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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Dang, Yuemei Qiu, Chen Gao, Feng Gu, Jinguang Zhang, Lihua Arman, Ghazaleh School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Software College East China Jiaotong University Jiangxi Nanchang China Urmia University of Technology Faculty of Information Technology and Computer Engineering Urmia Iran

ISBN: (纸本)9781665457033

Entity Alignment is the task of seeking entities with the same semantics from distinct knowledge graphs (KGs). Researchers have recently proposed Graph Convolutional Network (GCN) based approaches to achieve this task. Most existing GCN-based methods embed all the relations into an identical value, making the underlying loss on the diversity of the relationship. Relation semantics of entities and relation structure have benefitted the entity alignment task in the monolingual scenarios. This paper proposes a Relation-Aware Graph Convolutional Network with a Differential Adjacency Matrix (A-REA), which exploits the relation-specific semantics and structural information of entities in the KGs to promote the entity representations. The experiments on DBP-15K and DWY-100K datasets, including three cross-lingual subsets and two cross-knowledge graph sub-datasets, reveal that our approach leads to advance in entity alignment task in both the cross-lingual and cross-knowledge KGs. Discriminating the relation shows the effective signal for entity alignment, especially for the schema heterogeneity of different KGs. © 2023 IEEE.

关键词： Knowledge graph

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Brain Tumor Augmentation using the U-NET Architecture

Brain Tumor Augmentation using the U-NET Architecture

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Jabbar, Mohsin Siddiqui, Maria Hussain, Farhan Daud, Sultan Nust College of E&me Department of Computer and Software Engineering Islamabad Pakistan Namal University Department of Computer Science Mianwali Pakistan

ISBN: (纸本)9798350345933

Studies have found out that tumors in brain are one of the fiercest diseases which can ultimately lead to death. Gliomas are the most commonly found primary tumors that are very hard to predict and can be found anywhere in the brain. It is prime objective to differentiate the different tumor tissues such as enhancing tissues, edema, from healthy ones. To do this task, two types of segmentation techniques come into existent i.e. manual and automatic. The automation methods of brain tumor segmentation have gained ground over manual segmentation algorithms and further its estimation is very closer to clinical results. In this paper we propose a comprehensive U-NET architecture with modification in their layers for 2D slices segmentation as a major contribution to BRATS 2015 challenge. Then we enlisted different dataset that are available publicly i.e. BRATS and DICOM. Further, we present a robust frame- work inspired from U-NET model with addition and modification of layers and image pre-processing methodology such as contrast enhancement for visible input and output details. In this way our approach achieves highest dice score 0.92 on the publicly available BRATS 2015 dataset and with better time constraint i.e. training time decreases to 80-90 minute instead of previously 2 to 3 days. © 2022 IEEE.

关键词： Tumors

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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Class-wise Adaptive Logits Distillation with Meta-Learning

Class-wise Adaptive Logits Distillation with Meta-Learning

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

作者： Xiao Huang Wu Chen Wei Zhou College of Computer and Information Science College of Software Southwest University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Knowledge distillation has become a crucial technique for transferring intricate knowledge from a teacher model to a smaller student model. While logit-based knowledge distillation has shown promise, existing methods often overlook the efficient distillation of logits. In this paper, we introduce a novel approach called Class-wise Adaptive Logits Distillation (CALD) based on meta-learning. Our method leverages a meta-network to generate class-adaptive weights, delivering both explicit and implicit knowledge adaptively. By training the meta-network to assign higher weights to specific classes crucial for the student model’s learning from the teacher model, our approach enhances the knowledge transfer process. Experimental results on CIFAR-100 and ImageNet datasets demonstrate that CALD surpasses state-of-the-art knowledge distillation methods, achieving enhanced accuracy and efficiency in transferring knowledge from teacher to student models.

关键词： Metalearning Training Adaptation models Accuracy Signal processing Acoustics Speech processing Knowledge transfer

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KGCODE-Tab Results for SemTab 2022

KGCODE-Tab Results for SemTab 2022

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2022 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, SemTab 2022

作者： Li, Xinhe Wang, Shuxin Zhou, Wei Zhang, Gongrui Jiang, Chenghuan Hong, Tianyu Wang, Peng School of Computer Science and Engineering Southeast University China College of Software Engineering Southeast University China Chien-Shiung Wu College Southeast University China

This paper presents the results of KGCODE-Tab in the tabular data to knowledge graph matching contest SemTab 2022. As an efficient tabular data linking system, KGCODE-Tab is intended to participate in three tasks of the content: Column Type Annotation (CTA), Cell Entity Annotation (CEA), and Columns Property Annotation (CPA). The specific techniques used by KGCODE-Tab will be introduced briefly. The strengths and weaknesses of KGCODE-Tab will also be discussed. © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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In-Range Farthest Point Queries and Related Problem in High Dimensions 49

In-Range Farthest Point Queries and Related Problem in High ...

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49th EATCS International Conference on Automata, Languages, and Programming, ICALP 2022

作者： Huang, Ziyun Xu, Jinhui Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States

ISBN: (纸本)9783959772358

Range-aggregate query is an important type of queries with numerous applications. It aims to obtain some structural information (defined by an aggregate function F(·)) of the points (from a point set P) inside a given query range B. In this paper, we study the range-aggregate query problem in high dimensional space for two aggregate functions: (1) F(P ∩ B) is the farthest point in P ∩ B to a query point q in d and (2) F(P ∩ B) is the minimum enclosing ball (MEB) of P ∩ B. For problem (1), called In-Range Farthest Point (IFP) Query, we develop a bi-criteria approximation scheme: For any ϵ > 0 that specifies the approximation ratio of the farthest distance and any γ > 0 that measures the "fuzziness" of the query range, we show that it is possible to pre-process P into a data structure of size Õϵ,γ(dn1+ρ) in Õϵ,γ(dn1+ρ) time such that given any Rd query ball B and query point q, it outputs in Õϵ,γ(dnρ) time a point p that is a (1 − ϵ)-approximation of the farthest point to q among all points lying in a (1 + γ)-expansion B(1 + γ) of B, where 0 © Ziyun Huang and Jinhui Xu;licensed under Creative Commons License CC-BY 4.0

关键词： Aggregates

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Smart Classroom: A Deep Learning Approach towards Attention Assessment through Class Behavior Detection

Smart Classroom: A Deep Learning Approach towards Attention ...

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2022 Advances in science and engineering Technology International Conferences, ASET 2022

作者： Parambil, Medha Mohan Ambali Ali, Luqman Alnajjar, Fady Gochoo, Munkhjargal United Arab Emirates University College of IT Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781665418010

The role of education in shaping future generations is crucial. The rapid advancement in Artificial Intelligence (AI) has prompted a considerable turn of events in the educational field. Advanced AI technologies have the potential to replace traditional classroom practices, giving more importance to students' preferences. Assessing the attention in students is the building block to improve both instructing and learning. Most of the time, it becomes puzzling for an instructor to track every student and know their interest in learning, especially in a vast scenario. In such cases, instructors are often unaware of the most coherent approach while teaching students with different learning capabilities. Therefore, this paper proposed a system that incorporates AI to develop a real-time vision-based smart classroom that autonomously monitors students' attention, as well as, emotions and gives live graphical feedback to the instructor. Our system was able to monitor the presence of a student in the classroom and analyze the students' attention and emotion. The proposed approach was able to assign numerical scores to the student for the concentration level in the class. The stated system can help the instructors evaluate the student's attention instantaneously and more accurately, thus supporting them in conducting the session suitably that may lead to better academic performance. © 2022 IEEE.

关键词： Students

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Proactive Hybrid Autoscaling for Container-Based Edge Applications in Kubernetes 19th

Proactive Hybrid Autoscaling for Container-Based Edge Appli...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Zhu, Kaile Shen, Shihao Lan, Shizhan Wang, Xiaofei Zhang, Cheng Qiu, Chao Leung, Victor College of Intelligence and Computing Tianjin University Tianjin China China Mobile Guangxi Institute Co. Ltd. Shanghai China Institute of Technology Tianjin University of Finance and Economics Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (纸本)9783031651229

As the rising of the Internet of Things (IoT), edge computing is widely adopted in numerous applications. However, current autoscaling tools are not designed for edge applications and can not utilize the heterogeneous resources of edge nodes efficiently. In this paper, we propose a proactive hybrid autoscaler specifically optimized for edge computing scenario. With the Bidirectional Long Short Term Memory (Bi-LSTM) based load prediction model, the proposed autoscaler is able to predict the future workload and perform scaling operation before it arrives. In addition, a overload compensation algorithm is implemented to mitigate the Quality of Service (QoS) decreasing due to under-prediction. Then, a hybrid scaling method is applied to simultaneously modify the number of pods and their resource quotas without restarting. Experimental results with a real-world workload dataset shows the proposed load prediction model has better accuracy compared with the Long Short Term Memory model and the state-of-the-art statistical analysis model, Autoregressive Integrated Moving Average (ARIMA), which is also more than 350 times slower than our model in prediction speed. Finally, evaluation in a real Kubernetes cluster shows that the proposed proactive hybrid autoscaler outperforms the default Horizontal Pod Autoscaler (HPA) of Kubernetes in terms of both QoS and resource utilization efficiency. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Quality of service

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Review of Power Grid Optimization Scheduling Methods Based On Deep Reinforcement Learning

Review of Power Grid Optimization Scheduling Methods Based O...

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China International Electrical and Energy Conference (CIEEC)

作者： Yang Zherui Zhang Zhen Li Fangzhou Dai Nan Yan Yiying Wang Ke Dayu College Hohai University College of Energy and Electrical Engineering Hohai University College of Computer Science and Software Engineering Hohai University

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

In recent years, with the introduction of energy conservation and emission reduction targets, the introduction of renewable energy has become an important initiative. However, due to the many factors that affect the new energy units, the traditional scheduling method becomes inappropriate. If a more intelligent scheduling method is not developed, the lack of flexible energy scheduling capacity will further lead to problems such as insufficient wind power consumption level, inefficient power grid decision-making, high operating costs, and unstable energy supply. In order to solve the above problems and improve the environmental protection, efficiency, economy and safety of the power grid, this paper summarizes the application of artificial intelligence in the field of power grid scheduling, analyzes SAC, A3C and TD3 algorithms in detail, analyzes their mechanism through the algorithm structure, and summarizes the advantages and disadvantages of each algorithm and its application scenarios. In addition, this paper also analyzes the application of artificial intelligence in unit combination, economic scheduling optimization and optimal power flow optimization. Different from other review papers, this paper deeply analyzes several problems that may arise in the application of the algorithm, and specifically analyzes the future optimization direction and corresponding optimization methods, so as to improve the level of experience combination, fault handling, comprehensive consideration, interpretability and other aspects of the future agent.

关键词： Training Renewable energy sources Reviews Wind power generation Deep reinforcement learning Power grids Scheduling

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F-IKOS: An Abstract Interpretation-based Static Analyzer for Fortran Programs 12

F-IKOS: An Abstract Interpretation-based Static Analyzer for...

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12th International Workshop on Quantitative Approaches to software Quality, QuASoQ 2024

作者： Zou, Sheng Chen, Liqian Fan, Guangsheng Huang, Renjie Yin, Banghu College of Computer Science and Technology National University of Defense Technology Changsha410073 China State Key Laboratory of Complex & Critical Software Environment Changsha410073 China College of Systems Engineering National University of Defense Technology Changsha410073 China

The Fortran programming language is widely utilized in numerical computation and scientific computing. Fortran programs are prone to potential runtime errors related to numerical properties due to the large number of numerical operations. In this paper, we present F-IKOS, an abstract interpretation-based static analyzer for Fortran programs on top of IKOS, which soundly handles floating-point types in Fortran programs. Firstly, we translate Fortran programs to LLVM IR using compiler front-end Flang. After that, we extend IKOS to support sound floating-point analysis and then employ it to analyze the translated LLVM IR. Particularly, when analyzing floating-point types in programs, we first abstract floating-point expressions into real-number expressions with interval coefficients, and then linearize these expressions into real-number expressions with scalar coefficients. These linear expressions are subsequently handled by abstract domains originally designed for real-number types to produce sound analysis results. We have conducted experiments on representative Fortran programs to show the efficiency and effectiveness of F-IKOS. The experimental results are encouraging: F-IKOS soundly analyzes runtime errors in complex programs, outperforming other analyzers. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： FORTRAN (programming language)

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