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arXiv 2024年

作者： Sun, Geng Wang, Yixian Sun, Zemin Wu, Qingqing Kang, Jiawen Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China School of Automation Guangdong University of Technology Guangzhou510006 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computing-intensive and latency-sensitive demands of users poses a significant challenge due to the limited resources of UAVs. To address this challenge, we present a multi-objective optimization approach for multi-UAV-assisted MEC systems. First, we formulate a multi-objective optimization problem aiming at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total amount of offloaded tasks by jointly optimizing task offloading, computation resource allocation, and UAV trajectory control. Since the problem is a mixed-integer non-linear programming (MINLP) and NP-hard problem which is challenging, we propose a joint task offloading, computation resource allocation, and UAV trajectory control (JTORATC) approach to solve the problem. However, since the decision variables of task offloading, computation resource allocation, and UAV trajectory control are coupled with each other, the original problem is split into three sub-problems, i.e., task offloading, computation resource allocation, and UAV trajectory control, which are solved individually to obtain the corresponding decisions. Moreover, the sub-problem of task offloading is solved by using distributed splitting and threshold rounding methods, the sub-problem of computation resource allocation is solved by adopting the Karush-Kuhn-Tucker (KKT) method, and the sub-problem of UAV trajectory control is solved by employing the successive convex approximation (SCA) method. Simulation results show that the proposed JTORATC has superior performance compared to the other benchmark methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Deep Fusion of Shifted MLP and CNN for Medical Image Segmentation

Deep Fusion of Shifted MLP and CNN for Medical Image Segment...

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

作者： Chengyu Yuan Hao Xiong Guoqing Shangguan Hualei Shen Dong Liu Haojie Zhang Zhonghua Liu Kun Qian Bin Hu Björn W. Schuller Yoshiharu Yamamoto Shlomo Berkovsky College of Computer and Information Engineering Henan Normal University China Key Laboratory of Artificial Intelligence and Personalized Learning in Education Henan China Big Data Engineering Lab of Teaching Resources & Assessment of Education Quality Henan China Centre for Health Informatics Australian Institute of Health Innovation Macquarie University Australia Key Laboratory of Brain Health Intelligent Evaluation and Intervention (BIT) Ministry of Education China School of Medical Technology Beijing Institute of Technology China School of Information Engineering Zhejiang Ocean University China GLAM – Group on Language Audio & Music Imperial College London U.K. Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan

Medical image segmentation is an important task in modern analysis of medical images. Current methods tend to extract either local features with convolutions or global features with Transformers. However, few of them are able to effectively fuse global and local features to facilitate segmentation. In this work, we propose a novel hybrid network that involves three main branches: the Multi-Layer Perception (MLP) branch, the Convolutional Neural Network (CNN) branch, and a Fusion branch. The MLP and CNN branches aim to learn global and local features, respectively. To fuse these, the fusion branch introduces a novel hierarchical fusion that performs multi-layered fusions that generate high-level representations to enhance segmentation. Our evaluation with two datasets shows strong performance of the proposed method compared to state-of-the-art baselines.

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Pareto-aware Neural Architecture Generation for Diverse Computational Budgets

arXiv

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arXiv 2022年

作者： Guo, Yong Chen, Yaofo Zheng, Yin Chen, Qi Zhao, Peilin Chen, Jian Huang, Junzhou Tan, Mingkui The School of Software Engineering South China University of Technology China Max Planck Institute for Informatics Germany The Weixin Group Tencent China The School of Computer Science The University of Adelaide Australia Tencent AI Lab Tencent China The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China The Pazhou Laboratory Guangzhou China

Designing feasible and effective architectures under diverse computational budgets, incurred by different applications/devices, is essential for deploying deep models in real-world applications. To achieve this goal, existing methods often perform an independent architecture search process for each target budget, which is very inefficient yet unnecessary. More critically, these independent search processes cannot share their learned knowledge (i.e., the distribution of good architectures) with each other and thus often result in limited search results. To address these issues, we propose a Pareto-aware Neural Architecture Generator (PNAG) which only needs to be trained once and dynamically produces the Pareto optimal architecture for any given budget via inference. To train our PNAG, we learn the whole Pareto frontier by jointly finding multiple Pareto optimal architectures under diverse budgets. Such a joint search algorithm not only greatly reduces the overall search cost but also improves the search results. Extensive experiments on three hardware platforms (i.e., mobile device, CPU, and GPU) show the superiority of our method over existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Pareto principle

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Research on the Loading Method of Logistics Vehicle and Cargo Based on IPSO Algorithm

Research on the Loading Method of Logistics Vehicle and Carg...

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第42届中国控制会议

作者： Zewei Zhao Huqing Wang Zhe Sun Lei Xu Guangfu Gong Xiangpeng Xie Zhixin Sun Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing University of Posts and Telecommunications Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and Telecommunications

Logistics vehicle and cargo matching problem is a typical combination optimization problem,which has important theoretical and application value in the field of logistics *** proved particle swarm optimization(PSO) algorithm is one of the most extensive optimization methods studied in recent years,with excellent global search ability and convergence *** on the IPSO algorithm,this paper proposes an effective IPSO algorithm model for the matching problem of logistics vehicles and goods,and conducts the corresponding comparative *** final experimental results show that the algorithm has good results in practical application and can provide a feasible optimization method for solving the problem of logistics vehicles and goods.

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Itg-Yolov7-Based Temperature Extraction Method for Group-Raised Pigs from Thermal Images

SSRN

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SSRN 2024年

作者： Liu, Xiaowen Zeng, Xueting Chu, Mengyuan Li, Qian Liu, Gang Li, Tao Ding, Xiangdong Mi, Yang Key Lab of Smart Agriculture Systems Ministry of Education China Agricultural University Beijing100083 China Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture and Rural Affairs of China China Agricultural University Beijing100083 China Henan Fengyuan Hepu Agricultural and Animal Husbandry Co. Ltd Xinyang464000 China College of Animal Science and Technology China Agricultural University Beijing100193 China National Engineering Laboratory for Animal Breeding Laboratory of Animal Genetics Breeding and Reproduction Ministry of Agriculture and Rural Affairs Beijing100193 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China College of Information and Electrical Engineering China Agricultural University China

Body temperature detection in pigs is vital for effective pig breeding, particularly in large-scale farming where traditional infrared thermal imaging methods typically measure only one pig at a time, leading to inefficient monitoring. This study introduces a novel approach for detecting the body temperature of group-raised pigs by leveraging an image super-resolution (SR) algorithm alongside an instantaneous posture recognition technique. The Porcine-ESRGAN was employed to preprocess thermal infrared images, addressing issues related to low resolution and improving the characterization of target areas. Additionally, an improved You Only Look Once v7 (YOLOv7) based on pig posture classification was proposed and named ITG-YOLOv7. ITG-YOLOv7 was developed to identify the instantaneous posture of pigs and measure their ear root or eye temperature. This approach effectively resolves the challenge of obtaining the temperatures of pigs in a lying position. The ITG-YOLOv7 model integrates multi-scale features through the Scale Sequence Feature Fusion (SSFF) module and optimizes parameters with the Generalized Efficient Layer Aggregation Network (GELAN). The incorporation of Shape-IoU as the anchor box loss function enhances bounding box regression accuracy, thus improving detection precision. Performance assessment of Porcine-ESRGAN using thermal infrared images before and after preprocessing. The processed images significantly improve the detection precision (P), recall (R), and mean average precision (mAP) for lying pigs by 3.1%, 11.5%, and 6.9%, indicating that the method is effective in enhancing the temperature detection accuracy for lying pigs. The proposed ITG-YOLOv7 model achieves an accuracy of 93.2%, a R of 95.4%, and an mAP of 97.5% with a model size of 61.9 MB when used in conjunction with the Porcine-ESRGAN model. By correlating maximum head temperatures with ear root and eye temperatures, the maximum head temperature was identified as the body temperature ind

关键词： Thermography (imaging)

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Decoupled Prototype Learning for Reliable Test-Time Adaptation

arXiv

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arXiv 2024年

作者： Wang, Guowei Ding, Changxing Tan, Wentao Tan, Mingkui the School of Electronic and Information Engineering South China University of Technology 381 Wushan Road Tianhe District Guangzhou510000 China the Pazhou Lab Guangzhou510330 China the School of Future Technology South China University of Technology 381 Wushan Road Tianhe District Guangzhou510000 China the School of Software Engineering the Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou510006 China

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. However, its performance is significantly affected by noisy pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss’s vulnerability to label noise. To address this issue, we propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation. First, we decouple the optimization of class prototypes. For each class prototype, we reduce its distance with positive samples and enlarge its distance with negative samples in a contrastive manner. This strategy prevents the model from overfitting to noisy pseudo-labels. Second, we propose a memory-based strategy to enhance DPL’s robustness for the small batch sizes often encountered in TTA. We update each class’s pseudo-feature from a memory in a momentum manner and insert an additional DPL loss. Finally, we introduce a consistency regularization-based approach to leverage samples with unconfident pseudo-labels. This approach transfers feature styles of samples with unconfident pseudo-labels to those with confident pseudo-labels. Thus, more reliable samples for TTA are created. The experimental results demonstrate that our methods achieve state-of-the-art performance on domain generalization benchmarks, and reliably improve the performance of self-training-based methods on image corruption benchmarks. The code will be released. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Order-preserving pattern mining with forgetting mechanism

arXiv

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arXiv 2024年

作者： Li, Yan Ma, Chenyu Gao, Rong Wu, Youxi Li, Jinyan Wang, Wenjian Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300400 China School of Artificial Intelligence Hebei University of Technology Tianjin300400 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Computer and Information Technology Shanxi University Taiyuan237016 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important;however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed. Copyright © 2024, The Authors. All rights reserved.

关键词： Miners

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MAINet: Modality-Aware Interaction Network for Medical Image Fusion

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Lisi Wei Libo Zhao Xiaoli Zhang College of Computer Science and Technology Jilin University China College of Artificial Intelligence and Big Data Hulunbuir University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Jilin University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

Due to the limitations of imaging sensors, obtaining a medical image that simultaneously captures both functional metabolic data and structural tissue details remains a significant challenge in clinical diagnosis. To address this, Multimodal Medical Image Fusion (MMIF) has emerged as an effective technique for integrating complementary information from multimodal source images, such as CT, PET, and SPECT, which is critical for providing a comprehensive understanding of both anatomical and functional aspects of the human body. One of the key challenges in MMIF is how to exchange and aggregate this multimodal information. This paper rethinks MMIF by addressing the harmony of modality gaps and proposes a novel Modality-Aware Interaction Network (MAINet), which leverages cross-modal feature interaction and progressively fuses multiple features in graph space. Specifically, we introduce two key modules: the Cascade Modality Interaction (CMI) module and the Dual-Graph Learning (DGL) module. The CMI module, integrated within a multi-scale encoder with triple branches, facilitates complementary multimodal feature learning and provides beneficial feedback to enhance discriminative feature learning across modalities. In the decoding process, the DGL module aggregates hierarchical features in two distinct graph spaces, enabling global feature interactions. Moreover, the DGL module incorporates a bottom-up guidance mechanism, where deeper semantic features guide the learning of shallower detail features, thus improving the fusion process by enhancing both scale diversity and modality awareness for visual fidelity results. Experimental results on medical image datasets demonstrate the superiority of the proposed method over existing fusion approaches in both subjective and objective evaluations. We also validated the performance of the proposed method in applications such as infrared-visible image fusion and medical image segmentation.

关键词： Multimodal fusion Medical image fusion Cascade modality interaction Modality-awareness Graph convolutional network

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A Relation-Constraint Link Prediction Model for Dynamic knowledge Graphs with Entity Drift

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ACM Transactions on knowledge Discovery from data 1000年

作者： Xiulin Zheng Peipei Li Zan Zhang Jia Wu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China and School of Computer Science and Information Engineering Hefei University of Technology China School of Computing Faculty of Science and Engineering Macquarie University Australia

knowledge Graphs (KGs) often suffer from incompleteness and this issue motivates the task of knowledge Graph Completion (KGC). Traditional KGC models mainly concentrate on static KGs with a fixed set of entities and relations, or dynamic KGs with temporal characteristics, faltering in their generalization to constantly evolving KGs with possible irregular entity drift. Thus, in this paper, we propose a novel link prediction model based on the embedding representation to handle the incompleteness of KGs with entity drift, termed as DCEL. Unlike traditional link prediction, DCEL could generate precise embeddings for drifted entity without imposing any regular temporal characteristic. The drifted entity is added into the KG with its links to the existing entity predicted in an incremental fashion with no requirement to retrain the whole KG for computational efficiency. In terms of DCEL model, it fully takes advantages of unstructured textual description, and is composed of four modules, namely MRC (Machine Reading Comprehension), RCAA (Relation Constraint Attentive Aggregator), RSA (Relation Specific Alignment) and RCEO (Relation Constraint Embedding Optimization). Specifically, the MRC module is first employed to extract short texts from long and redundant descriptions. Then, RCAA is used to aggregate the embeddings of textual description of drifted entity and the pre-trained word embeddings learned from corpus to a single text-based entity embedding while shielding the impact of noise and irrelevant information. After that, RSA is applied to align the text-based entity embedding to graph-based space to obtain the corresponding graph-based entity embedding, and then the learned embeddings are fed into the gate structure to be optimized based on the RCEO to improve the accuracy of representation learning. Finally, the graph-based model TransE is used to perform link prediction for drifted entity. Extensive experiments conducted on benchmark datasets in terms of evaluat

关键词： dynamic knowledge graph entity drift link prediction relation constraint

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Web-based Teamwork: Distributed Software Development Course under Covid-19

Web-based Teamwork: Distributed Software Development Course ...

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International Conference on Computer Science & education (ICCSE)

作者： Rui ZHANG Pedro Mestre Carlos Serodio Marília Prada Wenjuan GAO College of Computer Science and Technology Jilin University Key Lab of Symbolic Computation and Knowledge Engineering Chinese Ministry of Education Changchun China Centro Algoritmi University of Minho Guimaraes Portugal CITAB University of Tras-os-Montes and Alto Douro Vila Real Portugal Department of Social and Organizational Psychology Instituto Universitário de Lisboa (ISCTE-IUL) Lisboa Portugal Library of Changchun University Changchun China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

In a increasingly globalized world, it is very important to help students to acquire the hard and soft skills needed to cope with the challenges they will face when working in such environments. However, even though students find this subject interesting and volunteer for it, it is hard, even impossible to teach college students such a distributed fashion of teamwork. In this paper, a case study is provided to illustrate the methodology and experiences of a joint course, distributed software development, of multiple universities from Asia, Europe, North and South Americas. The results show that student can get the spirit, effective communication for such web-based teamwork environment and achieve software project development. The recent developments because of the Covid-19 pandemic are the facts that now students have to cooperate remotely with each other not only across continents but also inside their own city or country. It has increased the awareness of the need for this kind of training and effectively improved the interactions between team elements in different countries and improved the students' feeling of belonging to their team.

关键词： Software Software engineering Games Faces Teamwork Electronic mail Tools

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