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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Chen, Tianyu Li, Zhixin Zhang, Canlong Ma, Huifang Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin541004 China College of Computer Science and Engineering Northwest Normal University Lanzhou730070 China

ISBN: (纸本)9783030891879

Image captioning methods with attention mechanism are leading this field, especially models with global and local attention. But there are few conventional models to integrate the relationship information between various regions of the image. In this paper, this kind of relationship features are embedded into the fused attention mechanism to explore the internal visual and semantic relations between different object regions. Besides, to alleviate the exposure bias problem and make the training process more efficient, we combine Generative Adversarial network with Reinforcement Learning and employ the greedy decoding method to generate a dynamic baseline reward for self-critical training. Finally, experiments on MSCOCO datasets show that the model can generate more accurate and vivid image captioning sentences and perform better in multiple prevailing metrics than the previous advanced models. © 2021, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Probability-based Global Cross-modal Upsampling for Pansharpening

Probability-based Global Cross-modal Upsampling for Pansharp...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zeyu Zhu Xiangyong Cao Man Zhou Junhao Huang Deyu Meng Xi'an Jiaotong University School of Computer Science and Technology Xi'an Jiaotong University Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University Nanyang Technological University Macau University of Science and Technology

Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods used in these approaches only utilize the local information of each pixel in the low-resolution multispectral (LRMS) image while neglecting to exploit its global information as well as the cross-modal information of the guiding panchromatic (PAN) image, which limits their performance improvement. To address this issue, this paper develops a novel probability-based global cross-modal upsampling (PGCU) method for pan-sharpening. Precisely, we first formulate the PGCU method from a probabilistic perspective and then design an efficient network module to implement it by fully utilizing the information mentioned above while simultaneously considering the channel specificity. The PGCU module consists of three blocks, i.e., information extraction (IE), distribution and expectation estimation (DEE), and fine adjustment (FA). Extensive experiments verify the superiority of the PGCU method compared with other popular upsampling methods. Additionally, experiments also show that the PGCU module can help improve the performance of existing SOTA deep learning pansharpening methods. The codes are available at https://***/Zeyu-Zhu/PGCU.

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A FinBERT Framework for Sentiment Analysis of Chinese Financial News

A FinBERT Framework for Sentiment Analysis of Chinese Financ...

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computer Technology and information Science (ISCTIS), International Symposium on

作者： Yuesheng Huang Renyi Lin Jiawen Li Yiqi Li Jiajia Chen Jiaqi Liang Guanyuan Feng Jujian Lv School of Computer Science Guangdong Polytechnic Normal University Guangzhou China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China School of Finance and Economics Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In the era of big data, sentiment analysis of the Chinese stock market has become a research hotspot. As a vital reflection of market sentiment, financial news has a significant impact on stock market trends. In this paper, we propose a framework based on the FinBERT pre-trained model optimized for financial news sentiment analysis in China. In particular, the model utilizes the Bidirectional Encoder Representations from Transformers (BERT) to deal with semantics, syntactic features, and specialized vocabulary of financial texts through self-supervised learning. The fine-tuning stage further optimizes the model, so that it can accurately predict sentiment tendencies. Experimental results show that the proposed model outperforms others on the sentiment classification task, with an accuracy of up to 94.52%. Consequently, the model can provide participants in the financial industry with a valuable reference of market trends, which helps investors and analysts make decisions properly.

关键词： Analytical models Sentiment analysis Vocabulary Accuracy Bidirectional control Market research Transformers Data models Encoding Stock markets

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security Analysis of Thumbnail-Preserving Image Encryption and a New Framework

arXiv

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arXiv 2025年

作者： Xie, Dong Li, Zhiyang Guo, Shuangxi Chen, Fulong Hu, Peng Anhui Provincial Key Laboratory of Network and Information Security The School of Computer and Information Anhui Normal University Wuhu241002 China

As a primary encryption primitive balancing the privacy and searchability of cloud storage images, thumbnail preserving encryption (TPE) enables users to quickly identify the privacy personal image on the cloud and request this image from the owner through a secure channel. In this paper, we have found that two different plaintext images may produce the same thumbnail. It results in the failure of search strategy because the collision of thumbnail occurs. To address this serious security issues, we conduct an in-depth analysis on the collision probabilities of thumbnails, and then propose a new TPE framework, called multi-factor thumbnail preserving encryption (MFTPE). It starts from the collision probability of two blocks, extend to the probabilities of two images and ultimately to N images. Then, we in detail describe three specific MFTPE constructions preserving different combinations of factors, i.e., the sum and the geometric mean, the sum and the range, and the sum and the weighted mean. The theoretical and experimental results demonstrate that the proposed MFTPE reduces the probability of thumbnails, exhibits strong robustness, and also effectively resists face detection and noise attacks. Copyright © 2025, The Authors. All rights reserved.

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SPM-GCN: An adaptive reordering algorithm for sparse LU factorization via GCN

SPM-GCN: An adaptive reordering algorithm for sparse LU fact...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Huazeng Liu Junjie Wang Qi Liu Wei Du Zanjun Zhang Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shaanxi Key Laboratory of Large Scale Electromagnetic Computing Xidian University Xi’an China

Sparse LU factorization is a critical kernel in scientific computing and engineering applications. A better nonzero pattern of sparse matrixes can accelerate LU factorization by reordering. Traditionally it’s difficult to predict which non-zero pattern is optimal for a sparse matrix. In this paper, we proposed a graph convolutional neural network (GCN) for adaptively selecting the optimal reordering algorithm from five candidate reordering algorithms in the Matrix preprocessing step, referred to as SPM-GCN. After using our SPM-GCN, the average numerical factorization time outperformed the other five algorithms, and the average numerical factorization time was reduced by 17.3% compared with the default method of swSuperLU.

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SiamIRPN: Siamese Visual Tracking with Improved Region Proposal networks

SiamIRPN: Siamese Visual Tracking with Improved Region Propo...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： Cao, Dun Dai, Renhua Zhao, Tianyu Alqahtani, Fayez Tolba, Amr Sherratt, R. Simon Zhu, Min Changsha University of Science and Technology School of Computer and Communications Engineering Hunan Changsha China Nanjing University of Posts and Telecommunications Ministry of Education Key Lab of Broadcast Wireless Communication and Sensor Network Technology China King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia The University of Reading School of Biomedical Engineering RG6 6AY United Kingdom Zhejiang Shuren University College of Information Science and Technology Zhejiang31000 China

ISBN: (纸本)9798350301113

Though introducing the Region Proposal network (RPN) from object detection enabled Siamese trackers' success, RPN-based trackers still struggle in challenging scenarios. We posit that the reason comes from two major limitations of the introduced RPN, where one is that the external structure of the RPN is simple and straight, the other is that the internal components used in RPN cannot cope with complex scenes. In this paper, we propose an Improved RPN (IRPN) suitable for visual tracking. Externally, we place a Convolutional Block Attention Mechanism (CBAM) to the IRPN, and internally, we adopt the advanced component groups for the IPRN. Using multiple IRPN blocks and deep architecture, we propose a Siamese tracker (SiamIRPN) having a layer-wise structure. Comprehensive experiments and ablation studies on five benchmarks (VOT-2019, OTB100, UAV123, GOT-10k, and NFS) show our proposed SiamIRPN achieves competitive performance. © 2023 IEEE.

关键词： Object detection

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A Method Based on Hierarchical Spatiotemporal Features for Trojan Traffic Detection

arXiv

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arXiv 2023年

作者： Xie, Jiang Li, Shuhao Zhang, Yongzheng Yun, Xiaochun Li, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China Key Laboratory of Network Assessment Technology IIE CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

Trojans are one of the most threatening network attacks currently. HTTP-based Trojan, in particular, accounts for a considerable proportion of them. Moreover, as the network environment becomes more complex, HTTP-based Trojan is more concealed than others. At present, many intrusion detection systems (IDSs) are increasingly difficult to effectively detect such Trojan traffic due to the inherent shortcomings of the methods used and the backwardness of training data. Classical anomaly detection and traditional machine learning-based (TML-based) anomaly detection are highly dependent on expert knowledge to extract features artificially, which is difficult to implement in HTTP-based Trojan traffic detection. Deep learning-based (DL-based) anomaly detection has been locally applied to IDSs, but it cannot be transplanted to HTTP-based Trojan traffic detection directly. To solve this problem, in this paper, we propose a neural network detection model (HSTF-Model) based on hierarchical spatiotemporal features of traffic. Meanwhile, we combine deep learning algorithms with expert knowledge through feature encoders and statistical characteristics to improve the self-learning ability of the model. Experiments indicate that F1 of HSTF-Model can reach 99.4% in real traffic. In addition, we present a dataset BTHT consisting of HTTP-based benign and Trojan traffic to facilitate related research in the field. © 2023, CC BY.

关键词： Anomaly detection

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LDfuzz: A Directed Greybox Fuzzer for Solidity Smart Contract

LDfuzz: A Directed Greybox Fuzzer for Solidity Smart Contrac...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Jiangtao Liao Huidan Hu Keke Huang Huasong Jin Changlu Lin College of Computer and Cyber Security Fujian Normal University School of mathematics and statistics Fujian Normal University Key Laboratory of Analytical Mathematics and Applications (Ministry of Education) Fujian Normal University Fujian Zhong Xin Network Security Information Technology Co. LTD Fuzhou Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Smart contracts are programmable units that possess the ability to execute a wide range of computational tasks, operating on a decentralized ledger called a blockchain, frequently employed for the management of valuable digital assets. Unlike conventional programs, once smart contracts are deployed, they are immutable and cannot be altered. As the value associated with smart contracts increases, they become increasingly enticing targets for potential attackers. It is therefore crucial to conduct comprehensive testing of smart contracts prior to their deployment. Fuzzing is an important testing approach. Regrettably, existing coverage-based fuzzing tools treat all covered code in the same manner and cannot perform extensive tests on specific code fragments. In this research, we present LDfuzz, a targeted greybox fuzzer explicitly developed for Ethereum smart contracts. Its primary goal is to generate inputs that efficiently navigate towards potentially suspicious program locations. We propose suspicious branch marked, an innovative approach that assesses branch-level security implications. Utilizing the suggested metrics as a foundation, we calculate the distance of each fuzzing inputs to the marked branch and formulate a power scheduling algorithm based on simulated annealing, which progressively allocates additional energy to seeds in proximity to the suspicious branch while diminishing energy for seeds that are more distant. We assess the efficacy of LDfuzz by conducting a comparative analysis with a leading fuzzer for smart contracts, benchmarking their respective performance. The results from our experimentation findings demonstrate that LDfuzz exhibits superior efficiency compared to current advanced tools in identifying bugs within real-world contracts. Moreover, LDfuzz excels beyond current tools in achieving broader branch coverage.

关键词： Schedules Codes Scheduling algorithms Navigation Smart contracts Simulated annealing Fuzzing

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GraphFlow: A Fast and Accurate Distributed Streaming Graph Computation Model 30

GraphFlow: A Fast and Accurate Distributed Streaming Graph C...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Liang, Zheheng Zheng, Yingying Bi, Sheng Yao, Chaosheng Wang, Jiayan Xu, Lijie Ji, Shuping Wang, Wei Duan, Shikai Information Center Guangdong Power Grid Limited Liability Company Guangzhou China Joint Laboratory on Cyberspace Security China Southern Power Grid Guangzhou China State Key Lab of Computer Science at Iscas China University of Cas Beijing China Guangzhou Power Supply Bureau Guangzhou China University of Toronto Toronto Canada Nanjing Institute of Software Technology China University of Cas Nanjing China Ant Group-Payment Business Group Hangzhou China

ISBN: (纸本)9798331515966

Streaming graph computation has been widely applied in many fields, e.g., social network analysis and online product recommendation. However, existing streaming graph computation approaches still present limitations on accuracy and efficiency. To improve the accuracy, some distributed systems use the sequential graph update method based on an incremental computation model. However, these systems cannot handle the dynamic graph update concurrently. The speculation-based parallel updating model can parallelize the graph computation, however, it is restricted due to ignoring the original messages when updating a graph. Streaming graph computation usually requires high accuracy and low latency. As such, it is challenging to utilize incremental computation while simultaneously supplying concurrent processing *** overcome these challenges, in this paper, we first analyze a number of classical graph algorithms and summarize three principles that graph algorithms should satisfy in streaming scenarios. Based on these principles, we propose GraphFlow, a streaming graph computation model. GraphFlow achieves fast and accurate computation by utilizing incremental state update and propagation. To reduce the impact of concurrent update conflicts, GraphFlow provides a fine-grained lock based parallel update strategy. We implement GraphFlow framework and evaluate its performance and concurrent update conflict probability on real-world datasets. Meanwhile, we compare GraphFlow with two existing representative graph processing systems. Experimental results show GraphFlow achieves low latency and outperforms other graph processing systems given large datasets. © 2024 IEEE.

关键词： Data flow graphs

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Research on Optimization and Scheduling of MPTCP Data networks in GNSS network Reference Stations

Research on Optimization and Scheduling of MPTCP Data Networ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Yuxin Wang Hoiio Kong Dexin Ma Xiyuan Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Global Navigation Satellite System network Reference Stations (GNSS-NRS) are pivotal in modern po-sitioning and navigation applications. However, GNSS-NRS face significant communication challenges, including instability during rapid user movement and inconsistent coverage by different operators, which often results in data transmission interruptions and delays. Moreover, the conventional Multipath Transmission Control Protocol (MPTCP) scheduling strategy does not accommodate the diversity of the data types managed by GNSS-NRS. This default approach treats all data uniformly, even under deteriorating network conditions. To overcome these limitations, this paper introduces a ‘Multiple Reception’ strategy based on MPTCP that ensures timely and reliable GNSS data transmission. This novel strategy differentiates between observational and control data based on their real-time criticality and applies tailored transmission techniques. Under adverse conditions, it utilizes multiple transmission paths and selectively receives control data, thus prioritizing the transfer of critical information. We conducted simulation experiments using MiniNet to compare the ‘Multiple Reception’ method against conventional and redundant strategies, demonstrating that it significantly reduces data loss rates in GNSS-NRS.

关键词： Global navigation satellite system Protocols Real-time systems Delays MPTCP Reliability Data communication Kernel Optimization Faces

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