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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Hao, Qiuyun Wang, FuQiang Zhang, Peng Ma, XiaoFeng Zhang, Jianqiang Zhao, Wei Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331507398

Acoustic information in confidential premise can be leaked through channels such as walls, pipes, doors and windows. Acoustic leakage level needs to be measured through specific indicators. Acoustic leakage evaluation indicator includes subjective evaluation indicator and objective evaluation indicator. The subjective evaluation indicator is a qualitative evaluation of acoustic leakage through subjective auditory perception of human ear, which can directly reflect the degree of acoustic leakage in confidential premise. At present, the objective evaluation indicator is mainly sound transmission loss (STL) to reflect objective degree of acoustic leakage in confidential premise. Therefore, it is relatively singular. Through study of Speech Transmission Index (STI), speech transmission index for public address systems (STIPA), which is a simplified form of FULL STI, is applied to acoustic leakage detection in this paper. STI has been proposed as an objective evaluation indicator and applied to acoustic leakage detection and acoustic information security. In order to confirm the effectiveness of objective evaluation indicator, a large number of related experimental studies have been conducted in the paper. Through experiments, it has been proven that STI can objectively evaluate level of acoustic leakage and level of acoustic information security in confidential premise. STI can better predict level of Chinese clarity under various complex transmission conditions. It provides a reliable basis for speech privacy of confidential premise and evaluation of architectural acoustic, sound reinforcement system and voice communication system. © 2024 IEEE.

关键词： Information leakage

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LCLB: Low Cross-Chain Transaction Ratio and Load Balancing for Blockchain Sharding 6

LCLB: Low Cross-Chain Transaction Ratio and Load Balancing f...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Wang, Yu Wu, Xiaoming Yuan, Qile Dong, Yunfeng Liu, Xiangzhi 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 Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798331541699

As blockchain technology becomes widely applied, handling high volumes of transactions has become a critical challenge. Sharding has emerged as an effective solution to enhance the throughput of blockchain systems. Yet, this approach introduces challenges related to the frequency of cross-shard transactions and workload balancing. High rates of cross-shard transactions can slow overall processing speed and reduce throughput, while uneven load distribution can overburden some shards and underutilize others, impacting overall efficiency. To address these issues, this paper proposes a sharding method named LCLB. This method employs the concept of label propagation to pre-shard accounts with frequent transactions into a designated shard, ensuring a lower ratio of cross-shard transactions. It also uses an elastic load balancing algorithm to dynamically adjust the distribution of accounts, ensuring a relatively balanced processing load across all shards. Our results indicate that LCLB significantly enhances blockchain sharding performance,not only enhancing transaction throughput but also significantly reducing transaction confirmation latency, while ensuring a balanced load across various shards. © 2024 IEEE.

关键词： Blockchain

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Implementation Analysis of Reachability Graph and Its Application in Net Learning

Implementation Analysis of Reachability Graph and Its Applic...

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Computer, Control and Robotics (ICCCR), International Conference on

作者： Mingjian Guang Chungang Yan Zihao Lu Rui Duan Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Reachability represents the most fundamental dynamic property of Petri nets. The reachability graph serves as a crucial tool for analyzing reachability, and its generation algorithm has garnered widespread attention. However, most of the existing work has focused primarily on the accuracy of the generation algorithm, often overlooking computational and time costs. Therefore, this paper concentrates on the implementation of reachability graphs and proposes three implementation methods: definition-based (DB), matrix-based (MB), and a fast generation method without self-loops (FG). Through comprehensive experiments, we evaluate and analyze the running speed and applicable scenarios of these three methods. Furthermore, we have successfully applied the proposed methods in net learning.

关键词： Digital control Costs Accuracy Heuristic algorithms Petri nets Benchmark testing Computational efficiency

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Security and privacy with opacity-based state observation for finite state machine

Security and privacy with opacity-based state observation fo...

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作者： Zhang, Zhipeng Xia, Chengyi Chen, Shengyong Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China

Topics on security and privacy are very fundamental and important in the Internet of Things (IoT). In this paper, we explore the impact of state observation on the opacity of IoT, which is modeled by Moore-type finite state machine. Firstly, to characterize the intrusion estimator under the event-state observation, a multi-linear algebraic expression is proposed through the Boolean semi-tensor product (STP), and on the basis of the novel representation, the validation criterion is given by some matrix manipulations. Subsequently, considering that the input information may not be measured due to the physical shielding of the system against external intruders, we continue to discuss the opacity with state observation by a permutation structure matrix. Meanwhile, the verification of opacity with event-state observation is generalized to the one with only state observation. Finally, we offer an example to illustrate the effectiveness of the newly proposed method. Taken together, the current results are helpful to provide a novel theoretical approach for investigating the privacy-preserving problem in the area of IoT and complex cyber-physical networks. © 2021 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.

关键词： Internet of things

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A Novel Hybrid Makeup Transfer Method Based on Latent Vector Manipulation 2

A Novel Hybrid Makeup Transfer Method Based on Latent Vector...

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2nd International Conference on Artificial Intelligence and Computer Information technology, AICIT 2023

作者： Li, Yumiao Li, Peng Shen, Ziqi Li, Wenhui Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China School of Computer Science Shaanxi Normal University Xi'an710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi'an710119 China Xi'an Key Laboratory of Cultural Tourism Resources Development and Utilization Xi'an710062 China

ISBN: (纸本)9798350302950

With the development of image processing technology and the improvement of people's living standards, there is a wide demand for makeup services in society. In this paper, we focus on style transfer and its application in automatic makeup generation, and propose a facial feature fusion recovery technique that utilizes an adaptive spatial feature fusion attention mechanism to solve the problem of inconsistent clarity between the reference image and the source image. By using the moving least squares method to calculate similarity on each sample image, the reference image set achieving the optimal makeup transfer effect can be efficiently selected. At the same time, the latent vector can be manipulated to achieve on-demand hybrid makeup generation and de-makeup image generation. By assigning different weights, the proportion of makeup in specific reference image in the final makeup effect can be controlled, and a new mixed makeup style can be obtained. The experiment shows that the proposed model can achieve state-of-the-art makeup transfer effect, and can also generate suitable makeup effects for low resolution input images. © 2023 IEEE.

关键词： Image enhancement

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OpenDrift: Online Evolving Fraud Detection for Open-Category and Concept-Drift Transactions

OpenDrift: Online Evolving Fraud Detection for Open-Category...

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IEEE International Conference on Web services (ICWS)

作者： Cheng Wang Songyao Chai Hangyu Zhu Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai China

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.

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A Sentiment Classification Model Based on Attention TIG-CNN-BiGRU

A Sentiment Classification Model Based on Attention TIG-CNN-...

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2023 International Conference on Culture-Oriented Science and technology, CoST 2023

作者： Li, Peng Liang, Tianmian An, Ning Zhang, Lichen Wu, Xiaojun Wang, Xiaoming Lu, Junling Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China School of Computer Science Shaanxi Normal University Xi'an710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi'an710119 China Xi'An Key Laboratory of Cultural Tourism Resources Development and Utilization Xi'an710062 China

ISBN: (纸本)9798350357998

The uniqueness of folk song expression forms and complex semantic relationships, and the existing models are not good at recognizing the emotion category of folk song lyrics, so an attention TIG-CNN-BiGRU based emotion classification model is proposed. That is, we first propose a text vector representation based on TF-IDF feature weighted pretrained Glove word embedding to extract important features by assigning different weights, and then combine CNN, BiGRU and attention mechanism to extract local features using multi-scale convolutional kernel CNN, BiGRU to capture semantic dependencies of long sequences and attention mechanism to assign high weights to key features to make the model have better lyrics recognition ability. Experiments comparing the model with various baseline methods on the lyrics dataset show that the model outperforms other lyrics sentiment classification models in terms of sentiment classification accuracy and various other metrics. © 2023 IEEE.

关键词： Deep neural networks

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ACDR-CRAFF Net: A Multi-Scale Network Based on Adaptive Channel and Coordinate Relational Attention Network for Remote Sensing Scene Classification

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IET Image Processing 2025年第1期19卷

作者： Dai, Wei Xu, Haixia Shi, Furong Yuan, Liming Wang, Xinyu Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

Accurate classification of remote sensing scene images is crucial for diverse applications, from environmental monitoring to urban planning. While convolutional neural networks (CNNs) have dramatically improved classification accuracy, challenges remain due to the complex distribution of small objects, varied spatial configurations, and intra-class multimodality in remote sensing images. In this work, we make three key contributions to address these challenges. (1) We propose the adaptive channel and coordinate relational attention network (ACDR-CRAFF), a novel multi-scale feature fusion framework designed to enhance feature representation across scales. (2) We introduce two innovative modules: the adaptive channel dimensionality reduction (ACDR) module, which dynamically adjusts channel representations to retain essential low-dimensional features, and the coordinate relational attention multi-scale feature fusion (CRAFF) module, which effectively captures and transfers spatial information between feature levels. (3) By integrating ACDR and CRAFF, our model achieves a progressive fusion of local to global features, ensuring robust feature expressiveness at multiple scales. Experimental results on four widely used benchmark datasets demonstrate that ACDR-CRAFF consistently outperforms several state-of-the-art methods, achieving significant improvements in classification accuracy and setting a new benchmark for complex remote sensing scene classification tasks. These results underscore the effectiveness of our approach in addressing the limitations of existing methods and advancing the state of the art in remote sensing image analysis. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Photointerpretation

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ni, Jinghui Cui, Hui Zhao, Lihai Li, Fengling Han, Xiaohui Xu, Lijuan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China Quan Cheng Laboratory China Shandong Open University China University of Technology Sydney Australia

ISBN: (纸本)9798350368741

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA. © 2025 IEEE.

关键词： Deep hashing image retrieval source-free domain adaptation

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Dynamic detection technology of encryption algorithm based on Pin 9

Dynamic detection technology of encryption algorithm based o...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Qu, Junpeng Du, Gaolei Sun, Yunge Fu, Yong Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Quan Cheng Laboratory Jinan250103 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

Aiming at the problem that malicious software uses encryption algorithm to hide its own malicious code and avoid detection, a dynamic detection technology based on Pin encryption algorithm is proposed. Use Pin to get the instruction flow of executable file running, then identify the function call in the instruction flow to get the function call framework, find the loop body in the calling function, and then carry out the encryption algorithm instruction detection and encryption algorithm constant detection on the loop body. If the detection passes, it can be considered that a component of the encryption algorithm is found. Then the complete encryption algorithm is found according to the generated function call framework. Experiments show that this method can effectively identify the encryption algorithms hidden in executable files. © 2024 IEEE.

关键词： Encryption algorithms

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