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20th International Conference on Information Security and Cryptology, Inscrypt 2024

作者： Jin, Huixuan Peng, Cong Fu, Jintao Luo, Min Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China Henan Key Laboratory of Network Cryptography Technology Henan Zhengzhou China Inspur Academy of Science and Technology Jinan China

ISBN: (纸本)9789819647330

Time-Locked Puzzle (TLP) and timed-release encryption (T-RE) are both time-based locking mechanisms. The former requires the decryptor to perform a predetermined amount of computation, while the latter requires a trusted server to release the decryption information after a preset time, thereby sending information to the future. As research has progressed, many feature-rich TLPs and TREs have emerged. However, TLP and TRE still face issues in practical deployment. The imbalance in computation load with TLP can result in significant computational burdens in many deployment scenarios, while the timed release in TRE necessitates that the server remains online. To address these issues, our goal is to develop a multifunctional solution to offload the computational burden and allow the server to be offline. Our scheme focuses on RSA-based TLPs and builds upon the sequential TRE schemes proposed by Chvojka and Jager et al.(ESORICS’21). We construct a multi-timeline scheme that aggregates multiple functions and allows the server to be offline. The multi-timeline structure proposed in our scheme, consisting of a main timeline, sub-timelines, and reward timelines, allows different timelines to assume different functions, and the main timeline controls the information release of the remaining timelines to achieve an efficient puzzle-solving release mechanism. The reward timeline provides a transfer account for storing bonuses for entities that solve puzzles, which achieves a self-balancing mechanism for computing resources. Due to the completion of a transaction guaranteed by the adaptor signature and smart contract, which ensure the atomicity of the transaction, the scheme achieves a traceable fair trading mechanism and a public verification mechanism. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computational burden multi-timeline TLP TRE

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SHOCK FORMATION FOR THE 2D ROTATING SHALLOW WATER EQUATIONS WITH NON-ZERO VORTICITY

arXiv

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arXiv 2025年

作者： Chen, Zhendong Xie, Chunjing School of Mathematical Sciences Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Institute of Natural Sciences Ministry of Education Key Laboratory of Scientific and Engineering Computing CMA-Shanghai Shanghai Jiao Tong University Shanghai200240 China

In the paper, the shock formation for the two-dimensional rotating shallow water system is established. We construct a large class of initial data which leads to the finite-time blow-up for the solutions. Moreover, the solutions are allowed to have non-zero large vorticity (in derivative sense), even up to the shock. Our results provide the first complete geometric description of the shock formation mechanism to the two-dimensional rotating shallow water system with vorticity. The formation of shock is characterized by the collapse of the characteristic hypersurfaces, where the first-order derivatives of the velocity, the height, and the specific vorticity blow up while the potential vorticity remains Lipschitz continuous. The methods developed in this paper should also be useful in studying the shock formation for the Euler equations with various source terms and a class of quasilinear Klein-Gordon equations in multi-dimensions. Copyright © 2025, The Authors. All rights reserved.

关键词： Euler equations

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DetecVFuzz: Enhancing Security in Consumer Electronic Devices Through Scalable Vulnerability Testing of Virtual Devices

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Xiao, Xi Guo, Yongjian Jolfaei, Alireza Chen, Chuan Haghighi, Mohammad Sayad Wen, Sheng Lin, Yuanyi Tsinghua University Shenzhen International Graduate School Shenzhen China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education China Peng Cheng Laboratory Guangdong Shenzhen China Flinders University College of Science and Engineering SA5042 Australia Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Ministry of Education China Qilu University of Technology Shandong Academy of Sciences National Supercomputer Center in Jinan Jinan China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

The interconnection facilitated by consumer electronics has led to the creation of a complex network of devices. Testing efficiency of such consumer electronic devices can be improved through hardware emulation. QEMU is a classical emulator for simulation and testing. Fuzzing is a promising solution to test the security of such devices, which has become pivotal in identifying device vulnerability. However, Qtest libfuzzer, the QEMU native fuzzer, requires manual writing of stub code, which makes conducting the fuzzing process difficult and yields poor performance. To address these challenges in virtual device security analysis, we propose DetecVFuzz, a novel automatic fuzzing framework based on QEMU-Qtest, aiming to find vulnerabilities in virtual PCI devices, including USB, audio, network, etc. We inject mutated inputs into nested data structures as parameters of memory-related APIs to ensure the stable operation of VM procedures. We implemented DetecVFuzz and evaluated it on different virtual devices, such as virtual memory USB stack and drivers. In preliminary evaluations on nine recent versions of the QEMU, we found 15 new memory errors in virtual devices. In general, DetecVFuzz achieves 89.37% line coverage and 78.65% branch coverage in virtual devices. We identified 26 new crashes/errors, three of which were unseen new bugs. © 1975-2011 IEEE.

关键词： Virtual addresses

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Thermal and Optical Performance of Semi-Transparent BIPV Windows in High-Rise Office Buildings: A Case Study in the UK 1st

Thermal and Optical Performance of Semi-Transparent BIPV Win...

引用

1st International Conference of Urban Climate, Sustainability, and Urban Design, UCSUD 2023

作者： Scibetta, Joseph Shi, Shaohang Du, Hu He, Bao-Jie Yang, Siliang School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS2 8AG United Kingdom School of Architecture Tsinghua University Beijing100080 China School of Civil Engineering and Built Environment Liverpool John Moores University LiverpoolL3 3AF United Kingdom School of Architecture and Urban Planning Ministry of Education Chongqing University Chongqing400045 China Key Laboratory of New Technology for Construction of Cities in Mountain Area Ministry of Education Chongqing University Chongqing400045 China CMA Key Open Laboratory of Transforming Climate Resources to Economy Chongqing401147 China Mott MacDonald LeedsLS12 1BE United Kingdom

ISBN: (纸本)9789819784004

Semi-transparent building-integrated photovoltaic (BIPV) windows replace the external building glazing, allowing power to be generated as part of the existing building envelope. Due to the replacing of the BIPV windows, both thermal and optical performance of a building are worth exploring. This paper presents the results of a simulation study investigating the effects of applying BIPV windows to an existing office building within the UK, with a view to assessing the thermal performance, daylighting condition and BIPV electrical production capacity. It was considered on a typical floor of the office building, consisting of an open plan office space, two meeting rooms and a kitchen suite. A baseline scenario without BIPV window was modelled for comparison, giving recommendations to the viability of the BIPV window in terms of the overall performance. Basically, the use of the BIPV window resulted an annual cooling load reduction of 16.3% compared to the baseline. Heating loads were also reduced but only by 1.89%, while lighting energy usage was slightly increased due to a significantly worse performance in daylighting condition in the open plan office when applying the BIPV window. Specifically, daylight factor dropped over 50% in the office area, which, however, was already poor to begin with. The annual electricity generation of the BIPV window was little with only 902 kWh. This study can be a reference for future research on thermal and optical performance of high-rise office buildings using semi-transparent BIPV windows. © The Author(s) 2025.

关键词： Daylighting

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Learning Anatomical Semantics for Low-Dose Ct Denoising Via Pretrained Vision Models and Self-Supervised Learning

SSRN

引用

SSRN 2025年

作者： Hu, Chudi Wei, Meiyi Zhu, Yikun Chen, Gang The Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University No. 299 Bayi Road Wuchang District Hubei Province Wuhan430072 China

Background: In order to reduce radiation dose and improve the diagnostic accuracy of computed tomography (CT), numerous studies in recent years have explored deep learning-based image denoising methods to suppress noise and artifacts induced by photon starvation and electronic noise. However, most existing methods supervise the denoising process using normal-dose CT images as a reference, neglecting the differences in noise levels among various anatomical structures in CT images, which results in over-smoothing in the denoised images. Methods: In this paper, we propose an anatomical semantics-based low-dose CT denoising method that leverages anatomical semantics for fine-grained denoising. Specifically, our approach comprises two components: a conventional denoising network for image denoising and an anatomy-aware network for extracting anatomical semantic features to guide the denoising process. To obtain comprehensive anatomical semantics, we extract general semantic features by leveraging a pretrained vision model and capture dataset-specific semantic features through self-supervised contrastive learning. Furthermore, to ensure consistency between the general semantic features and the dataset-specific semantic features, we introduce a semantic alignment module that maps both the general semantic features and the dataset-specific semantic features into a unified feature space. Results: Experimental results demonstrate that, compared with existing methods, our approach exhibits superior denoising performance on two public ***: The low-dose CT denoising method based on anatomical semantics proposed in this study significantly improves denoising performance by incorporating general semantic features and dataset-specific semantic features, demonstrating promising potential for improving diagnostic accuracy in clinical practice, reducing patient radiation exposure, and advancing research in medical image processing. © 2025, The Authors. All rights rese

关键词： Self-supervised learning

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A neural kernel framework for relation extraction

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Expert Systems with Applications 2025年 286卷

作者： Wang, Kai Chen, Yanping Yang, Weizhe Huang, Ruizhang Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University China State Key Laboratory of Public Big Data Guizhou University China

Relation extraction (RE) poses significant challenges due to the complexity of identifying semantic relationships between overlapping entity pairs within sentences. Traditional kernel-based methods effectively leverage instance-level similarities but heavily depend on manually designed kernel functions, requiring substantial domain expertise and limiting their flexibility. On the other hand, neural network approaches excel at automatically learning abstract feature representations but typically neglect critical instance-specific information, potentially missing valuable relational details during inference. These limitations motivate the exploration of a hybrid approach that effectively integrates the strengths of traditional kernels with the representational power of neural networks. In this paper, we propose a neural kernel framework designed to bridge this critical gap. The proposed framework employs neural kernels, whose parameters are initialized based on task-specific objectives and optimized through neural training procedures, enabling adaptive learning of similarity measures without relying on handcrafted kernel functions. By maintaining instance-level details through annotated training examples, neural kernels create discriminative, flexible decision boundaries tailored specifically to the relation extraction task. We further develop three complementary neural kernel components, instance kernel, description kernel, and cluster kernel, to show the advantages of kernel substitution. Extensive experiments on the ACE 2005, SemEval 2010, and CoNLL 2004 datasets demonstrate that our neural kernel framework significantly outperforms existing state-of-the-art methods, achieving F1-scores of 88.11 %, 91.08 %, and 98.63 %, respectively. © 2025 Elsevier Ltd

关键词： Natural language processing systems

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Rsta: A Reversible Signal Transmission Architecture for Breaking the Limitation of Unidirectional Transmission

SSRN

引用

SSRN 2025年

作者： Liu, Xin Cui, Shuang Zhang, Xun Zhou, Fuxing Sheng, Kaikai Wang, Bin Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian116622 China

Molecular circuits are essential tools for biosensing and molecular information processing. However, the long-distance and reversible transmission of orthogonal signals remains a significant challenge in molecular circuits, preventing molecular signals from achieving reversible transmission similar to that of electrical signals in wires. To overcome the limitations of unidirectional signal transmission and facilitate the integration of antagonistic circuits. We propose a reversible signal transmission architecture (RSTA) with wire-like characteristics to achieve reversible signal transmission based on fundamental molecular devices known as reversible signal fan-out devices (RSFDs). The polarity of the input signal dictates the transmission direction, establishing the initial condition for the output signals. Cascaded RSFDs within RSTA offer two key advantages. First, the temporal response to signals. Second, the reversible transformation of opposite circuit types without structural modification. We experimentally validated the principles and functionality of RSTA using temporal logic device and integrated molecular circuits. This work advances the field of reversible molecular signal transmission, enabling the rational design of complex molecular circuits and molecular information processing systems. We envision applications of this technology in nanomachines, molecular computing, and biosensing. © 2025, The Authors. All rights reserved.

关键词： Temporal logic

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LLMDet: Learning Strong Open-Vocabulary Object Detectors under the Supervision of Large Language Models

arXiv

引用

arXiv 2025年

作者： Fu, Shenghao Yang, Qize Mo, Qijie Yan, Junkai Wei, Xihan Meng, Jingke Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Tongyi Lab Alibaba Group China Peng Cheng Laboratory China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology China China

Recent open-vocabulary detectors achieve promising performance with abundant region-level annotated data. In this work, we show that an open-vocabulary detector co-training with a large language model by generating image-level detailed captions for each image can further improve performance. To achieve the goal, we first collect a dataset, GroundingCap-1M, wherein each image is accompanied by associated grounding labels and an image-level detailed caption. With this dataset, we finetune an open-vocabulary detector with training objectives including a standard grounding loss and a caption generation loss. We take advantage of a large language model to generate both region-level short captions for each region of interest and image-level long captions for the whole image. Under the supervision of the large language model, the resulting detector, LLMDet, outperforms the baseline by a clear margin, enjoying superior open-vocabulary ability. Further, we show that the improved LLMDet can in turn build a stronger large multi-modal model, achieving mutual benefits. The code, model, and dataset is available at https://***/iSEE-laboratory/LLMDet. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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UAV-Assisted Joint Mobile Edge computing and Data Collection via Matching-Enabled Deep Reinforcement Learning

引用

IEEE Internet of Things Journal 2025年

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a non-convex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the UAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the data collection volume compared with other benchmark algorithms. © 2025 IEEE. All rights reserved,

关键词： Unmanned aerial vehicles (UAV)

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Graph Prompt Clustering

引用

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Guangdong University of Technology School of Information Engineering Guangzhou China South China Technology Commercialization Center Guangzhou China

Due to the wide existence of unlabeled graph-structured data (e.g. molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations, and seldom refer to the obvious differences in data distributions of distinct graph-level datasets. How to characteristically consider multiple graph-level datasets in a general well-designed model without prior knowledge is still challenging. In view of this, we propose a novel Graph Prompt Clustering (GPC) method. Within this model, there are two main modules, i.e., graph model pretraining as well as prompt and finetuning. In the graph model pretraining module, the graph model is pretrained by a selected source graph-level dataset with mutual information maximization and self-supervised clustering regularization. In the prompt and finetuning module, the network parameters of the pretrained graph model are frozen, and a groups of learnable prompt vectors assigned to each graph-level representation are trained for adapting different target graph-level datasets with various data distributions. Experimental results across six benchmark datasets demonstrate the impressive generalization capability and effectiveness of GPC compared with the state-of-the-art methods. © 1979-2012 IEEE.

关键词： Graph embeddings

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