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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Xia, Hong Wang, Bo Zhang, Yongkang Chen, Yanping School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing China Xi 'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9798350327380

In order to solve the problem that the similarity method used in software module clustering can produce arbitrary decision, and the description matrix of dendrogram generated by base clustering in hierarchical clustering combination does not consider more characteristics of entities, a hierarchical clustering combination method based on intuitionistic fuzzy similarity (HCC-IFS) was proposed. In the process of entity similarity calculation, the intuitionistic fuzzy set theory and similarity calculation method are used to provide membership and non-membership attributes for each entity, so as to improve the merging efficiency and solve arbitrary decision problems. In the description matrix representation of base clustering, the absolute and relative features obtained from the dendrogram are used to construct the description matrix, and the generated description matrix is combined. Finally, the proposed algorithm is verified by experiments. Experimental results show that HCC-IFS can produce better clustering results and improve the recovery quality of software architecture. © 2023 IEEE.

关键词： Fuzzy set theory

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Leveraging Catastrophic Forgetting to Develop Safe Diffusion Models against Malicious Finetuning 38

Leveraging Catastrophic Forgetting to Develop Safe Diffusion...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Pan, Jiadong Gao, Hongcheng Wu, Zongyu Hu, Taihang Su, Li Huang, Qingming Li, Liang Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China University of Chinese Academy of Sciences China The Pennsylvania State University United States Nankai University China

Diffusion models (DMs) have demonstrated remarkable proficiency in producing images based on textual prompts. Numerous methods have been proposed to ensure these models generate safe images. Early methods attempt to incorporate safety filters into models to mitigate the risk of generating harmful images but such external filters do not inherently detoxify the model and can be easily bypassed. Hence, model unlearning and data cleaning are the most essential methods for maintaining the safety of models, given their impact on model parameters. However, malicious fine-tuning can still make models prone to generating harmful or undesirable images even with these methods. Inspired by the phenomenon of catastrophic forgetting, we propose a training policy using contrastive learning to increase the latent space distance between clean and harmful data distribution, thereby protecting models from being fine-tuned to generate harmful images due to forgetting. The experimental results demonstrate that our methods not only maintain clean image generation capabilities before malicious fine-tuning but also effectively prevent DMs from producing harmful images after malicious fine-tuning. Our method can also be combined with other safety methods to maintain their safety against malicious fine-tuning further. WARNING: This paper contains offensive images generated by models. © 2024 Neural information processing systems foundation. All rights reserved.

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A Fine-Grained Image Description Generation Method Based on Joint Objectives 18th

A Fine-Grained Image Description Generation Method Based on...

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18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Zhang, Yifan Lin, Chunzhen Cao, Donglin Lin, Dazhen Artificial Intelligence Department Xiamen University Xiamen361005 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China

ISBN: (纸本)9789819996360

The goal of fine-grained image description generation techniques is to learn detailed information from images and simulate human-like descriptions that provide coherent and comprehensive textual details about the image content. Currently, most of these methods face two main challenges: description repetition and omission. Moreover, the existing evaluation metrics cannot clearly reflect the performance of models on these two issues. To address these challenges, we propose an innovative Fine-grained Image Description Generation model based on Joint Objectives. Furthermore, we introduce new object-based evaluation metrics to more intuitively assess the model’s performance in handling description repetition and omission. This novel approach combines visual features at both the image level and object level to maximize their advantages and incorporates an object penalty mechanism to reduce description repetition. Experimental results demonstrate that our proposed method significantly improves the CIDEr evaluation metric, indicating its excellent performance in addressing description repetition and omission issues. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image analysis

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Chinese fable generation based on large scale pretrained language models 3

Chinese fable generation based on large scale pretrained lan...

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3rd International Symposium on Computer Applications and Information Systems, ISCAIS 2024

作者： Jing, Rong Wan, Fucheng Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of China's Ethnic Languages and Intelligent Processing of Gansu Province Northwest Minzu University Gansu Lanzhou China

ISBN: (纸本)9781510681316

Story generation is an important part of natural language processing, text generation tasks have received extensive attention from scholars in recent years. Existing methods are difficult to generate complete, logically fluent texts with low repetition rate based on a given outline vocabulary. To solve this problem, based on the dataset generated from outline conditional stories, we use the latest GLM model and instruct-tuning to generate Chinese fables and compare it with other big model fine-tuning with different parameter settings, and the experimental results show that the fables generated by Instruct-tuning have stronger logic and lower repetition rate. The models we used showed an increase of 1.5%, 2.43%, 1.51%, 3.25%, and 2.09% over the baseline model on the Blue-2, distinct-3, distinct-2, distinct-3, and distinct-4 metrics. © 2024 SPIE.

关键词： Natural language processing systems

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An ultra-wideband high-isolation dual-port MIMO antenna design

An ultra-wideband high-isolation dual-port MIMO antenna desi...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Cao, Shuangqing Li, Minquan Zi, Zhenfa Li, Zhonghui Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China Hefei Normal University School of Physics and Materials Engineering Hefei230601 China

ISBN: (纸本)9798350355581

In this paper, an antenna for ultra-wideband multiple-input and multiple-output (UWB-MIMO) is designed. The design comprises two hexagonal monopole antennas. In order to achieve ultra-wideband characteristics and mitigate the coupling, a stepped microstrip line structure for impedance matching and T-shaped defective ground structure are employed, which optimizes the broadband characteristics and achieves high isolation characteristics. The antenna achieves a wide impedance bandwidth of 3.5 GHz to 13.9 GHz (relative bandwidth of 119%). Furthermore, the isolation between the two feed ports of the antenna exceeds 18 dB, the envelope correlation coefficient (ECC) is less than 0.012, and it exhibits favorable radiation characteristics. © 2024 IEEE.

关键词： Bandwidth

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Lightweight Food Image Recognition With Global Shuffle Convolution

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 392-402页

作者： Sheng, Guorui Min, Weiqing Yao, Tao Song, Jingru Yang, Yancun Wang, Lili Jiang, Shuqiang Ludong University Department of Information and Electrical Engineering Yantai264025 China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Consumer behaviors and habits in food choices impact their physical health and have implications for climate change and global warming. Efficient food image recognition can assist individuals in making more environmentally friendly and healthier dietary choices using end devices, such as smartphones. Simultaneously, it can enhance the efficiency of server-side training, thereby reducing carbon emissions. We propose a lightweight deep neural network named Global Shuffle Net (GSNet) that can efficiently recognize food images. In GSNet, we develop a novel convolution method called global shuffle convolution, which captures the dependence between long-range pixels. Merging global shuffle convolution with classic local convolution yields a framework that works as the backbone of GSNet. Through GSNet's ability to capture the dependence between long-range pixels at the start of the network, by restricting the number of layers in the middle and rear, the parameters and floating operation operations (FLOPs) can be minimized without compromising the performance, thus permitting a lightweight goal to be achieved. Experimental results on four popular food recognition datasets demonstrate that our approach achieves state-of-the-art performance with higher accuracy and fewer FLOPs and parameters. For example, in comparison to the current state-of-the-art model of MobileViTv2, GSNet achieved 87.9% accuracy of the top-1 level on the Eidgenössische Technische Hochschule Zürich (ETHZ) Food-101 dataset with 28% reduction in the parameters, 37% reduction in the FLOPs, but a 0.7% more accuracy. © 2024 IEEE.

关键词： Carbon dioxide

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in Data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on Big Data computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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Scheduling optimization for upstream dataflows in edge computing

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Digital Communications and Networks 2023年第6期9卷 1448-1457页

作者： Haohao Wang Mengmeng Sun Lianming Zhang Pingping Dong Yehua Wei Jing Mei College of Information Science and Engineering Hunan Normal UniversityChangsha410081China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China Hunan Xiangjiang Artificial Intelligence Academy Hunan Normal UniversityChangsha410081China

Edge computing can alleviate the problem of insufficient computational resources for the user equipment,improve the network processing environment,and promote the user *** computing is well known as a prospective method for the development of the Internet of Things(IoT).However,with the development of smart terminals,much more time is required for scheduling the terminal high-intensity upstream dataflow in the edge server than for scheduling that in the downstream *** this paper,we study the scheduling strategy for upstream dataflows in edge computing networks and introduce a three-tier edge computing network *** propose a Time-Slicing Self-Adaptive Scheduling(TSAS)algorithm based on the hierarchical queue,which can reduce the queuing delay of the dataflow,improve the timeliness of dataflow processing and achieve an efficient and reasonable performance of dataflow *** experimental results show that the TSAS algorithm can reduce latency,minimize energy consumption,and increase system throughput.

关键词： Edge computing Time-slicing Dataflow scheduling Dynamic analysis

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Boundary Cue Guidance and Contextual Feature Mining for Glass Segmentation 48

Boundary Cue Guidance and Contextual Feature Mining for Glas...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Xiao, Qiquan Zhang, Yuan Li, Xuanya Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9781728163277

Glass is ubiquitous in the real world, and its perception has many applications, including robot navigation and drone tracking. However, due to the transparent property of glass, the interior of a glass area can be any surrounding scene or object, which brings challenges for computer vision. Inspired by the human senses, boundary cues are one of the crucial factors for people to judge the location of glass contours. Hence, we propose a boundary cue guidance and contextual feature mining network (BCNet) to accurately and efficiently segment glass. Specifically, we first design a multi-branch boundary extraction module (MBEM) for learning accurate boundary cues combined with multi-level encoded features. Second, we propose a boundary cue guidance module (BCGM), inject the boundary cues into the representation learning, and provide constraints with object structure semantics to guide feature extraction. Besides, we design a contextual feature mining module (CFMM) to dynamically capture the contextual information of different receptive fields for the detection of different sizes and shapes of the glass. Finally, extensive experiments on two benchmark glass datasets, GDD and GSD. The results demonstrate that our BCNet achieves state-of-the-art segmentation performance against existing methods. © 2023 IEEE.

关键词： Boundary cue guidance Contextual feature Convolutional neural network Glass segmentation

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Lightweight-Based Defect Detection for Small Target Insulators

Lightweight-Based Defect Detection for Small Target Insulato...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Liu, Shuxin Zhang, Lei Shi, Chengcheng Qin, Shuhan Ji, Guanjun Wang, Xiaodi School of Electrical Engineering Shanghai Dianji University Shanghai201306 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Fujian354300 China

ISBN: (纸本)9783031694820

Identifying defects in insulators, especially in small targets, remains a significant challenge due to the diverse types of insulators and complex transmission line backgrounds. This paper aims to enhance the accuracy of defect detection in insulators while minimizing the model’s parameter count for stable power system operation. To achieve this, we propose improvements to the feature fusion mechanism in the YOLOv8 network. These enhancements focus on better integrating multi-scale feature information, particularly emphasizing the detection sensitivity towards small targets. Additionally, these modifications lead to a reduction in the model’s parameter count while improving the detection accuracy of small targets. Furthermore, we introduce the SIOU loss function to the improved model, further enhancing its accuracy. Through experiments, our improved model achieves a mean average precision (mAP) of 89.2%, surpassing YOLOv8n by 2.7%. Remarkably, the model’s parameter count is significantly reduced to 2,796,249, demonstrating a balance between higher model accuracy and fewer parameters. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Insulating materials

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