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9th International Conference on Fuzzy Systems and Data Mining, FSDM 2023

作者： Ma, Chi Li, Huikai Hu, Hui Li, Jingyan Wu, Jie School of Computer Science and Engineering Huizhou University Huizhou516007 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781643684703

Crop diseases and pests are major threats to crop yield and quality, as well as global food security and agricultural livelihoods. Therefore, it is essential to identify crop pests and diseases in a timely, efficient and accurate manner. In this paper, we propose MConvNeXt, a crop pest classification model based on multi-scale feature fusion, which adds a multi-scale module to the original ConvNeXt network model to better capture feature information at different scales. We experimentally analyzed the MConvNeXt model on the IP102 dataset, and the results show that it outperforms the baseline network and other classical image classification models in pest classification. © 2023 The authors and IOS Press.

关键词： Classification (of information)

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Control Action Types - Patterns of Applied Control for Self-adaptive Systems (Summary)

Control Action Types - Patterns of Applied Control for Self-...

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2024 software engineering, SE 2024

作者： Kröher, Christian Gerling, Lea Schmid, Klaus University of Hildesheim Institute of Computer Science Software Systems Engineering Universitätsplatz 1 Hildesheim31141 Germany

ISBN: (纸本)9783885797371

This paper summarizes [KGS23], which received the Best Paper award at the 18th Symposium on software engineering for Adaptive and Self-Managing Systems (SEAMS23) in 2023. A Self-adaptive System (SaS) modifies its behavior according to changes in its environment autonomously. Distributed control and central control represent two complementary paradigms to establish this capability. Both of these approaches come with respective advantages and disadvantages. A promising approach to minimize these negative aspects is to combine them to achieve the best of both paradigms. In this paper, we present patterns for integrating distributed and central control, which we call Control Action Types (CATs). Each CAT describes a unique type of interaction between a central controller and a distributed controlled SaS to achieve a desired adaptation. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Decentralized control

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A Cooperative Multi-AUV Mobile Scheme with Optical Communication via DDPG Approach 29

A Cooperative Multi-AUV Mobile Scheme with Optical Communica...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Mengzhen Luo, Hanjiang Li, Xiang Ruby, Rukhsana Tao, Hang Wu, Kaishun Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Hong Kong

ISBN: (纸本)9798350330717

To implement high-speed wireless communication from the deep ocean to the sea surface leveraging multiple autonomous underwater vehicles (AUVs) with underwater wireless optical communication (UWOC) technique is an emerging and promising technology that enables real-time data collection for accurate underwater exploration and monitoring, e.g., coordinated moving targets tracking. However, multi-hop UWOC is more susceptible to beam misalignment and positional uncertainty caused by external interference in the harsh marine environments. To address these challenges, we design a cooperative movement scheme for multiple AUVs based on deep reinforcement learning (DRL) approach to realize robust and reliable optical communication under mobile target tracking scenarios. In this scheme, we first model the optical channel with optical noise and then analyze the link performance of multiple AUVs to meet the bit error rate (BER) requirements. Afterwards, we map the cooperative optical communication problem to a Markov decision process (MDP) by incorporating the extended Kalman filter (EKF) technique to enhance effective communication. Finally, we propose a cooperative control strategy for multiple mobile AUVs based on deep deterministic policy gradient (DDPG). Through extensive simulations, it is demonstrated that the proposed algorithm is effective in achieving reliable underwater optical communication under mobile scenarios. © 2023 IEEE.

关键词： Autonomous Underwater Vehicles Deep Deterministic Policy Gradient Extended Kalman Filter Underwater Wireless Optical Communication

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Comparing the intensity of variability changes in software product line evolution (Summary)

Comparing the intensity of variability changes in software p...

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2024 software engineering, SE 2024

作者： Kröher, Christian Gerling, Lea Schmid, Klaus University of Hildesheim Institute of Computer Science Software Systems Engineering Universitätsplatz 1 Hildesheim31141 Germany

ISBN: (纸本)9783885797371

This paper summarizes [KGS23], published in the Journal of Systems & software. software Product Line (SPL) evolution affects multiple kinds of artifacts like code, build, and variability model. Only part of these artifacts are relevant to variability. We studied the intensity (frequency and amount) in which developers change variability information in our earlier work. Applying this to the Linux kernel revealed that changes to variability information occur infrequently and only affect small parts of the analyzed artifacts. Here, we extend our previous work to a comparative evolution analysis of four SPLs. We provide a detailed analysis of the individual intensities of variability changes by applying our updated approach to each SPL. A comparison of these results confirms our findings of infrequent and small changes to variability information in our previous study. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Change Intensity Evolution analysis software product line evolution Variability changes

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Unpaired 3D Shape-to-Shape Translation via Gradient-Guided Triplane Diffusion

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3376-3386页

作者： Zhang, Wenxiao Rahmani, Hossein Liu, Jun Hohai University College of Computer Science and Software Engineering Nanjing211100 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom

Unpaired shape-to-shape translation refers to the task of transforming the geometry and semantics of an input shape into a new shape domain without paired training data. Previous methods utilize GAN-based architectures to perform shape translation, employing adversarial training to transform the source shape encoding into the target domain in the low-dimensional latent feature space. However, these methods encounter difficulties in generating diverse and high-quality results, as they often suffer from issues such as "mode collapse". This leads to limited generation diversity and makes it challenging to find an accurate latent code that adequately represents the input shape. In this paper, we achieve unpaired shape-to-shape translation via a triplane diffusion model, in which we factorize 3D objects into triplane representations and conduct a diffusion process on these representations to accomplish shape domain transformation. We observe that by adding an appropriate amount of noise to an input object during the forward diffusion process, domain-specific shape structures are smoothed out while the overall structure is still preserved. Subsequently, we progressively remove the noise via an unconditional diffusion model trained on the target shape domain in the reverse diffusion process. This allows us to obtain a denoised output that retains the structural similarities of the source input while aligning with the distribution of the target shape domain. During this process, we propose two gradient-based guidance mechanisms to guide the translation process to guarantee more faithful results during the denoising process. We conduct extensive experiments on different shape domains, and the experimental results demonstrate that our method achieves superior shape fidelity with high quality compared to current state-of-the-art baselines. © 1995-2012 IEEE.

关键词： Signal encoding

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KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration

KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driv...

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

作者： Li, Chengyuan Zhou, Suyang Kong, Jieping Qi, Lei Xue, Hui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350368741

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods. © 2025 IEEE.

关键词： Prompt Learning Vision-Language Models Zero-shot Anomaly Detection

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A Novel Data Mining Framework for Discovering Association Rules in Multivariate Time Series 7

A Novel Data Mining Framework for Discovering Association Ru...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Wang, Shengwei Lyu, Jianhua Zhang, Baili School Of Software Southeast University Nanjing China College Of Computer Science And Engineering Southeast University Nanjing China

ISBN: (纸本)9798350361445

As a significant field within data mining, association rule mining plays a pivotal role in unveiling relationships between items within a dataset. Existing approaches in multivariate time series (MTS) association rule mining primarily emphasize the generation of association rules by combining features from different variables, but neglect to adequately consider the temporal relationship between these features and ignore the trend information of variables, resulting in an incomplete capture of the essential characteristics inherent in MTS data. In this paper, we introduce a novel framework for association rule mining in multivariate time series. First, we discretize the multivariate time series into the symbolic sequence using trend-based symbolic aggregate approximation(TSAX). Then, a rule mining method is introduced to discover association rules that consider the temporal relationship among symbols of different variables. The results of experiments demonstrate the discovery of significant rules within the 'Weather&Traffic' dataset which gives the traffic conditions in France depending on the weather. Further, we also provide the outcomes of the sensitivity analysis conducted on our method, illustrating that significant rules can be mined by adjusting the parameters of TSAX to different settings. The experimental results highlight the utility of our proposed framework in discovering temporal association rules within multivariate time series datasets. © 2024 IEEE.

关键词： Association rules

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Underwater Image Enhancement Method Based on CGAN and Parallel Attention Mechanism 3

Underwater Image Enhancement Method Based on CGAN and Parall...

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3rd International Conference on Neural Networks, Information and Communication engineering, NNICE 2023

作者： Ma, Chi Yang, Le Hu, Hui Chen, Yuenai Bu, Anxu The School of Computer Science and Engineering Huizhou University Huizhou516007 China The School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9798350335972

Due to the absorption and scattering of light in water, the underwater image has some problems such as colour distortion, serious colour difference and ambiguity, which seriously affects the detection of underwater resources and object recognition. At present, the latest underwater image enhancement methods are all based on deep learning, but they do not take into account the diversity of channel information and pixel features. In order to achieve better enhancement effect, a parallel attention mechanism combining Channel Attention and Pixel Attention is introduced based on Conditional Generative Adversarial Network consisting of multi-scale generator and double discriminator. Compared with other latest methods, the enhancement effect of the proposed method is closer to the real image, and the necessity of each module of the proposed model is proved by ablation study. © 2023 IEEE.

关键词： Pixels

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EdgeMob: A Context-Aware Dynamic Service Migration for Wearable IoT in Healthcare 1

EdgeMob: A Context-Aware Dynamic Service Migration for Weara...

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1st International Conference on Computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Raza, Imran Hussain, Syed Asad Latif, Sana COMSATS University Islamabad Lahore Department of Computer Science Lahore Pakistan Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9798350351736

Edge Computing (EC) is a distributed network architecture offering computation and storage resources at the network edge, addressing cloud computing limitations by optimizing latency and enhancing data safety and privacy for real-time services. It also supports intelligent solutions for IoT-based smart healthcare systems through Deep Learning (DL). DL aids in resource allocation optimization and disease prediction. However, the surge in wearable device usage introduces challenges in user mobility and migration, leading to increased latency and service inconsistencies. To tackle this, we propose EdgeMob, an intelligent and context-aware scheme deployed as a federation of edge servers. EdgeMob utilizes Q-learning-based reinforcement learning for personalized service migration decisions based on user mobility and Quality of Service (QoS). We evaluate our system against existing solutions, considering parameters such as user-perceived latency, migration cost, and network bandwidth utilization. © 2024 IEEE.

关键词： Electronic health record

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of computer science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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