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11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Li, Yunlong Jin, Huaiping Jin, Huaikang Cao, Yundong Kunming University of Science and Technology Faculty of Information Engineering and Automation Department of Automation Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Huaneng Renewables Co. Ltd. Yunnan Branch Kunming650000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Stable and reliable wind power forecasting is of great significance for grid dispatching. However, wind power has strong nonlinear characteristics. Its amount of historical data is very large, and traditional machine learning methods cannot fit the nonlinear relationship well. Furthermore, owing to the seasonality and periodicity of the wind power, the performance of the offline model inevitably deteriorates. To address these problems, this study proposes an adaptive ensemble model for ultra-short-term wind power forecasting. First, a long short-term memory (LSTM) network is established with historical wind power data for an entire year to ensure a strong generalization performance. Second, local weighted partial least squares (LWPLS) were used to obtain LSTM network prediction errors in real time by adaptive modeling and further improving its prediction accuracy. In addition, just-in-time learning (JITL) is used to ensure the adaptability of forecasting using LWPLS. Finally, to fully integrate the prediction advantages of different prediction models, a random forest (RF) was used to transform the determination of the ensemble weight into a three-classification problem. The adaptive fusion of the three different forecasting mechanisms is realized by weighting through the posterior probability of classification. Finally, the effectiveness and superiority of the proposed method are verified in a practical case. © 2022 IEEE.

关键词： Error correction

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Gender Identification for Coloring Black and White Portrait with cGan 10th

Gender Identification for Coloring Black and White Portrait ...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Liu, Qingyun Lin, Mugang Sun, Yaqi Zhang, Feng College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

This paper proposes a method to color black & white portrait images with gender recognition model and condition generation network (cGan). As a cGan model essentially, a method named pix2pix interprets image style transformation as a translation process from input pixels to target pixels. Lots of input to target image pairs should be prepared for model training for different application scenarios. When pix2pix is applied to color black & white portraits, there are obvious differences in color and saturation between portraits of different genders while the ability of gender recognition isnt owned by pix2pix in essence. Therefore, a gender recognition model is added to the network to produce more realistic and gender specific color portraits. The experiments show that our work has achieved better results than some other common methods. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Color

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Analysis and Design of Image Encryption Algorithms Based on Interlaced Chaos 10th

Analysis and Design of Image Encryption Algorithms Based on ...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Li, Kangman Li, Qiuping College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

For chaotic encryption algorithm, the algorithm of generating chaotic sequence is complex, and the data is float, which directly affects the speed of encryption. In this paper, an interlaced chaotic encryption algorithm is proposed, In this algorithm, the pixel interval is encrypted by chaotic sequence, and the chaotic sequences of other pixels are obtained by XOR the chaotic values of the front and back points, Experiments show that the algorithm can encrypt and decrypt images quickly and has good effect. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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A Image Adaptive Steganography Algorithm Combining Chaotic Encryption and Minimum Distortion Function 10th

A Image Adaptive Steganography Algorithm Combining Chaotic E...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Jiao, Ge Liu, Jiahao Zhou, Sheng Luo, Ning College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

Digital image has the characteristics of high redundancy and convenient processing, which is the ideal carrier of information hiding application. Therefore, digital image steganography has become a hot research direction in the field of information security. In order to ensure the concealment, reliability and security of image steganographic information, an image adaptive steganographic algorithm combining chaotic encryption and minimum distortion function is designed. The algorithm generates random keys, encrypt the secret information using Logistic and ChebyShev mapping, and then embeds the key and the encrypted secret information into the carrier image using the HILL steganographic algorithm. The algorithm stores the embedded key and the decryption key separately, so the attacker cannot get the correct secret information even if he gets the embedded key, which improves the security of the key. Experiments show that the pixel change rate of the algorithm is 6.76%, the peak signal-to-noise ratio is 59.51 db, and it has a very good anti-steganographic analysis ability. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

Domain Adaptive Code Completion via Language Models and Deco...

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Ze Tang Jidong Ge Shangqing Liu Tingwei Zhu Tongtong Xu Liguo Huang Bin Luo National Key Laboratory for Novel Software Technology Nanjing University China Nanyang Technological University Singapore Huawei Software Engineering Application Technology China Department of Computer Science Southern Methodist University USA

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose $k$ NM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that $k$ NM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that $k$ NM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.

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Epidemic Prevention System Based on Voice Recognition Combined with Intelligent Recognition of Mask and Helmet 3

Epidemic Prevention System Based on Voice Recognition Combin...

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3rd International Conference on Video, Signal and Image Processing, VSIP 2021

作者： Zhou, Huaming Wang, Aili Li, Meixin Zhao, Yinghui Iwahori, Yuji Heilongjiang Province Key Laboratory Of Laser Spectroscopy Technology And Application Harbin University Of Science And Technology Heilongjiang Harbin China Department Of Computer Science Chubu University Aichi487-8501 Japan

ISBN: (纸本)9781450385886

At present, COVID-19 cross-infection is easy to occur in dense places such as elevators. There are no epidemic prevention measures for construction site elevators on the market, and most of them require manual temperature measurement and reminders to wear masks and helmets to avoid the spread of the epidemic. This paper designs an intelligent epidemic prevention system for the elevator ride process in a modern construction site environment, which can achieve non-contact human temperature measurement, mask and helmet recognition and voice call elevator function. The system uses Arduino UNO as the control core, Kendryte K210 as machine vision processing module, non-contact infrared temperature sensor MLX90614, and voice recognition sensor LD3320. The system has the functions of non-contact temperature detection, mask/helmet recognition(YOLOv3) and voice call elevator. Experimental results showed that the recognition accuracy rate of helmet, mask, voice call elevator is 91.5%, 92.0% and 93.0% respectively. The temperature measurement accuracy rate is 0.2ĝ., which can effectively prevent the spread of the epidemic caused by contact and breathing, and has the advantages of stable, intelligent, and safe work. © 2021 ACM.

关键词： Temperature measurement

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A Grouping Strategy-Based Progressive Fusion Network for Hyperspectral Image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

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

作者： Guohua Lv Baodong Zhang Yongbiao Gao Guixin Zhao Guotao Wang Juncan Wang 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively.

关键词： Convolution Superresolution Feature extraction Acoustics Data mining Speech processing Hyperspectral imaging

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MyWAL:performance optimization by removing redundant input/output stack in key-value store

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Frontiers of Information technology & Electronic Engineering 2023年第7期24卷 980-993页

作者： Xiao ZHANG Mengyu LI Michael NGULUBE Yonghao CHEN Yiping ZHAO School of Computer Science Northwestern Polytechnical UniversityXi'an 710072China MIIT Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityXi'an 710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical UniversityXi'an 710072China School of Software Northwestern Polytechnical UniversityXi'an 710072China

Based on a log-structured merge(LSM)tree,the key-value(KV)storage system can provide high reading performance and optimize random writing *** is widely used in modern data storage systems like e-commerce,online analytics,and real-time *** LSM tree stores new KV data in the memory and flushes to disk in *** prevent data loss in memory if there is an unexpected crash,RocksDB appends updating data in the write-ahead log(WAL)before updating the ***,synchronous WAL significantly reduces writing *** this paper,we present a new WAL mechanism named *** directly manages raw devices(or partitions)instead of saving data on a traditional file *** can avoid useless metadata updating and write data sequentially on *** results show that MyWAL can significantly improve the data writing performance of RocksDB compared to the traditional WAL for small KV data on solid-state disks(SSDs),as much as five to eight times *** non-volatile memory express soild-state drives(NVMe SSDs)and non-volatile memory(NVM),MyWAL can improve data writing performance by 10%–30%.Furthermore,the results of YCSB(Yahoo!Cloud Serving Benchmark)show that the latency decreased by 50%compared with SpanDB.

关键词： key-value(KV)store Log-structured merge(LSM)tree Non-volatile memory(NVM) Non-volatile memory express soild-state drive(NVMe SSD) Write-ahead log(WAL)

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Research on Task Scheduling in Distributed Vulnerability Scanning System 10th

Research on Task Scheduling in Distributed Vulnerability Sca...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Jiang, Jie Tang, Sixin College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

Network threats caused by system vulnerabilities is increasing gradually, the distributed vulnerability scanning system can scan large-scale and complex networks and report the vulnerability information. Task scheduling is one of the core components in a distributed system. In this paper, we use dynamic optimization algorithms to improve the task scheduling efficiency of distributed vulnerability scanning system, we propose a PSO-based task scheduling scheme and improves the search ability of particles by adjusting algorithm parameters. We compared the time consume when using existing ‘Resource Aware Scheduling algorithm (RASA) with the basic particle swarm optimization (PSO) algorithm and the improved particle swarm optimization (IPSO) algorithm. Our results show that IPSO has better performance than other scheduling methods. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scheduling algorithms

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Multi-Scale Automated Self-Supervised Learning for Recommendation

Multi-Scale Automated Self-Supervised Learning for Recommend...

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Intelligent Management and Software Engineering (IMSE), International Conference on

作者： Yuhang Yan Liqin Tian Yujie Gao School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China School of Computer North China Institute of Science and Technology Yanjiao China

Automated self-supervised learning frameworks have shown significant results in the recommendation domain. However, existing methods such as AutoCF use graph convolutional networks and graph attention mechanisms that are limited to a single expression granularity and cannot fully capture the multi-granularity of user interests and product attributes. To address this problem, we propose MS-AutoCF, a multi-scale automated self-supervised learning recommender system that obtains user and product representations at different granularities through multi-layer graph convolution to match the global and local details of their interest preferences. Meanwhile, we design a query key value-based multi-head graph attention module that can adaptively aggregate and enhance the representations with different granularities. Experimental results show that the MS- AutoCF system achieves significant improvements over existing single-scale methods on several public datasets.

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