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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Zhang, Wen Chen, Jiaoyan Li, Juan Xu, Zezhong Pan, Jeff Z. Chen, Huajun Zhejiang University School of Software Technology Ningbo China The University of Manchester Department of Computer Science Manchester United Kingdom Zhejiang University College of Computer Science and Technology Hangzhou China The University of Edinburgh School of Informatics Edinburgh United Kingdom

ISBN: (纸本)9798331508821

Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry. Conventional KG reasoning based on symbolic logic is deterministic, with reasoning results being explainable, while modern embedding-based reasoning can deal with uncertainty and predict plausible knowledge, often with high efficiency via vector computation. A promising direction is to integrate both logic-based and embedding-based methods, with the vision of having advantages for both. It has attracted wide research attention with more and more works published in recent years. In this paper, we comprehensively survey these works, focusing on how logics and embeddings are integrated. We first briefly introduce preliminaries, then systematically categorize and discuss works of logic and embedding-aware KG reasoning from different perspectives, and finally conclude and discuss the challenges and further directions. © 2024 IEEE.

关键词： Knowledge graph

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The Progression of Competencies and Dispositions: How do Maturing software Engineers Compare? 54

The Progression of Competencies and Dispositions: How do Mat...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Clear, Tony Clear, Alison Auckland University of Technology Department of Computer Science and Software Engineering Auckand New Zealand School of Computing Eastern Institute of technology Auckland New Zealand

ISBN: (纸本)9798350351507

This research paper describes and extends the outcomes from an in- depth study investigating the difference in the expected skills requirements from junior software engineers to senior software engineers, and reflections on the findings from that study. It is a given that senior software engineers have more experience and skills than junior software engineers. However, a focus on their differing competencies and dispositions provides an enhanced mechanism for comparison. Gaps were identified in assessing 'professional knowledge' as categorized by the IEEE/ACM Computing Curriculum Overview Report (CC2020), and in assessing 'dispositions'. It appeared that the specific scenario of comparing the expected competencies between junior and senior software engineers, tested the framework for assessing competencies developed in the CC2020 project and applied in its mapping to the IEEE/ACM computer science (CS2013) approved curriculum. In this study into the difference between Junior and Senior software Engineers, an initial review of relevant literature was conducted. The review found that research analyzing job requirements for software engineers of different levels was limited;'experience' as a keyword was seldom mentioned;and a common distinction was made between 'soft' and 'hard' skills - the latter being skills that were 'technical', such as programming languages, frameworks, libraries, and tools, whereas soft skills referred to skills such as personality traits, attitudes, and teamwork skills. In our extension of that work the notion of soft skills was unpacked into professional skills and dispositions. The process of mapping from the CC2020 competency framework to the CS2013 curriculum had deliberately modelled how to represent a competency-based rather than a knowledge-based curriculum. The critical deficiency identified here was the limitation imposed by adopting a skills framework based on the cognitive taxonomy, and thereby unwittingly omitting the crucial compani

关键词： Mapping

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A survey for light field super-resolution

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High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Language-agnostic Zero-shot Entity Linking via Entity Prototype Network 15

Language-agnostic Zero-shot Entity Linking via Entity Protot...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Yang, Haihong Qin, Ming Zhang, Qiang Xu, Renjun Zhejiang University College of Computer Science and Technology Hangzhou China Zhejiang University School of Software Technology Hangzhou China Zhejiang University Center for Data Science Hangzhou China

ISBN: (纸本)9798331508821

Following the recent trend of language understanding, we introduce a novel approach for constructing a general-purpose entity linking system by learning entity prototypes. Our model, the Entity Prototype Network (EPN), generates language-agnostic entity prototypes by reading entity descriptions and aligning diverse surface forms in different languages to the corresponding entity prototype in a unified vector space. To validate our core assumption of learnable language-agnostic entity prototypes, we define a new Zero-shot Cross-lingual Entity Linking (ZXEL) task. Additionally, we propose a simple yet effective auxiliary task termed Entity Identification, which enhances our model's ability to capture both intra-entity similarity and inter-entity dissimilarity. We evaluate our model on the new task in both classic and generalized settings. Experimental results demonstrate consistent improvements (exceeding 30% on average) over competitive baselines. An ablation study confirms the necessity of our model design and highlights the impact of the proposed auxiliary task. © 2024 IEEE.

关键词： Zero-shot learning

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Joint Mobile Edge Caching and Pricing: A Mean-Field Game Approach 40

Joint Mobile Edge Caching and Pricing: A Mean-Field Game App...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Xu, Yin Zhang, Xichong Xiao, Mingjun Wu, Jie Liu, An Zhang, Sheng School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Temple University Department of Computer and Information Sciences Philadelphia United States School of Computer Science and Technology Soochow University Suzhou China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350317152

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines. © 2024 IEEE.

关键词： Game theory

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Local and Global Feature Interaction Network for Endoscope Image Classification 12th

Local and Global Feature Interaction Network for Endoscope I...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Dong, Zhengqi Xu, Benzhu Shi, Jun Zheng, Liping School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China

ISBN: (纸本)9783031463136

Convolutional Neural Network (CNN) shows great performance in the field of endoscopic image classification in past few years. It can capture local features of endoscopic images, but it fails to exploit global semantic information. Recently transformer is proposed and successfully applied in computer vision, which can model global long-range dependencies based on the self-attention mechanism. Considering the advantages of CNN and transformer, we propose a novel local and global feature interaction network for endoscope image classification. Different from the existed methods fusing CNN and transformer features, our method achieves these two kinds of features interaction during feature learning and further enhances the feature discrimination ability of CNN and transformer features. Specifically, our backbone consists of multiple CNN-FIB-Transformer (CFT) modules which includes CNN block, feature interaction block (FIB) and transformer encoder. Features extracted from CNN block and transformer encoder are fed into our proposed FIB in parallel, which can interact local and global features of endoscopic images through cross-attention mechanism and further improve their own feature representation capability. Furthermore, we also introduce the loss function of prediction alignment to guarantee the prediction consistency of CNN and transformer features, in addition of conventional cross-entropy loss functions for their own features. Experiments on two public endoscope image datasets (Kvasir2 and HyperKvasir) have demonstrated the feasibility and effectiveness of the proposed method for endoscope image classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Forecasting

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Energy-Efficient Task Offloading in UAV-Enabled MEC via Multi-agent Reinforcement Learning 18th

Energy-Efficient Task Offloading in UAV-Enabled MEC via Mu...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Gao, Jiakun Zhang, Jie Xu, Xiaolong Qi, Lianyong Yuan, Yuan Li, Zheng Dou, Wanchun School of Software Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China QingDao266580 China School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Zhongguancun Laboratory Beihang University Beijing100191 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819998951

Nowadays, artificial intelligence-based tasks are imposing increasing demands on computation resources and energy consumption. Unmanned aerial vehicles (UAVs) are widely utilized in mobile edge computing (MEC) due to maneuverability and integration of MEC servers, providing computation assistance to ground terminals (GTs). The task offloading process from GTs to UAVs in UAV-enabled MEC faces challenges such as workload imbalance among UAVs due to uneven GT distribution and conflicts arising from the increasing number of GTs and limited communication resources. Additionally, the dynamic nature of communication networks and workload needs to be considered. To address these challenges, this paper proposes a Multi-Agent Deep Deterministic Policy Gradient based distributed offloading method, named DMARL, treating each GT as an independent decision-maker responsible for determining task offloading strategies and transmission power. Furthermore, a UAV-enabled MEC with Non-Orthogonal Multiple Access architecture is introduced, incorporating task computation and transmission queue models. In addition, a differential reward function that considers both system-level rewards and individual rewards for each GT is designed. Simulation experiments conducted in three different scenarios demonstrate that the proposed method exhibits superior performance in balancing latency and energy consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Reinforcement learning

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PANDORA: Deep Graph Learning Based COVID-19 Infection Risk Level Forecasting

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IEEE Transactions on Computational Social Systems 2024年第1期11卷 717-730页

作者： Yu, Shuo Xia, Feng Wang, Yueru Li, Shihao Febrinanto, Falih Gozi Chetty, Madhu Dalian University of Technology School of Computer Science and Technology Dalian116024 China Rmit University School of Computing Technologies MelbourneVIC3000 Australia National Tsing Hua University Department of Mathematics Hsinchu30013 Taiwan Dalian University of Technology School of Software Dalian116620 China Institute of Innovation Science and Sustainability Federation University Australia BallaratVIC3353 Australia

Coronavirus disease 2019 (COVID-19) as a global pandemic causes a massive disruption to social stability that threatens human life and the economy. An effective forecasting system is arguably important to provide an early signal of the risk of COVID-19 infection so that the authorities are ready to protect the people from the worst. However, making a good forecasting model for infection risks in different cities or regions is not an easy task, because it has a lot of influential factors that are difficult to be identified manually. To address the current limitations, we propose a deep graph learning model, called PANDORA, to predict the infection risks of COVID-19, by considering all essential factors and integrating them into a geographical network. The framework uses geographical position relationships and transportation frequency as higher order structural properties formulated by higher order network structures (i.e., network motifs). Moreover, four significant node attributes (i.e., multiple features of a particular area, including climate, medical condition, economy, and human mobility) are also considered. We propose three different aggregators to better aggregate node attributes and structural features, namely, Hadamard, Summation, and Connection. Experimental results over real data show that PANDORA outperforms the baseline methods with higher accuracy and faster convergence speed, no matter which aggregator is chosen. © 2014 IEEE.

关键词： COVID-19

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Performance Based Scheduling in Distributed Mixed Criticality Systems

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IEEE Access 2025年 13卷 95321-95340页

作者： Ali, Amjad Wasly, Saud Khattak, Asad Masood Ali, Ihsan Iqbal, Shahid Hayat, Bashir University of Swat Department of Computer and Software Technology Khyber Pakhtunkhwa Swat Pakistan King Abdul Aziz University Electrical and Computer Engineering Department Jaddah Saudi Arabia Zayed University College of Technological Innovation United Arab Emirates Institute of Management Sciences School of Computer Science and IT Peshawar Pakistan

With a focus on computationally intensive, distributed, and parallel workloads, scheduling in mixed-criticality distributed systems presents significant challenges due to shared memory and resources, as well as the diverse demands of tasks. The system’s efficiency is heavily dependent on the overall scheduling duration (make span), while individual task deadlines impose strict timing constraints. When the tasks need to simultaneously access the shared memory, then these tasks interfere the execution of one another. For managing the scheduling of interfering tasks in distributed mixed-criticality systems, a novel Interference-Aware Partitioning Fixed Priority (IAP-FP) approach is proposed, which effectively handles task partitioning among cores while considering interference, ensuring better performance and adherence to critical deadlines. This method reduces task waiting times, which minimizes the scheduling duration and improving the schedulability of the task set. The novel proposed approach is compared with Mixed-Criticality Multicore Compositional Earliest Deadline First (MMC-EDF), Global and Mixed Criticality Partitioning (MC-Partitioning) approaches to show the efficiency. The proposed approach schedules 75% tasks while the MMC-EDF, Global and MC-Partitioning approaches schedules 65%, 0% and 18% tasks respectively for target utilization U=0.8. As the utilization of mixed criticality (MC) workload increases, the schedulability of MC task sets decreases, but still the proposed approach performs better than the MMC-EDF, Global and MC-partitioning approaches. © 2013 IEEE.

关键词： Embedded systems

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MSFDTA: Predicting drug target affinity based on multi-scale features and adaptive fusion 8

MSFDTA: Predicting drug target affinity based on multi-scale...

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8th International Conference on Electrical, Mechanical and computer Engineering, ICEMCE 2024

作者： Huang, Wanhua Tian, Xuechong Su, Ying Lv, Xiaoyi Xinjiang University Shuimogou School of Computer Science and Technology Xinjiang Urumqi China Xinjiang University School of Software Shaybak District Xinjiang Urumqi China

ISBN: (纸本)9798331506230

Accurate prediction of drug target affinity (DTA) is crucial for drug development and drug repurposing. However, most deep learning-based methods base their predictions only on the molecular structure information of the drug or target or the network information of its interactions. This may neglect the specificity of different scale features thus reducing the effectiveness of the experiment. In this study, we propose a multiscale approach that combines the molecular structure scale and interaction network scale of drugs and targets to capture feature information, and then obtain composite features through a feature adaptive fusion module. Our experimental results on two benchmark datasets show that the proposed MSFDTA outperforms the state-of-the-art methods, and the significant improvement of DTA prediction performance by combining multiscale feature information is demonstrated by ablation experiments. © 2024 IEEE.

关键词： Targeted drug delivery

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