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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Zhitao Yu Peng Sun Mingle Zhou Qianlong Liu Shilong Zhao Hisense Video Technology Co. Ltd Qing Dao China Hisense Visual Technology Co. Ltd Qing Dao China 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In vehicle liquid crystal display (LCD) technology has attracted much attention for its wide range of applications in automotive infotainment systems. However, conventional LCD technologies have limitations in terms of energy consumption and display quality, especially in the context of the increasing demand for high contrast and low energy consumption. To address these challenges, this paper carries out a study of LCD technology based on an area dynamic dimming algorithm. The technology significantly improves the contrast ratio of LCDs and effectively reduces energy consumption by dividing the backlight module into multiple independently adjustable regions and dynamically adjusting the backlight brightness of each region according to the image content. The results show that the region dynamic dimming algorithm proposed in this paper can effectively adjust the brightness of the backlight according to the image content, achieving higher display contrast and lower energy consumption.

关键词： Energy consumption Heuristic algorithms Brightness Light emitting diodes Liquid crystal displays Robustness Entropy Energy efficiency Vehicle dynamics Optimization

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A Secure keyword Spotting Scheme Based on Complex Residual network

A Secure Keyword Spotting Scheme Based on Complex Residual N...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Jingyi Chen Xiangyu Gao Peijia Zheng Huiyu Zhou Jian Li Dong Wei Jianbin Zou School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Nanning China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Amazon Web Services Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

To address the privacy risks associated with intelligent voice assistants, this paper proposes a non-interactive, end-to-end keyword spotting scheme in the encrypted domain. The scheme introduces an innovative complex-valued residual neural network (CR-NN) and leverages low-degree polynomial approximations to enable encrypted computation of complex activation functions. By supporting single instruction multiple data (SIMD) operations, the scheme significantly enhances computational efficiency while reducing ciphertext size. Experimental results demonstrate that the proposed method outperforms existing approaches across multiple performance metrics, balancing efficient keyword spotting with robust privacy protection and offering new insights for the application of deep learning in the encrypted domain.

关键词： Deep learning Measurement Privacy Single instruction multiple data Image processing Personal voice assistants Polynomials Cryptography Protection Residual neural networks

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A New and Efficient IT Knowledge Exchange Platform 2nd

A New and Efficient IT Knowledge Exchange Platform

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Sha, Jinting Yu, Wendong Wei, Hongwei Yuan, Lin Li, Zhujun Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China Shandong Tianyi Information Technology Co. Ltd. Shandong Jinan250101 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China

ISBN: (纸本)9789819619061

As information technology continues to advance, an increasing number of technicians are joining the IT field. However, the technology in this field is changing very rapidly, requiring many technicians to constantly learn new knowledge to keep pace with developments. Therefore, in this context, the creation of a user-friendly IT technology exchange platform is highly valuable, so that technicians can easily learn and interact with each other. This platform adopts Vue 2 as the front-end framework, utilizes Spring Boot for the back-end development, Elasticsearch for full-text search capabilities, MySQL for data storage, Spring security for authentication and security, and MyBatis for data persistence. Users can register accounts, create and publish articles and resources, and also like or bookmark articles and resources. In addition, the platform also supports features such as full-text search, article categorization, resource classification and so on. Testing has demonstrated that this IT knowledge exchange platform meets the basic needs of its users. At the same time, the platform offers robust security measures to protect user privacy and ensure data security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： IT knowledge exchange platform Spring Boot Vue 2

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Data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

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Data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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Design and Implementation of Takeaway Ordering Recommendation System Based on Python and Flask 1

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Cao, Junjie Yu, Wendong Wei, Hongwei Yuan, Lin Li, Zhujun Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China Shandong Tianyi Information Technology Co. Ltd. Shandong Jinan250101 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China

ISBN: (数字)9789819619047

ISBN: (纸本)9789819619030

With the rapid development of the economy, people are getting busier and busier, and it is difficult to find time to cook, so ordering takeout or dining at restaurants has become the norm. In the 2020 work report, the government proposed to fully implement the "Internet +" process, which also indicates the arrival of the advantages of the digital economy. Therefore, it has become an inevitable trend to use Internet technology to change our daily life and production methods. Driven by this trend, the catering industry has begun to move towards informatization. In order to make it more convenient for merchants and customers to meet their respective needs and save time and energy, a takeaway ordering recommendation system has emerged. This paper uses a collaborative filtering recommendation algorithm to recommend personalized foods that users may like, so as to provide more convenient services for catering services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bottles

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A Natural Language Processing Approach for Instruction Set Architecture Identification

arXiv

引用

arXiv 2022年

作者： Sahabandu, Dinuka Mertoguno, Sukarno Poovendran, Radha The Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195-2500 United States The Institute for Information Security & Privacy Georgia Institute of Technology North Avenue AtlantaGA30332 United States

Binary analysis of software is a critical step in cyber forensics applications such as program vulnerability assessment and malware detection. This involves interpreting instructions executed by software and often necessitates converting the software’s binary file data to assembly language. The conversion process requires information about the binary file’s target instruction set architecture (ISA). However, ISA information might not be included in binary files due to compilation errors, partial downloads, or adversarial corruption of file metadata. Machine learning (ML) is a promising methodology that can be used to identify the target ISA using binary data in the object code section of binary files. In this paper we propose a binary code feature extraction model to improve the accuracy and scalability of ML-based ISA identification methods. Our feature extraction model can be used in the absence of domain knowledge about the ISAs. Specifically, we adapt models from natural language processing (NLP) to i) identify successive byte patterns commonly observed in binary codes, ii) estimate the significance of each byte pattern to a binary file, and iii) estimate the relevance of each byte pattern in distinguishing between ISAs. We introduce character-level features of encoded binaries to identify fine-grained bit patterns inherent to each ISA. We use a dataset with binaries from 12 different ISAs to evaluate our approach. Empirical evaluations show that using our byte-level features in ML-based ISA identification results in an 8% higher accuracy than the state-of-the-art features based on byte-histograms and byte pattern signatures. We observe that character-level features allow reducing the size of the feature set by up to 16x while maintaining accuracy above 97%. © 2022, CC BY.

关键词： Extraction

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Detecting Unknown HTTP-based Malicious Communication Behavior via Generated Adversarial Flows and Hierarchical Traffic Features

arXiv

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arXiv 2023年

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

Malicious communication behavior is the network communication behavior generated by malware (botnet, spyware, etc.) after victim devices are infected. Experienced adversaries often hide malicious information in HTTP traffic to evade detection. However, related detection methods have inadequate generalization ability because they are usually based on artificial feature engineering and outmoded datasets. In this paper, we propose an HTTP-based Malicious Communication traffic Detection Model (HMCD-Model) based on generated adversarial flows and hierarchical traffic features. HMCD-Model consists of two parts. The first is a generation algorithm based on WGAN-GP to generate HTTP-based malicious communication traffic for data enhancement. The second is a hybrid neural network based on CNN and LSTM to extract hierarchical spatial-temporal features of HTTP-based traffic. In addition, we collect and publish a dataset, HMCT-2020, which consists of large-scale malicious and benign traffic during three years (2018-2020). Taking the data in HMCT-2020(18) as the training set and the data in other datasets as the test set, the experimental results show that the HMCD-Model can effectively detect unknown HTTP-based malicious communication traffic. It can reach F1 ≈ 98.66% in the dataset HMCT-2020(19-20), F1 ≈ 90.69% in the public dataset CIC-IDS-2017 and F1 ≈ 83.66% in the real traffic, which is 20+% higher than other representative methods on average. This validates that HMCD-Model has the ability to discover unknown HTTP-based malicious communication behavior. © 2023, CC0.

关键词： HTTP

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HSTF-Model: an HTTP-based Trojan Detection Model via the Hierarchical Spatio-Temporal Features of Traffics

arXiv

引用

arXiv 2023年

作者： Xie, Jiang Li, Shuhao Yun, Xiaochun Zhang, Yongzheng Chang, Peng Institute of Information Engineering Chinese Academy of Sciences Beijing China Key Laboratory of Network Assessment Technology University of Chinese Academy of Sciences 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

HTTP-based Trojan is extremely threatening, and it is difficult to be effectively detected because of its concealment and confusion. Previous detection methods usually are with poor generalization ability due to outdated datasets and reliance on manual feature extraction, which makes these methods always perform well under their private dataset, but poorly or even fail to work in real network environment. In this paper, we propose an HTTP-based Trojan detection model via the Hierarchical Spatio-Temporal Features of traffics (HSTF-Model) based on the formalized description of traffic spatio-temporal behavior from both packet level and flow level. In this model, we employ Convolutional Neural network (CNN) to extract spatial information and Long Short-Term Memory (LSTM) to extract temporal information. In addition, we present a dataset consisting of Benign and Trojan HTTP Traffic (BTHT-2018). Experimental results show that our model can guarantee high accuracy (the F1 of 98.62%∼99.81% and the FPR of 0.34%∼0.02% in BTHT-2018). More importantly, our model has a huge advantage over other related methods in generalization ability. HSTF-Model trained with BTHT-2018 can reach the F1 of 93.51% on the public dataset ISCX-2012, which is 20+% better than the best of related machine learning methods. © 2023, CC BY.

关键词： HTTP

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MedRE-DeBGPR: A Novel Model for Medical Relation Extraction Based on DeBERTa, GlobalPointer and RoPE

SSRN

引用

SSRN 2024年

作者： Deng, Zhiqi Luo, Xudong Gong, Shutao Liu, Jinlu School of Computer Science and Engineering Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China

In medical text analysis, extracting relationships among medical entities is crucial for enhancing clinical decision-making and building comprehensive medical knowledge graphs. This paper proposes a novel model for Relation Extraction (RE) from medical texts, which combines the strengths of DeBERTa (a pre-trained language model known for capturing complex linguistic phenomena) and GPLinker (a framework adept at handling overlapping and nested entities). By integrating these two technologies, our model effectively manages the inherent complexities of medical language, such as domain-specific terminology, complex sentence structures, and nested relationships. The proposed model uses the Fast Gradient Method (FGM) for adversarial training to improve robustness, Efficient GlobalPointer for parameter usage optimisation, and Rotary Position Embedding (RoPE) for better entity recognition. Experimental results on the CMedCausal dataset demonstrate that our approach outperforms existing baseline models and large language models in extracting entity relationships from medical documents. This model has significant implications for developing clinical decision support systems and medical knowledge graphs, and it holds possibility for adaptation across different languages and medical domains. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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