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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

Digital Communications and Networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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Dual-step optimization for binary sequences with high merit factors

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Digital Signal Processing: A Review Journal 2025年 165卷

作者： Pšeničnik, Blaž Mlinarič, Rene Brest, Janez Bošković, Borko Computer Architecture and Languages Laboratory Institute of Computer Science Faculty of Electrical Engineering and Computer Science University of Maribor Koroška c. 46 Maribor2000 Slovenia

The problem of finding aperiodic low auto-correlation binary sequences (LABS) presents a significant computational challenge, particularly as the sequence length increases. Such sequences have important applications in communication engineering, physics, chemistry, and cryptography. This paper introduces a dual-step algorithm for long binary sequences with high merit factors. The first step employs a parallel algorithm utilizing skew-symmetry and restriction classes to generate sequence candidates with merit factors above a predefined threshold. The second step uses a priority queue algorithm to refine these candidates further, searching the entire search space unrestrictedly. By combining GPU-based parallel computing and dual-step optimization, our approach has successfully identified best-known binary sequences for all lengths ranging from 450 to 527, with the exception of length 518, where the previous best-known merit factor value was matched with a different sequence. This hybrid method significantly outperforms traditional exhaustive and stochastic search methods, offering an efficient solution for finding long sequences with good merit factors. © 2025 The Author(s)

关键词： Digital arithmetic

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ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios 31

ToolEyes: Fine-Grained Evaluation for Tool Learning Capabili...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Ye, Junjie Li, Guanyu Gao, Songyang Huang, Caishuang Wu, Yilong Li, Sixian Fan, Xiaoran Dou, Shihan Ji, Tao Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Research Institute of Intelligent Complex Systems Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Pengcheng Laboratory China

ISBN: (纸本)9798891761964

Existing evaluations of tool learning primarily focus on validating the alignment of selected tools (e.g., various APIs) for large language models (LLMs) with expected outcomes. However, these approaches rely on a limited set of scenarios where answers can be pre-determined. Furthermore, a sole emphasis on outcomes disregards the complex capabilities required for LLMs to effectively use tools. To tackle this issue, we propose ToolEyes, a fine-grained system tailored for the evaluation of the LLMs' tool learning capabilities in authentic scenarios. The system meticulously examines seven real-world scenarios, analyzing five dimensions crucial to LLMs in tool learning: format alignment, intent comprehension, behavior planning, tool selection, and answer organization. Additionally, ToolEyes incorporates a tool library boasting approximately 600 tools, serving as an intermediary between LLMs and the physical world. Evaluations involving ten LLMs across three categories reveal a preference for specific scenarios and limited cognitive abilities in tool learning. Intriguingly, expanding the model size even exacerbates the hindrance to tool learning. The code and data are available at https://***/Junjie-Ye/ToolEyes. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and languages for Open Named Entity Recognition 31

Beyond Boundaries: Learning a Universal Entity Taxonomy acro...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yang, Yuming Zhao, Wantong Huang, Caishuang Ye, Junjie Wang, Xiao Zheng, Huiyuan Nan, Yang Wang, Yuran Xu, Xueying Huang, Kaixin Zhang, Yunke Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Honor Device Co. Ltd. Institute of Modern Languages and Linguistics Fudan University China Pengcheng Laboratory China Research Institute of Intelligent Complex Systems Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9798891761964

Open Named Entity Recognition (NER), which involves identifying arbitrary types of entities from arbitrary domains, remains challenging for Large Language Models (LLMs). Recent studies suggest that fine-tuning LLMs on extensive NER data can boost their performance. However, training directly on existing datasets neglects their inconsistent entity definitions and redundant data, limiting LLMs to dataset-specific learning and hindering out-of-domain adaptation. To address this, we present B2NERD, a compact dataset designed to guide LLMs' generalization in Open NER under a universal entity taxonomy. B2NERD is refined from 54 existing English and Chinese datasets using a two-step process. First, we detect inconsistent entity definitions across datasets and clarify them by distinguishable label names to construct a universal taxonomy of 400+ entity types. Second, we address redundancy using a data pruning strategy that selects fewer samples with greater category and semantic diversity. Comprehensive evaluation shows that B2NERD significantly enhances LLMs' Open NER capabilities. Our B2NER models, trained on B2NERD, outperform GPT-4 by 6.8-12.0 F1 points and surpass previous methods in 3 out-of-domain benchmarks across 15 datasets and 6 languages. The data, models, and code are publicly available at https://***/UmeanNever/B2NER. © 2025 Association for Computational Linguistics.

关键词： Taxonomies

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HIET:Hybrid Information Enhancement Transformer Network for Single-Photon Image Reconstruction

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Journal of Beijing institute of Technology 2025年第1期34卷 1-17页

作者： Yiming Liu Xuri Yao Tao Zhang Yifei Sun Ying Fu School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Center for Quantum Technology Research and Key Laboratory of Advanced Optoelec-tronic Quantum Architecture and Measurements(MOE) School of PhysicsBeijing Institute of TechnologyBeijing 100081China School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Uniview Technology Hangzhou 310056China

Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal ***,these advantages also make them highly susceptible to ***,single-photon cameras face severe quantization as low as 1 bit/*** factors make it a daunting task to recover high-quality scene information from noisy single-photon *** current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm *** this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel ***,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution ***,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon *** results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.

关键词： single-photon images hybrid information enhancement structual feature enhancement data simulation pipeline

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An effective meta-heuristics for trajectory planning problem in UAV-assisted vessel emission detection system

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-21页

作者： Zhu, Jie Cui, Weizhi Huang, Haiping Zhu, Tao Sun, Yuzhong School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications Nanjing 210003 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100864 China

With the development of the shipping industry, the pollution problem caused by vessel fuels is becoming increasingly severe. Due to the great mobility and on-demand service capability of Unmanned Aerial Vehicles (UAVs), UAVs have become effective tools for detecting pollution in Emission Control Areas (ECAs). How to obtain the effective trajectories of UAVs within the energy limitation and the time limitation is a difficult problem to solve. The paper investigates the trajectory planning problem in UAV-assisted vessel emission detection system. In the problem, the sailing vessels in a given area are taken as the detection tasks and multiple UAVs are ready to detect these vessels. Each vessel is assigned a different detecting weight and sails to the port at a different speed. The objective is to complete as many detection tasks as possible before the deadline and maximize the total weight of these completed tasks. A Genetic Algorithm (GA)-based trajectory planning algorithm framework is proposed for the problem under study. The framework consists of key components, such as the chromosome generation method to generate chromosomes of each generation, the feasible solution generation method to compute the feasible solution according to the sequence defined in a chromosome, and the chromosome reconstruction method to improve the quality of chromosomes based on history information. The experimental results show that the proposed algorithm outperforms the baseline algorithms in terms of effectiveness and robustness. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Emission control areas Genetic algorithm Trajectory planning UAV scheduling

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Spatial-Temporal Constrained Aircraft Sequencing and Path Planning for Airport Ground Movement Problem 19th

Spatial-Temporal Constrained Aircraft Sequencing and Path P...

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19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Liu, Fengmei Sun, Jingzhe Zhu, Jie Zhao, Zheng Huang, Huimin School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China The State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789819623754

In order to effectively alleviate the congestion and delay of aircraft during peak hours in a large hub airport, the paper studies the cooperative scheduling and path planning for arrival and departure aircrafts. A cooperative aircraft scheduling and path planning model for the airport ground movement problem is established with multiple spatial and temporal constraints, such as the queue constraint, time difference constraint, corner constraint. It aims to minimize the average taxiing time for all aircrafts. A genetic algorithm with elite selection strategy (ESGA) is proposed for the problem under study. Experimental results show that the proposed algorithm can effectively obtain the aircraft sequencing and path planning solutions, and significantly reduce the average taxiing time of the aircraft. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion planning

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Predicting Large Language Model Capabilities on Closed-Book QA Tasks Using Only Information Available Prior to Training

arXiv

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arXiv 2025年

作者： Jiang, Changhao Zhang, Ming Ye, Junjie Fan, Xiaoran Cao, Yifei Sun, Jiajun Xi, Zhiheng Dou, Shihan Dong, Yi Shen, Yujiong Tong, Jingqi Wang, Zhen Liang, Tao Fei, Zhihui Wan, Mingyang Ma, Guojun Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University Shanghai China ByteDance China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China

The GPT-4 technical report from OpenAI suggests that model performance on specific tasks can be predicted prior to training, though methodologies remain unspecified. This approach is crucial for optimizing resource allocation and ensuring data alignment with target tasks. To achieve this vision, we focus on predicting performance on Closed-book Question Answering (CBQA) tasks, which are closely tied to pre-training data and knowledge retention. We address three major challenges: 1) mastering the entire pre-training process, especially data construction;2) evaluating a model’s knowledge retention;and 3) predicting task-specific knowledge retention using only information available prior to training. To tackle these challenges, we pre-train three large language models (i.e., 1.6B, 7B, and 13B) using 560k dollars and 520k GPU hours. We analyze the pre-training data with knowledge triples and assess knowledge retention using established methods. Additionally, we introduce the SMI metric, an information-theoretic measure that quantifies the relationship between pre-training data, model size, and task-specific knowledge retention. Our experiments reveal a strong linear correlation (R2 > 0.84) between the SMI metric and the model’s accuracy on CBQA tasks across models of varying sizes (i.e., 1.1B, 1.6B, 7B, and 13B). The dataset, model, and code are available at https://***/yuhui1038/SMI. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

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DHAG-DTA: Dynamic Hierarchical Affinity Graph Model for Drug-Target Binding Affinity Prediction

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 697-709页

作者： Cheng Wang Yang Liu Shitao Song Kun Cao Xiaoyan Liu Gaurav Sharma Maozu Guo School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical Engineering Liaoning University of Technology Liaoning China Department of Electrical and Computer Engineering University of Rochester Rochester NY USA School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

Computational methods for predicting drug-target binding affinity (DTA) are critical for large-scale screening of prospective therapeutic compounds during drug discovery. Deep neural networks (DNNs) have recently shown significant promise for DTA prediction. By leveraging available data for training, DNNs can expand the use of DTA prediction to situations where only sequence information is available for potential drug molecules and their targets, and there is no prior knowledge regarding the molecular geometric conformations. We propose DHAG-DTA, a general dynamic hierarchical affinity graph DNN approach, for DTA prediction using molecular sequence information and already known drug-target interactions. DHAG-DTA introduces a two-level hierarchical graph structure: at the upper level, interactions between drug and target molecules are represented via an affinity graph and at the lower level, embedded molecular graphs represent interactions within the individual molecules. This allows for integration of information from both inter and intra molecular interactions for DTA prediction, which has also been addressed in other recent independent work. The fundamental innovations introduced by DHAG-DTA include: (a) a single overall hierarchical graph that allows better assimilation of information during the learning process compared with loosely-coupled individual graphs, (b) dynamic determination of the affinity graph structure via the introduction of unlabeled edges and a maximum entropy criterion for active edge selection, (c) skip connections in the DNN for fusing intra and inter molecular information, and (d) fusion of both model-based and similarity-based feature embeddings to get robust embeddings of unseen molecules. Experimental results on two common benchmark datasets demonstrate that DHAG-DTA outperforms other existing models on multiple evaluation metrics, achieving state-of-the-art performance.

关键词： Drugs Predictive models Graph neural networks Artificial neural networks Biological system modeling Proteins Atoms Computational modeling Bioinformatics Entropy

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Resource Allocation for Semantic Segmentation Tasks in Autonomous Driving: A Likelihood Active Inference Approach

Resource Allocation for Semantic Segmentation Tasks in Auton...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinxin Zhu F. Richard Yu Ying He Biao He College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The latest Segment Anything Model enables realtime scene annotation and understanding for autonomous driving systems, enhancing driving safety. However, effectively allocating resources for real-time performance and accuracy remains challenging in edge-cloud architectures. Traditional reinforcement learning struggles with poor generalization and the explorationexploitation dilemma, making it difficult to define clear reward functions. To address this, we propose a likelihood active inference approach to optimize resource allocation and improve system resource utilization. We use "intelligence" as a high-level indicator to quantify the efficiency of cognition in active inference, evaluating the difference between predicted and actual states during policy exploration. Experimental results show our algorithm outperforms mainstream deep reinforcement learning algorithms, improving sample efficiency and suitability for dynamically changing task workloads.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Signal processing Prediction algorithms Inference algorithms Real-time systems Resource management Speech processing Autonomous vehicles

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