025.i2.293-332.

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Side-Channel Attacks (SCAs) pose a significant threat to data security in embedded environments. To counteract the power-based SCAs, masking is a widely used defense technique, that introduces randomness to obscure the sidechannel information generated during the processing of secret data. However, in practice, some challenges exist when implementing masking schemes. For example, in the implementation of Boolean masking, they may refer to low noise level and implementation flaws. To address the said implementation challenges, we present an effective and efficient solution that incorporates the

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Gaspoz, John, and Siemen Dhooghe. "Code-based Masking: From Fields to Bits Bitsliced Higher-Order Masked SKINNY." IACR Transactions on Cryptographic Hardware and Embedded Systems 2025, no. 3 (2025): 212–37. https://doi.org/10.46586/tches.v2025.i3.212-237.

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Masking is one of the most prevalent and investigated countermeasures against side-channel analysis. As an alternative to the simple (e.g., additive) encoding function of Boolean masking, a collection of more algebraically complex masking types has emerged. Recently, inner product masking and the more generic codebased masking have proven to enable higher theoretical security properties than Boolean masking. In CARDIS 2017, Poussier et al. connected this “security order amplification” effect to the bit-probing model, demonstrating that for the same shared size, sharings from more complex encod

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3

Xiao, Yisheng, Ruiyang Xu, Lijun Wu, et al. "AMOM: Adaptive Masking over Masking for Conditional Masked Language Model." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13789–97. http://dx.doi.org/10.1609/aaai.v37i11.26615.

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Transformer-based autoregressive (AR) methods have achieved appealing performance for varied sequence-to-sequence generation tasks, e.g., neural machine translation, summarization, and code generation, but suffer from low inference efficiency. To speed up the inference stage, many non-autoregressive (NAR) strategies have been proposed in the past few years. Among them, the conditional masked language model (CMLM) is one of the most versatile frameworks, as it can support many different sequence generation scenarios and achieve very competitive performance on these tasks. In this paper, we furt

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Carlet, Claude, Abderrahman Daif, Sylvain Guilley, and Cédric Tavernier. "Quasi-linear masking against SCA and FIA, with cost amortization." IACR Transactions on Cryptographic Hardware and Embedded Systems 2024, no. 1 (2023): 398–432. http://dx.doi.org/10.46586/tches.v2024.i1.398-432.

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The implementation of cryptographic algorithms must be protected against physical attacks. Side-channel and fault injection analyses are two prominent such implementation-level attacks. Protections against either do exist. Against sidechannel attacks, they are characterized by SNI security orders: the higher the order, the more difficult the attack.In this paper, we leverage fast discrete Fourier transform to reduce the complexity of high-order masking. The security paradigm is that of code-based masking. Coding theory is amenable both to mask material at a prescribed order, by mixing the info

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Usman, Usman Bukar, Kabir Umar, and Aliyu Isah Agaie. "CodeELECTRA: An ELECTRA-based approach for improved vulnerability detection in blockchain smart contracts." Dutse Journal of Pure and Applied Sciences 10, no. 3b (2024): 95–105. http://dx.doi.org/10.4314/dujopas.v10i3b.11.

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Blockchain technology has gained significant traction due to its core features of immutability, transparency, and decentralization. Smart contracts, self-executing programs stored on blockchains, play a vital role in enabling secure and automated transactions. Secure and automated transactions are made possible by self-executing programs and smart contracts that are kept on blockchains. The rapid progress of blockchain technology has been linked to an increase in security concerns targeting smart contracts. In comparison to traditional approaches, deep learning and transformer-based approaches

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Levina, Alla, and Gleb Ryaskin. "Robust Code Constructions Based on Bent Functions and Spline Wavelet Decomposition." Mathematics 10, no. 18 (2022): 3305. http://dx.doi.org/10.3390/math10183305.

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The paper investigates new robust code constructions based on bent functions and spline–wavelet transformation. Implementation of bent functions in code construction increases the probability of error detection in the data channel and cryptographic devices. Meanwhile, the use of spline wavelet theory for constructing the codes gives the possibility to increase system security from the actions of an attacker. Presented constructions combine spline-wavelets functions and bent functions. Developed robust codes, compared to existing ones, have a higher parameter of the maximum error masking probab

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Mehboob, R., S. Shabbir, and A. Javed. "A Technique of Code Clone Detection based on Defined Mechanism for Threshold Calculation." Nucleus 54, no. 4 (2018): 197–204. https://doi.org/10.71330/thenucleus.2017.258.

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Over the past few years the revolution in the technology and use of programming languages for product development has made code reusability a common practice. Consequently the problem of code cloning is also increasing leading to redundancy and increased maintenance cost. The real motivation of the proposed research work is to identify code clones from pair of codes that are going to be utilized for a project under consideration. Existing practices such as control flow graphs (CFGs) and abstract syntax tree (AST) promote a high level of abstraction by masking the inner details of the code. The

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Wang, Weijia, Yu Yu, and Francois-Xavier Standaert. "Provable Order Amplification for Code-Based Masking: How to Avoid Non-Linear Leakages Due to Masked Operations." IEEE Transactions on Information Forensics and Security 14, no. 11 (2019): 3069–82. http://dx.doi.org/10.1109/tifs.2019.2912549.

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Goy, Guillaume, Julien Maillard, Philippe Gaborit, and Antoine Loiseau. "Single trace HQC shared key recovery with SASCA." IACR Transactions on Cryptographic Hardware and Embedded Systems 2024, no. 2 (2024): 64–87. http://dx.doi.org/10.46586/tches.v2024.i2.64-87.

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This paper presents practicable single trace attacks against the Hamming Quasi-Cyclic (HQC) Key Encapsulation Mechanism. These attacks are the first Soft Analytical Side-Channel Attacks (SASCA) against code-based cryptography. We mount SASCA based on Belief Propagation (BP) on several steps of HQC’s decapsulation process. Firstly, we target the Reed-Solomon (RS) decoder involved in the HQC publicly known code. We perform simulated attacks under Hamming weight leakage model, and reach excellent accuracies (superior to 0.9) up to a high noise level (σ = 3), thanks to a re-decoding strategy. In a

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10

Yao, Xincheng, Chongyang Zhang, Ruoqi Li, Jun Sun, and Zhenyu Liu. "One-for-All: Proposal Masked Cross-Class Anomaly Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4792–800. http://dx.doi.org/10.1609/aaai.v37i4.25604.

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One of the most challenges for anomaly detection (AD) is how to learn one unified and generalizable model to adapt to multi-class especially cross-class settings: the model is trained with normal samples from seen classes with the objective to detect anomalies from both seen and unseen classes. In this work, we propose a novel Proposal Masked Anomaly Detection (PMAD) approach for such challenging multi- and cross-class anomaly detection. The proposed PMAD can be adapted to seen and unseen classes by two key designs: MAE-based patch-level reconstruction and prototype-guided proposal masking. Fi

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Burke, Colin J., Patrick D. Aleo, Yu-Ching Chen, et al. "Deblending and classifying astronomical sources with Mask R-CNN deep learning." Monthly Notices of the Royal Astronomical Society 490, no. 3 (2019): 3952–65. http://dx.doi.org/10.1093/mnras/stz2845.

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ABSTRACT We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent r

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Zeitschner, Jannik, and Amir Moradi. "PoMMES: Prevention of Micro-architectural Leakages in Masked Embedded Software." IACR Transactions on Cryptographic Hardware and Embedded Systems 2024, no. 3 (2024): 342–76. http://dx.doi.org/10.46586/tches.v2024.i3.342-376.

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Software solutions to address computational challenges are ubiquitous in our daily lives. One specific application area where software is often used is in embedded systems, which, like other digital electronic devices, are vulnerable to side-channel analysis attacks. Although masking is the most common countermeasure and provides a solid theoretical foundation for ensuring security, recent research has revealed a crucial gap between theoretical and real-world security. This shortcoming stems from the micro-architectural effects of the underlying micro-processor. Common security models used to

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Zhu, Fengmin, Michael Sammler, Rodolphe Lepigre, Derek Dreyer, and Deepak Garg. "BFF: foundational and automated verification of bitfield-manipulating programs." Proceedings of the ACM on Programming Languages 6, OOPSLA2 (2022): 1613–38. http://dx.doi.org/10.1145/3563345.

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Low-level systems code often needs to interact with data, such as page table entries or network packet headers, in which multiple pieces of information are packaged together as bitfield components of a single machine integer and accessed via bitfield manipulations (e.g., shifts and masking). Most existing approaches to verifying such code employ SMT solvers, instantiated with theories for bit vector reasoning: these provide a powerful hammer, but also significantly increase the trusted computing base of the verification toolchain. In this work, we propose an alternative approach to the verific

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Chen, Ying, Rebekah Wu, James Felton, David M. Rocke, and Anu Chakicherla. "A Method to Detect Differential Gene Expression in Cross-Species Hybridization Experiments at Gene and Probe Level." Biomedical Informatics Insights 3 (January 2010): BII.S3846. http://dx.doi.org/10.4137/bii.s3846.

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Motivation Whole genome microarrays are increasingly becoming the method of choice to study responses in model organisms to disease, stressors or other stimuli. However, whole genome sequences are available for only some model organisms, and there are still many species whose genome sequences are not yet available. Cross-species studies, where arrays developed for one species are used to study gene expression in a closely related species, have been used to address this gap, with some promising results. Current analytical methods have included filtration of some probes or genes that showed low

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Harrington, J. Patrick. "Polarized Continuum Radiation from Stellar Atmospheres." Proceedings of the International Astronomical Union 10, S305 (2014): 395–400. http://dx.doi.org/10.1017/s1743921315005116.

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AbstractContinuum scattering by free electrons can be significant in early type stars, while in late type stars Rayleigh scattering by hydrogen atoms or molecules may be important. Computer programs used to construct models of stellar atmospheres generally treat the scattering of the continuum radiation as isotropic and unpolarized, but this scattering has a dipole angular dependence and will produce polarization. We review an accurate method for evaluating the polarization and limb darkening of the radiation from model stellar atmospheres. We use this method to obtain results for: (i) Late ty

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Mak, Lesley, and Pooya Taheri. "An Automated Tool for Upgrading Fortran Codes." Software 1, no. 3 (2022): 299–315. http://dx.doi.org/10.3390/software1030014.

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With archaic coding techniques, there will be a time when it will be necessary to modernize vulnerable software. However, redeveloping out-of-date code can be a time-consuming task when dealing with a multitude of files. To reduce the amount of reassembly for Fortran-based projects, in this paper, we develop a prototype for automating the manual labor of refactoring individual files. ForDADT (Fortran Dynamic Autonomous Diagnostic Tool) project is a Python program designed to reduce the amount of refactoring necessary when compiling Fortran files. In this paper, we demonstrate how ForDADT is us

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Liu, Yongxu, Yinghui Quan, Guoyao Xiao, Aobo Li, and Jinjian Wu. "Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3792–801. http://dx.doi.org/10.1609/aaai.v38i4.28170.

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Quality assessment of images and videos emphasizes both local details and global semantics, whereas general data sampling methods (e.g., resizing, cropping or grid-based fragment) fail to catch them simultaneously. To address the deficiency, current approaches have to adopt multi-branch models and take as input the multi-resolution data, which burdens the model complexity. In this work, instead of stacking up models, a more elegant data sampling method (named as SAMA, scaling and masking) is explored, which compacts both the local and global content in a regular input size. The basic idea is t

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Liva, Gianluigi, Shumei Song, Lan Lan, Yifei Zhang, Shu Lin, and William E. Ryan. "Design of LDPC Codes: A Survey and New Results." Journal of Communications Software and Systems 2, no. 3 (2017): 191. http://dx.doi.org/10.24138/jcomss.v2i3.283.

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This survey paper provides fundamentals in the design of LDPC codes. To provide a target for the code designer, we first summarize the EXIT chart technique for determining(near-)optimal degree distributions for LDPC code ensembles. We also demonstrate the simplicity of representing codes by protographs and how this naturally leads to quasi-cyclic LDPC codes. The EXIT chart technique is then extended to the special case of protograph-based LDPC codes. Next, we present several design approaches for LDPC codes which incorporate one or more accumulators, including quasi-cyclic accumulatorbased cod

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Li, Daixun, Weiying Xie, Jiaqing Zhang, and Yunsong Li. "MDFL: Multi-Domain Diffusion-Driven Feature Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8653–60. http://dx.doi.org/10.1609/aaai.v38i8.28710.

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High-dimensional images, known for their rich semantic information, are widely applied in remote sensing and other fields. The spatial information in these images reflects the object's texture features, while the spectral information reveals the potential spectral representations across different bands. Currently, the understanding of high-dimensional images remains limited to a single-domain perspective with performance degradation. Motivated by the masking texture effect observed in the human visual system, we present a multi-domain diffusion-driven feature learning network (MDFL) , a scheme

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Vakulenko, S. P., N. K. Volosova, and D. F. Pastukhov. "METHODS OF QR CODE TRANSMISSION IN COMPUTER STEGANOGRAPHY." World of Transport and Transportation 16, no. 5 (2018): 14–25. http://dx.doi.org/10.30932/1992-3252-2018-16-5-2.

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For the English full text of the article please see the attached PDF-File (English version follows Russian version).ABSTRACT The article deals with the problems of using steganography methods for transmitting data. The authors justify their approach by arguing that the importance of cryptography in terms of transfer of hidden data i s obvious, however, being cryptographic, coded information maintains potential threat with the mere existence of an encrypted message, that is, an indication that it is necessary to apply cryptanalysis. With the help of steganography, mathematical methods are const

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Semenova, Elizaveta, Maria Luisa Guerriero, Bairu Zhang, et al. "Flexible Fitting of PROTAC Concentration–Response Curves with Changepoint Gaussian Processes." SLAS DISCOVERY: Advancing the Science of Drug Discovery 26, no. 9 (2021): 1212–24. http://dx.doi.org/10.1177/24725552211028142.

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A proteolysis-targeting chimera (PROTAC) is a new technology that marks proteins for degradation in a highly specific manner. During screening, PROTAC compounds are tested in concentration–response (CR) assays to determine their potency, and parameters such as the half-maximal degradation concentration (DC50) are estimated from the fitted CR curves. These parameters are used to rank compounds, with lower DC50 values indicating greater potency. However, PROTAC data often exhibit biphasic and polyphasic relationships, making standard sigmoidal CR models inappropriate. A common solution includes

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Kurtin, Danielle L., Daniel A. J. Parsons, and Scott M. Stagg. "VTES: a stochastic Python-based tool to simulate viral transmission." F1000Research 9 (October 5, 2020): 1198. http://dx.doi.org/10.12688/f1000research.26786.1.

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The spread of diseases like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in human populations involve a large number of variables, making it difficult to predict how it will spread across communities and populations. Reduced representation simulations allow us to reduce the complexity of disease spread and model transmission based on a few key variables. Here we have created a Viral Transmission Education Simulator (VTES) that simulates the spread of disease through the interactions between circles representing individual people bouncing around a bounded, 2D plane. Infections a

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Cui, Jianming, Wenxiu Kong, Xiaojun Zhang, Da Chen, and Qingtian Zeng. "DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes." Entropy 23, no. 7 (2021): 863. http://dx.doi.org/10.3390/e23070863.

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Polar code has been adopted as the control channel coding scheme for the fifth generation (5G), and the performance of short polar codes is receiving intensive attention. The successive cancellation flipping (SC flipping) algorithm suffers a significant performance loss in short block lengths. To address this issue, we propose a double long short-term memory (DLSTM) neural network to locate the first error bit. To enhance the prediction accuracy of the DLSTM network, all frozen bits are clipped in the output layer. Then, Gaussian approximation is applied to measure the channel reliability and

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Sinex, D. G., and D. C. Havey. "Neural mechanisms of tone-on-tone masking: patterns of discharge rate and discharge synchrony related to rates of spontaneous discharge in the chinchilla auditory nerve." Journal of Neurophysiology 56, no. 6 (1986): 1763–80. http://dx.doi.org/10.1152/jn.1986.56.6.1763.

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Responses of chinchilla auditory nerve fibers to brief probe tones in the presence of a fixed tonal masker were obtained. The stimulus conditions were analogous to those that have been used in many psychophysical experiments. The relation between previously described response properties of auditory nerve fibers and features of psychophysical tone-on-tone masking was examined. In psychophysical studies, a fixed narrowband masker produces a characteristic pattern of masked thresholds, which becomes broad and asymmetrical at high masker levels. In the present experiment 1, a 5,000-Hz masker was p

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Su, Zian, Xiangzhe Xu, Ziyang Huang, et al. "CodeArt: Better Code Models by Attention Regularization When Symbols Are Lacking." Proceedings of the ACM on Software Engineering 1, FSE (2024): 562–85. http://dx.doi.org/10.1145/3643752.

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Transformer based code models have impressive performance in many software engineering tasks. However, their effectiveness degrades when symbols are missing or not informative. The reason is that the model may not learn to pay attention to the right correlations/contexts without the help of symbols. We propose a new method to pre-train general code models when symbols are lacking. We observe that in such cases, programs degenerate to something written in a very primitive language. We hence propose to use program analysis to extract contexts a priori (instead of relying on symbols and masked la

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Wang, Liang, Xiang Tao, Qiang Liu, Shu Wu, and Liang Wang. "Rethinking Graph Masked Autoencoders through Alignment and Uniformity." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15528–36. http://dx.doi.org/10.1609/aaai.v38i14.29479.

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Self-supervised learning on graphs can be bifurcated into contrastive and generative methods. Contrastive methods, also known as graph contrastive learning (GCL), have dominated graph self-supervised learning in the past few years, but the recent advent of graph masked autoencoder (GraphMAE) rekindles the momentum behind generative methods. Despite the empirical success of GraphMAE, there is still a dearth of theoretical understanding regarding its efficacy. Moreover, while both generative and contrastive methods have been shown to be effective, their connections and differences have yet to be

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Özdel, Süleyman, Efe Bozkir, and Enkelejda Kasneci. "Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking." Proceedings of the ACM on Human-Computer Interaction 8, ETRA (2024): 1–28. http://dx.doi.org/10.1145/3655605.

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As eye tracking becomes pervasive with screen-based devices and head-mounted displays, privacy concerns regarding eye-tracking data have escalated. While state-of-the-art approaches for privacy-preserving eye tracking mostly involve differential privacy and empirical data manipulations, previous research has not focused on methods for scanpaths. We introduce a novel privacy-preserving scanpath comparison protocol designed for the widely used Needleman-Wunsch algorithm, a generalized version of the edit distance algorithm. Particularly, by incorporating the Paillier homomorphic encryption schem

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Wu, Cong, Xiao-Jun Wu, Josef Kittler, et al. "SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-Supervised Skeleton-Based Action Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5949–57. http://dx.doi.org/10.1609/aaai.v38i6.28409.

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Contrastive learning has achieved great success in skeleton-based action recognition. However, most existing approaches encode the skeleton sequences as entangled spatiotemporal representations and confine the contrasts to the same level of representation. Instead, this paper introduces a novel contrastive learning framework, namely Spatiotemporal Clues Disentanglement Network (SCD-Net). Specifically, we integrate the decoupling module with a feature extractor to derive explicit clues from spatial and temporal domains respectively. As for the training of SCD-Net, with a constructed global anch

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Thompson, William, Jensen Lawrence, Dori Blakely, et al. "Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets." Astronomical Journal 166, no. 4 (2023): 164. http://dx.doi.org/10.3847/1538-3881/acf5cc.

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Abstract As next-generation imaging instruments and interferometers search for planets closer to their stars, they must contend with increasing orbital motion and longer integration times. These compounding effects make it difficult to detect faint planets but also present an opportunity. Increased orbital motion makes it possible to move the search for planets into the orbital domain, where direct images can be freely combined with the radial velocity and proper motion anomaly, even without a confirmed detection in any single epoch. In this paper, we present a fast and differentiable multimet

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Zhao, Zhuoran, Jinbin Bai, Delong Chen, Debang Wang, and Yubo Pan. "Taming Diffusion Models for Music-Driven Conducting Motion Generation." Proceedings of the AAAI Symposium Series 1, no. 1 (2023): 40–44. http://dx.doi.org/10.1609/aaaiss.v1i1.27474.

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Generating the motion of orchestral conductors from a given piece of symphony music is a challenging task since it requires a model to learn semantic music features and capture the underlying distribution of real conducting motion. Prior works have applied Generative Adversarial Networks (GAN) to this task, but the promising diffusion model, which recently showed its advantages in terms of both training stability and output quality, has not been exploited in this context. This paper presents Diffusion-Conductor, a novel DDIM-based approach for music-driven conducting motion generation, which i

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Zhang, Nan, Liam O’Neill, Gautam Das, Xiuzhen Cheng, and Heng Huang. "No Silver Bullet." International Journal of Healthcare Information Systems and Informatics 7, no. 4 (2012): 48–58. http://dx.doi.org/10.4018/jhisi.2012100104.

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In accordance with HIPAA regulations, patients’ personal information is typically removed or generalized prior to being released as public data files. However, it is not known if the standard method of de-identification is sufficient to prevent re-identification by an intruder. The authors conducted analytical processing to identify security vulnerabilities in the protocols to de-identify hospital data. Their techniques for discovering privacy leakage utilized three disclosure channels: (1) data inter-dependency, (2) biomedical domain knowledge, and (3) suppression algorithms and partial suppr

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Zeng, Fan-Gang, Ying-Yee Kong, Henry J. Michalewski, and Arnold Starr. "Perceptual Consequences of Disrupted Auditory Nerve Activity." Journal of Neurophysiology 93, no. 6 (2005): 3050–63. http://dx.doi.org/10.1152/jn.00985.2004.

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Perceptual consequences of disrupted auditory nerve activity were systematically studied in 21 subjects who had been clinically diagnosed with auditory neuropathy (AN), a recently defined disorder characterized by normal outer hair cell function but disrupted auditory nerve function. Neurological and electrophysical evidence suggests that disrupted auditory nerve activity is due to desynchronized or reduced neural activity or both. Psychophysical measures showed that the disrupted neural activity has minimal effects on intensity-related perception, such as loudness discrimination, pitch discri

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Nechaeva, Anastasia L. "<i>Otherness</i> as Basis of Artistic Chronotope: Script vs. Film." SibScript 25, no. 4 (2023): 567–76. http://dx.doi.org/10.21603/sibscript-2023-25-4-567-576.

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The article describes the existential category of the Other and the Otherness in V. Sorokin's screenplay for the film called 4, which was directed by I. Hrzhanovsky in 2004. The Otherness helped V. Sorokin to realize his artistic idea of post-Soviet Russia. According to Jean Paul Sartre, the Other conveys a feeling of the last days: without a single scene of physical violence, the reader develops a feeling of nausea. The essence of the Other is based on the principles of rejection, fear, and inevitability; the Other is the only verifier of one’s existence. In 4, the frightening atmosphere of t

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Lin, Zhiwei, Yongtao Wang, Shengxiang Qi, Nan Dong, and Ming-Hsuan Yang. "BEV-MAE: Bird’s Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3531–39. http://dx.doi.org/10.1609/aaai.v38i4.28141.

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Existing LiDAR-based 3D object detection methods for autonomous driving scenarios mainly adopt the training-from-scratch paradigm. Unfortunately, this paradigm heavily relies on large-scale labeled data, whose collection can be expensive and time-consuming. Self-supervised pre-training is an effective and desirable way to alleviate this dependence on extensive annotated data. In this work, we present BEV-MAE, an efficient masked autoencoder pre-training framework for LiDAR-based 3D object detection in autonomous driving. Specifically, we propose a bird's eye view (BEV) guided masking strategy

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Cheng, Shing Hing William, Chitchanok Chuengsatiansup, Daniel Genkin, et al. "Evict+Spec+Time: Exploiting Out-of-Order Execution to Improve Cache-Timing Attacks." IACR Transactions on Cryptographic Hardware and Embedded Systems 2024, no. 3 (2024): 224–48. http://dx.doi.org/10.46586/tches.v2024.i3.224-248.

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Speculative out-of-order execution is a strategy of masking execution latency by allowing younger instructions to execute before older instructions. While originally considered to be innocuous, speculative out-of-order execution was brought into the spotlight with the 2018 publication of the Spectre and Meltdown attacks. These attacks demonstrated that microarchitectural side channels can leak sensitive data accessed by speculatively executed instructions that are not part of the normal program execution. Since then, a significant effort has been vested in investigating how microarchitectural

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Fan, Deng-Ping, Ziling Huang, Peng Zheng, Hong Liu, Xuebin Qin, and Luc Van Gool. "Facial-sketch Synthesis: A New Challenge." Machine Intelligence Research 19, no. 4 (2022): 257–87. http://dx.doi.org/10.1007/s11633-022-1349-9.

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AbstractThis paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high cost of obtaining hand-drawn sketch datasets, there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade. We first introduce a high-quality dataset for FSS, named FS2K, which consists of 2 104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability and should thus facilitat

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Wang, Xu, Mingming Zhang, Xiangxin Meng, Jian Zhang, Yang Liu, and Chunming Hu. "Element-Based Automated DNN Repair with Fine-Tuned Masked Language Model." Proceedings of the ACM on Software Engineering 2, FSE (2025): 106–29. https://doi.org/10.1145/3715716.

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Deep Neural Networks (DNNs) are prevalent across a wide range of applications. Despite their success, the complexity and opaque nature of DNNs pose significant challenges in debugging and repairing DNN models, limiting their reliability and broader adoption. In this paper, we propose MLM4DNN, an element-based automated DNN repair method. Unlike previous techniques that focus on post-training adjustments or rely heavily on predefined bug patterns, MLM4DNN repairs DNNs by leveraging a fine-tuned Masked Language Model (MLM) to predict correct fixes for nine predefined key elements in DNNs. We con

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Belim, Sergey V., Sergey N. Munko, and Svetlana Yu Belim. "Steganographic embedding model in files with hierarchical structure." Journal Of Applied Informatics 20, no. 1 (2025): 125–39. https://doi.org/10.37791/2687-0649-2025-20-1-125-139.

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All steganographic methods are focused on a specific container file format. Text documents with markup are the most difficult object for steganography methods. The article suggests a model for embedding structured text documents in control tags. The model uses the document tree structure and embeds into free leaf nodes. This approach adds hidden data that does not affect the display of the document. Two steganographic methods are implemented based on this model. The first method embeds hidden data into html document tags. The embedding method adds underplayed tags and style classes to free lea

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Zhao, Jianwei, Qiang Zhai, Pengbo Zhao, Rui Huang, and Hong Cheng. "Co-Visual Pattern-Augmented Generative Transformer Learning for Automobile Geo-Localization." Remote Sensing 15, no. 9 (2023): 2221. http://dx.doi.org/10.3390/rs15092221.

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Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographic location of the ground-level camera by matching against enormous geo-tagged aerial (e.g., satellite) images, has received a lot of attention but remains extremely challenging due to the drastic appearance differences across aerial–ground views. In existing methods, global representations of different views are extracted primarily using Siamese-like architectur

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Lin, Yuqi, Minghao Chen, Kaipeng Zhang, et al. "TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP without Training." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3513–21. http://dx.doi.org/10.1609/aaai.v38i4.28139.

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Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in open-vocabulary classification. The class token in the image encoder is trained to capture the global features to distinguish different text descriptions supervised by contrastive loss, making it highly effective for single-label classification. However, it shows poor performance on multi-label datasets because the global feature tends to be dominated by the most prominent class and the contrastive nature of softmax operation aggravates it. In this study, we observe that the multi-label classification re

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Chayka, Larysa. "STRUCTURE AND DYNAMICS OF VERBAL CONFLICT SITUATION (BASED ON THE ENGLISH LANGUAGE DISCOURSE)." Odessa Linguistic Journal, no. 12 (2018): 39–46. http://dx.doi.org/10.32837/2312-3192/12/6.

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The article provides the results of the verbal conflict analysis based on the English language dialogical discourse highlighting the problems related to its definition, structure and dynamics. The paper discusses a series of issues concerning the verbal conflict which is characterized by linguistic manipulation, i.e., by using language features and principles of its application on the purpose of hidden influence on the addressee in the right direction for the addresser. The article offers definitions of concepts of the verbal conflict situation, its phases and components, identifies actual and

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Tsmots, I. G., V. M. Teslyuk, Yu V. Opotiak, and I. V. Pikh. "MODELS AND TOOLS FOR DEBUGGING AND TESTING MOBILE SYSTEMS FOR NEURO-LIKE CRYPTOGRAPHIC PROTECTION OF DATA TRANSMISSION." Ukrainian Journal of Information Technology 4, no. 2 (2022): 45–55. http://dx.doi.org/10.23939/ujit2022.02.045.

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The work revealed the need for providing cryptographic protection and immunity to data transmission and control commands when using the mobile robotic platform as well as the importance of taking into account the limitations regarding dimensions, energy consumption and productivity. It was found that one of the ways to meet the requirements of cryptographic protection is the use of neuro-like networks. Their feature is the ability to pre-calculate the weight coefficients that will be used when encrypting/decrypting data. It is suggested that during neuro-like encryption/decryption of data, the

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Cayuso, Juan, Richard Bloch, Selim C. Hotinli, Matthew C. Johnson, and Fiona McCarthy. "Velocity reconstruction with the cosmic microwave background and galaxy surveys." Journal of Cosmology and Astroparticle Physics 2023, no. 02 (2023): 051. http://dx.doi.org/10.1088/1475-7516/2023/02/051.

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Abstract The kinetic Sunyaev Zel'dovich (kSZ) and moving lens effects, secondary contributions to the cosmic microwave background (CMB), carry significant cosmological information due to their dependence on the large-scale peculiar velocity field. Previous work identified a promising means of extracting this cosmological information using a set of quadratic estimators for the radial and transverse components of the velocity field. These estimators are based on the statistically anisotropic components of the cross-correlation between the CMB and a tracer of large scale structure, such as a gala

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Konduri, Raja Rajeswari, Nagireddi Roopavathi, Balantrapu Vijaya Lakshmi, and Putrevu Venkata Krishna Chaitanya. "Mitigating Peak Sidelobe Levels in Pulse Compression Radar using Artificial Neural Networks." Indian Journal of Artificial Intelligence and Neural Networking 3, no. 6 (2024): 12–20. http://dx.doi.org/10.54105/ijainn.f9517.03061023.

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In this paper, Artificial Neural Networks (ANNs) are being considered to obtain low sidelobe pattern for binary codes and thereby to improve the performance of pulse compression radar. Pulse compression is a popular technique used for improving range resolution in the radar systems. This paper proposes a new approach for Pulse Compression using various types of ANN networks like Multi-Layer Perception (MLP), Recursive Neural Networks (RNN), Radial Basis Function (RBF) and Recurrent Radial Basis Function (RRBF) and a special class of Feed-Forward Wavelet Neural Network (WNN) with one input laye

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Raja, Rajeswari Konduri. "Mitigating Peak Sidelobe Levels in Pulse Compression Radar using Artificial Neural Networks." Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 3, no. 6 (2024): 12–20. https://doi.org/10.54105/ijainn.F9517.03061023.

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<strong>Abstract:</strong> In this paper, Artificial Neural Networks (ANNs) are being considered to obtain low sidelobe pattern for binary codes and thereby to improve the performance of pulse compression radar. Pulse compression is a popular technique used for improving range resolution in the radar systems. This paper proposes a new approach for Pulse Compression using various types of ANN networks like Multi-Layer Perception (MLP), Recursive Neural Networks (RNN), Radial Basis Function (RBF) and Recurrent Radial Basis Function (RRBF) and a special class of Feed-Forward Wavelet Neural Networ

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Marin, Maximillian, Roger Vargas, Michael Harris, et al. "Benchmarking the empirical accuracy of short-read sequencing across the M. tuberculosis genome." Bioinformatics 38, no. 7 (2022): 1781–87. http://dx.doi.org/10.1093/bioinformatics/btac023.

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Abstract Motivation Short-read whole-genome sequencing (WGS) is a vital tool for clinical applications and basic research. Genetic divergence from the reference genome, repetitive sequences and sequencing bias reduces the performance of variant calling using short-read alignment, but the loss in recall and specificity has not been adequately characterized. To benchmark short-read variant calling, we used 36 diverse clinical Mycobacterium tuberculosis (Mtb) isolates dually sequenced with Illumina short-reads and PacBio long-reads. We systematically studied the short-read variant calling accurac

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Dreifeld, O. V. "Pragmatic and Semantic Analysis of Anti-War Protest Utterances (on the Data of Public Opinion Discourse in the Spring of 2022 in Russia)." Discourse 9, no. 6 (2023): 116–27. http://dx.doi.org/10.32603/2412-8562-2023-9-6-116-127.

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Introduction. The main objective of article is analysis the personal anti-war utterances on protest posters to determine their pragmatic functions in the discourse of public opinion. The theoretical and practical significance of article is identification, description and classification types of personal speech tactics of anti-war protest discourse based on material of verbalized statements at solitary pickets in the spring of 2022 in Russia.Methodology and sources. A content analysis, pragmatic and semantic analysis of 455 utterances are carried out, utterances are obtained by continuous sampl

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Rognes, Torbjørn, Tomáš Flouri, Ben Nichols, Christopher Quince, and Frédéric Mahé. "VSEARCH: a versatile open source tool for metagenomics." PeerJ 4 (October 18, 2016): e2584. http://dx.doi.org/10.7717/peerj.2584.

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BackgroundVSEARCH is an open source and free of charge multithreaded 64-bit tool for processing and preparing metagenomics, genomics and population genomics nucleotide sequence data. It is designed as an alternative to the widely used USEARCH tool (Edgar, 2010) for which the source code is not publicly available, algorithm details are only rudimentarily described, and only a memory-confined 32-bit version is freely available for academic use.MethodsWhen searching nucleotide sequences, VSEARCH uses a fast heuristic based on words shared by the query and target sequences in order to quickly iden

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Han, Changho, Youngjae Song, Hong-Seok Lim, et al. "Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals—Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study." Journal of Medical Internet Research 23, no. 9 (2021): e31129. http://dx.doi.org/10.2196/31129.

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Background When using a smartwatch to obtain electrocardiogram (ECG) signals from multiple leads, the device has to be placed on different parts of the body sequentially. The ECG signals measured from different leads are asynchronous. Artificial intelligence (AI) models for asynchronous ECG signals have barely been explored. Objective We aimed to develop an AI model for detecting acute myocardial infarction using asynchronous ECGs and compare its performance with that of the automatic ECG interpretations provided by a commercial ECG analysis software. We sought to evaluate the feasibility of i

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Stratonov, Vasyl. ""Computer crimes": some features and characteristics." Naukovyy Visnyk Dnipropetrovs'kogo Derzhavnogo Universytetu Vnutrishnikh Sprav 2, no. 2 (2020): 134–41. http://dx.doi.org/10.31733/2078-3566-2020-2-134-141.

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Unfortunately, along with positive achievements, informatization also has negative manifestations, namely, the possibility of using computer technology to commit crimes. The world has long been talking about "cy-bercrime" about "computer crime," and chapter 16 of the Criminal Code of Ukraine deals with crimes in the use of computers, computer systems and networks, as well as telecommunications. Therefore, we can state that a unified approach to the definition of a concept does not exist. However, the introduction of certain norms into the law does not solve the problems. Problems arise with th

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