OpenRaiser/Intern-Atlas huggingface dataset:
Insecurity Through Obscurity: Veiled Vulnerabilities in Closed-Source Contracts
Venue: arXiv cs.CR 2025
Year: 2025

Most blockchains cannot hide the binary code of programs (i.e., smart contracts) running on them. To conceal proprietary business logic and to potentially deter attacks, many smart contracts are closed-source and employ layers of obfuscation. However, we demonstrate that such obfuscation can obscure critical vulnerabilities rather than enhance security, a phenomenon we term insecurity through obscurity. To systematically analyze these risks on a large scale, we present SKANF, a novel EVM bytecode analysis tool tailored for closed-source and obfuscated contracts. SKANF combines control-flow deobfuscation, symbolic execution, and concolic execution based on historical transactions to identify and exploit asset management vulnerabilities. Our evaluation on real-world Maximal Extractable Value (MEV) bots reveals that SKANF detects vulnerabilities in 1,030 contracts and successfully generates exploits for 394 of them, with potential losses of \$10.6M. Additionally, we uncover 104 real-world MEV bot attacks that collectively resulted in \$2.76M in losses.
OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques
Venue: arXiv cs.CR 2025
Year: 2025

This technical report presents a comprehensive analysis of malware classification using OpCode sequences. Two distinct approaches are evaluated: traditional machine learning using n-gram analysis with Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree classifiers; and a deep learning approach employing a Convolutional Neural Network (CNN). The traditional machine learning approach establishes a baseline using handcrafted 1-gram and 2-gram features from disassembled malware samples. The deep learning methodology builds upon the work proposed in "Deep Android Malware Detection" by McLaughlin et al. and evaluates the performance of a CNN model trained to automatically extract features from raw OpCode data. Empirical results are compared using standard performance metrics (accuracy, precision, recall, and F1-score). While the SVM classifier outperforms other traditional techniques, the CNN model demonstrates competitive performance with the added benefit of automated feature extraction.
Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, Graph Convolutional Networks (GCNs) have gained popularity for their exceptional ability to process graph-structured data. Existing GCN-based approaches typically employ a shallow model architecture due to the over-smoothing phenomenon. Current approaches to mitigating over-smoothing primarily involve adding supplementary components to GCN architectures, such as residual connections and random edge-dropping strategies. However, these improvements toward deep GCNs have achieved only limited success. In this work, we analyze the intrinsic message passing mechanism of GCNs and identify a critical issue: messages originating from high-order neighbors must traverse through low-order neighbors to reach the target node. This repeated reliance on low-order neighbors leads to redundant information aggregation, a phenomenon we term over-aggregation. Our analysis demonstrates that over-aggregation not only introduces significant redundancy but also serves as the fundamental cause of over-smoothing in GCNs.
How to Mine Potentially Popular Items? A Reverse MIPS-based Approach
Venue: arXiv cs.DB 2025
Year: 2025

The $k$-MIPS ($k$ Maximum Inner Product Search) problem has been employed in many fields. Recently, its reverse version, the reverse $k$-MIPS problem, has been proposed. Given an item vector (i.e., query), it retrieves all user vectors such that their $k$-MIPS results contain the item vector. Consider the cardinality of a reverse $k$-MIPS result. A large cardinality means that the item is potentially popular, because it is included in the $k$-MIPS results of many users. This mining is important in recommender systems, market analysis, and new item development. Motivated by this, we formulate a new problem. In this problem, the score of each item is defined as the cardinality of its reverse $k$-MIPS result, and the $N$ items with the highest score are retrieved. A straightforward approach is to compute the scores of all items, but this is clearly prohibitive for large numbers of users and items. We remove this inefficiency issue and propose a fast algorithm for this problem. Because the main bottleneck of the problem is to compute the score of each item, we devise a new upper-bounding technique that is specific to our problem and filters unnecessary score computations. We conduct extensive experiments on real datasets and show the superiority of our algorithm over competitors.
Approximate Reverse $k$-Ranks Queries in High Dimensions
Venue: arXiv cs.DB 2025
Year: 2025

Many objects are represented as high-dimensional vectors nowadays. In this setting, the relevance between two objects (vectors) is usually evaluated by their inner product. Recently, item-centric searches, which search for users relevant to query items, have received attention and find important applications, such as product promotion and market analysis. To support these applications, this paper considers reverse $k$-ranks queries. Given a query vector $\mathbf{q}$, $k$, a set $\mathbf{U}$ of user vectors, and a set $\mathbf{P}$ of item vectors, this query retrieves the $k$ user vectors $\mathbf{u} \in \mathbf{U}$ with the highest $r(\mathbf{q},\mathbf{u},\mathbf{P})$, where $r(\mathbf{q},\mathbf{u},\mathbf{P})$ shows the rank of $\mathbf{q}$ for $\mathbf{u}$ among $\mathbf{P}$. Because efficiently computing the exact answer for this query is difficult in high dimensions, we address the problem of approximate reverse $k$-ranks queries. Informally, given an approximation factor $c$, this problem allows, as an output, a user $\mathbf{u}'$ such that $r(\mathbf{q},\mathbf{u}',\mathbf{P}) > τ$ but $r(\mathbf{q},\mathbf{u}',\mathbf{P}) \leq c \times τ$, where $τ$ is the rank threshold for the exact answer. We propose a new algorithm for solving this problem efficiently. Through theoretical and empirical analyses, we confirm the efficiency and effectiveness of our algorithm.
Integrating Locality-Aware Attention with Transformers for General Geometry PDEs
Venue: arXiv cs.LG 2025
Year: 2025

Neural operators have emerged as promising frameworks for learning mappings governed by partial differential equations (PDEs), serving as data-driven alternatives to traditional numerical methods. While methods such as the Fourier neural operator (FNO) have demonstrated notable performance, their reliance on uniform grids restricts their applicability to complex geometries and irregular meshes. Recently, Transformer-based neural operators with linear attention mechanisms have shown potential in overcoming these limitations for large-scale PDE simulations. However, these approaches predominantly emphasize global feature aggregation, often overlooking fine-scale dynamics and localized PDE behaviors essential for accurate solutions. To address these challenges, we propose the Locality-Aware Attention Transformer (LA2Former), which leverages K-nearest neighbors for dynamic patchifying and integrates global-local attention for enhanced PDE modeling. By combining linear attention for efficient global context encoding with pairwise attention for capturing intricate local interactions, LA2Former achieves an optimal balance between computational efficiency and predictive accuracy. Extensive evaluations across six benchmark datasets demonstrate that LA2Former improves predictive accuracy by over 50% relative to existing linear attention methods, while also outperforming full pairwise attention under optimal conditions. This work underscores the critical importance of localized feature learning in advancing Transformer-based neural operators for solving PDEs on complex and irregular domains.
An algorithm to compute Selmer groups via resolutions by permutations modules
Venue: arXiv cs.SC 2025
Year: 2025

Given a number field with absolute Galois group $\mathcal{G}$, a finite Galois module $M$, and a Selmer system $\mathcal{L}$, this article gives a method to compute Sel$_\mathcal{L}$, the Selmer group of $M$ attached to $\mathcal{L}$. First we describe an algorithm to obtain a resolution of $M$ where the morphisms are given by Hecke operators. Then we construct another group $H^1_S(\mathcal{G}, M)$ and we prove, using the properties of Hecke operators, that $H^1_S(\mathcal{G}, M)$ is a Selmer group containing Sel$_\mathcal{L}$. Then, we discuss the time complexity of this method.
Deep Learning Models Meet Financial Data Modalities
Venue: arXiv cs.LG 2025
Year: 2025

Algorithmic trading relies on extracting meaningful signals from diverse financial data sources, including candlestick charts, order statistics on put and canceled orders, traded volume data, limit order books, and news flow. While deep learning has demonstrated remarkable success in processing unstructured data and has significantly advanced natural language processing, its application to structured financial data remains an ongoing challenge. This study investigates the integration of deep learning models with financial data modalities, aiming to enhance predictive performance in trading strategies and portfolio optimization. We present a novel approach to incorporating limit order book analysis into algorithmic trading by developing embedding techniques and treating sequential limit order book snapshots as distinct input channels in an image-based representation. Our methodology for processing limit order book data achieves state-of-the-art performance in high-frequency trading algorithms, underscoring the effectiveness of deep learning in financial applications.
Multi-class Item Mining under Local Differential Privacy
Venue: arXiv cs.CR 2025
Year: 2025

Item mining, a fundamental task for collecting statistical data from users, has raised increasing privacy concerns. To address these concerns, local differential privacy (LDP) was proposed as a privacy-preserving technique. Existing LDP item mining mechanisms primarily concentrate on global statistics, i.e., those from the entire dataset. Nevertheless, they fall short of user-tailored tasks such as personalized recommendations, whereas classwise statistics can improve task accuracy with fine-grained information. Meanwhile, the introduction of class labels brings new challenges. Label perturbation may result in invalid items for aggregation. To this end, we propose frameworks for multi-class item mining, along with two mechanisms: validity perturbation to reduce the impact of invalid data, and correlated perturbation to preserve the relationship between labels and items. We also apply these optimized methods to two multi-class item mining queries: frequency estimation and top-$k$ item mining. Through theoretical analysis and extensive experiments, we verify the effectiveness and superiority of these methods.
Can Local Representation Alignment RNNs Solve Temporal Tasks?
Venue: arXiv cs.LG 2025
Year: 2025

Recurrent Neural Networks (RNNs) are commonly used for real-time processing, streaming data, and cases where the amount of training samples is limited. Backpropagation Through Time (BPTT) is the predominant algorithm for training RNNs; however, it is frequently criticized for being prone to exploding and vanishing gradients and being biologically implausible. In this paper, we present and evaluate a target propagation-based method for RNNs, which uses local updates and seeks to reduce the said instabilities. Having stable RNN models increases their practical use in a wide range of fields such as natural language processing, time-series forecasting, anomaly detection, control systems, and robotics. The proposed solution uses local representation alignment (LRA). We thoroughly analyze the performance of this method, experiment with normalization and different local error functions, and invalidate certain assumptions about the behavior of this type of learning. Namely, we demonstrate that despite the decomposition of the network into sub-graphs, the model still suffers from vanishing gradients. We also show that gradient clipping as proposed in LRA has little to no effect on network performance. This results in an LRA RNN model that is very difficult to train due to vanishing gradients. We address this by introducing gradient regularization in the direction of the update and demonstrate that this modification promotes gradient flow and meaningfully impacts convergence. We compare and discuss the performance of the algorithm, and we show that the regularized LRA RNN considerably outperforms the unregularized version on three landmark tasks: temporal order, 3-bit temporal order, and random permutation.
Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation
Venue: arXiv cs.CR 2025
Year: 2025

Many adversarial attack approaches are proposed to verify the vulnerability of language models. However, they require numerous queries and the information on the target model. Even black-box attack methods also require the target model's output information. They are not applicable in real-world scenarios, as in hard black-box settings where the target model is closed and inaccessible. Even the recently proposed hard black-box attacks still require many queries and demand extremely high costs for training adversarial generators. To address these challenges, we propose Q-faker (Query-free Hard Black-box Attacker), a novel and efficient method that generates adversarial examples without accessing the target model. To avoid accessing the target model, we use a surrogate model instead. The surrogate model generates adversarial sentences for a target-agnostic attack. During this process, we leverage controlled generation techniques. We evaluate our proposed method on eight datasets. Experimental results demonstrate our method's effectiveness including high transferability and the high quality of the generated adversarial examples, and prove its practical in hard black-box settings.
HDBFormer: Efficient RGB-D Semantic Segmentation with A Heterogeneous Dual-Branch Framework
Venue: arXiv cs.CV 2025
Year: 2025

In RGB-D semantic segmentation for indoor scenes, a key challenge is effectively integrating the rich color information from RGB images with the spatial distance information from depth images. However, most existing methods overlook the inherent differences in how RGB and depth images express information. Properly distinguishing the processing of RGB and depth images is essential to fully exploiting their unique and significant characteristics. To address this, we propose a novel heterogeneous dual-branch framework called HDBFormer, specifically designed to handle these modality differences. For RGB images, which contain rich detail, we employ both a basic and detail encoder to extract local and global features. For the simpler depth images, we propose LDFormer, a lightweight hierarchical encoder that efficiently extracts depth features with fewer parameters. Additionally, we introduce the Modality Information Interaction Module (MIIM), which combines transformers with large kernel convolutions to interact global and local information across modalities efficiently. Extensive experiments show that HDBFormer achieves state-of-the-art performance on the NYUDepthv2 and SUN-RGBD datasets. The code is available at: https://github.com/Weishuobin/HDBFormer.
How to Achieve Higher Accuracy with Less Training Points?
Venue: arXiv cs.LG 2025
Year: 2025

In the era of large-scale model training, the extensive use of available datasets has resulted in significant computational inefficiencies. To tackle this issue, we explore methods for identifying informative subsets of training data that can achieve comparable or even superior model performance. We propose a technique based on influence functions to determine which training samples should be included in the training set. We conducted empirical evaluations of our method on binary classification tasks utilizing logistic regression models. Our approach demonstrates performance comparable to that of training on the entire dataset while using only 10% of the data. Furthermore, we found that our method achieved even higher accuracy when trained with just 60% of the data.
Memristive chaotic circuit for information processing through time
Venue: arXiv cs.ET 2025
Year: 2025

Human brain processes sensory information in real-time with extraordinary efficiency compared to the possibilities of current artificial computing systems. It operates as a complex nonlinear system, composed of interacting dynamic units - neurons and synapses - that processes data-streams as time goes by, i.e. through time, using time as an internal self-standing variable. Here we report on a memristor-based compact chaotic circuit included in a computing architecture that can process information through time. We realized a hardware memristive version of the formally simplest chaotic circuit that, thanks to the nonlinearity of the nonvolatile memristor device, evolves with complex dynamics in response to a driving signal. The circuit is used in a single-node reservoir computing scheme to demonstrate nonlinear classification tasks and the processing of data streams through time. These results demonstrate that a simple memristor-based chaotic circuit has the potential to operate as a nonlinear dynamics-based computing system and to process temporal information through time.
Capacity-achieving sparse superposition codes with spatially coupled VAMP decoder
Venue: arXiv cs.IT 2025
Year: 2025

Sparse superposition (SS) codes provide an efficient communication scheme over the Gaussian channel, utilizing the vector approximate message passing (VAMP) decoder for rotational invariant design matrices. Previous work has established that the VAMP decoder for SS achieves Shannon capacity when the design matrix satisfies a specific spectral criterion and exponential decay power allocation is used. In this work, we propose a spatially coupled VAMP (SC-VAMP) decoder for SS with spatially coupled design matrices. Based on state evolution (SE) analysis, we demonstrate that the SC-VAMP decoder is capacity-achieving when the design matrices satisfy the spectra criterion. Empirically, we show that the SC-VAMP decoder outperforms the VAMP decoder with exponential decay power allocation, achieving a lower section error rate. All codes are available on https://github.com/yztfu/SC-VAMP-for-Superposition-Code.git.
FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking
Venue: arXiv cs.CV 2025
Year: 2025

Anti-UAV tracking poses significant challenges, including small target sizes, abrupt camera motion, and cluttered infrared backgrounds. Existing tracking paradigms can be broadly categorized into global- and local-based methods. Global-based trackers, such as SiamDT, achieve high accuracy by scanning the entire field of view but suffer from excessive computational overhead, limiting real-world deployment. In contrast, local-based methods, including OSTrack and ROMTrack, efficiently restrict the search region but struggle when targets undergo significant displacements due to abrupt camera motion. Through preliminary experiments, it is evident that a local tracker, when paired with adaptive search region adjustment, can significantly enhance tracking accuracy, narrowing the gap between local and global trackers. To address this challenge, we propose FocusTrack, a novel framework that dynamically refines the search region and strengthens feature representations, achieving an optimal balance between computational efficiency and tracking accuracy. Specifically, our Search Region Adjustment (SRA) strategy estimates the target presence probability and adaptively adjusts the field of view, ensuring the target remains within focus. Furthermore, to counteract feature degradation caused by varying search regions, the Attention-to-Mask (ATM) module is proposed. This module integrates hierarchical information, enriching the target representations with fine-grained details. Experimental results demonstrate that FocusTrack achieves state-of-the-art performance, obtaining 67.7% AUC on AntiUAV and 62.8% AUC on AntiUAV410, outperforming the baseline tracker by 8.5% and 9.1% AUC, respectively. In terms of efficiency, FocusTrack surpasses global-based trackers, requiring only 30G MACs and achieving 143 fps with FocusTrack (SRA) and 44 fps with the full version, both enabling real-time tracking.
Robot Navigation in Dynamic Environments using Acceleration Obstacles
Venue: arXiv cs.RO 2025
Year: 2025

This paper addresses the issue of motion planning in dynamic environments by extending the concept of Velocity Obstacle and Nonlinear Velocity Obstacle to Acceleration Obstacle AO and Nonlinear Acceleration Obstacle NAO. Similarly to VO and NLVO, the AO and NAO represent the set of colliding constant accelerations of the maneuvering robot with obstacles moving along linear and nonlinear trajectories, respectively. Contrary to prior works, we derive analytically the exact boundaries of AO and NAO. To enhance an intuitive understanding of these representations, we first derive the AO in several steps: first extending the VO to the Basic Acceleration Obstacle BAO that consists of the set of constant accelerations of the robot that would collide with an obstacle moving at constant accelerations, while assuming zero initial velocities of the robot and obstacle. This is then extended to the AO while assuming arbitrary initial velocities of the robot and obstacle. And finally, we derive the NAO that in addition to the prior assumptions, accounts for obstacles moving along arbitrary trajectories. The introduction of NAO allows the generation of safe avoidance maneuvers that directly account for the robot's second-order dynamics, with acceleration as its control input. The AO and NAO are demonstrated in several examples of selecting avoidance maneuvers in challenging road traffic. It is shown that the use of NAO drastically reduces the adjustment rate of the maneuvering robot's acceleration while moving in complex road traffic scenarios. The presented approach enables reactive and efficient navigation for multiple robots, with potential application for autonomous vehicles operating in complex dynamic environments.
DenSe-AdViT: A novel Vision Transformer for Dense SAR Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Vision Transformer (ViT) has achieved remarkable results in object detection for synthetic aperture radar (SAR) images, owing to its exceptional ability to extract global features. However, it struggles with the extraction of multi-scale local features, leading to limited performance in detecting small targets, especially when they are densely arranged. Therefore, we propose Density-Sensitive Vision Transformer with Adaptive Tokens (DenSe-AdViT) for dense SAR target detection. We design a Density-Aware Module (DAM) as a preliminary component that generates a density tensor based on target distribution. It is guided by a meticulously crafted objective metric, enabling precise and effective capture of the spatial distribution and density of objects. To integrate the multi-scale information enhanced by convolutional neural networks (CNNs) with the global features derived from the Transformer, Density-Enhanced Fusion Module (DEFM) is proposed. It effectively refines attention toward target-survival regions with the assist of density mask and the multiple sources features. Notably, our DenSe-AdViT achieves 79.8% mAP on the RSDD dataset and 92.5% on the SIVED dataset, both of which feature a large number of densely distributed vehicle targets.