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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Yuanchao Qian, Ruyi Wang, Yida Huo, Qirun Capital Normal University College of Information Engineering Beijing China Skl of Computer Architecture Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798350329223

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time. © 2023 IEEE.

关键词： Deep neural networks

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EagerReuse: An Efficient Memory Reuse Approach for Complex Computational Graph 29

EagerReuse: An Efficient Memory Reuse Approach for Complex C...

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

作者： Qian, Ruyi Cao, Bojun Gao, Mengjuan Shi, Qinwen Wang, Yida Xu, Yuanchao Huo, Qirun Qiu, Keni Capital Normal University College of Information Engineering Beijing China Institute of Computing Technology Cas Skl of Computer Architecture Beijing China

ISBN: (纸本)9798350330717

Memory reuse is a promising approach for deep neural network (DNN) to reduce memory consumption because it does not introduce any additional runtime overhead. We observe that existing memory reuse algorithms consider only the effect of an individual data feature (either tensor size or tensor lifetime) on memory reuse and ignore the relative position relationship (RPR) among tensors. As computational graphs grow slightly more complex, the mining of memory reuse becomes insufficient. To address this issue, we propose a new memory reuse algorithm - EagerReuse, which can exploit more memory reuse opportunities by analyzing RPR among tensors and reusing them as quickly as possible. We evaluated the algorithms with inference models in TensorFlow Model Garden, and the results show that the EagerReuse outperforms the state-of-the-art algorithms in three out of seven cases. For more complex computational graphs, EagerReuse can achieve better memory usage with slightly higher but acceptable overhead. © 2023 IEEE.

关键词： computational graph memory optimization memory reuse memory usage

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Effect of Environment Size on Spatial Perception in Virtual Reality 6

Effect of Environment Size on Spatial Perception in Virtual ...

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6th International Conference on computer-Human Interaction Research and Applications, CHIRA 2022

作者： Bach, Morten Trier Jensen, Poul Anker Lucaci, Andrei Iuliu Pupczyk, Damian Grzegorz Madsen, Claus Brøndgaard Computer Graphics Group Department of Architecture and Media Technology Aalborg University Aalborg Denmark

ISBN: (纸本)9789897586095

A trend of distance underestimations in Virtual Reality (VR) is well documented, but the reason still remains unclear. Therefore, this paper investigates the effect of differently sized Virtual Environments (VEs) on egocentric distance perception in VR as a potential influence. Verbal assessment, blind walking, and our own proposed method: walk and assess, were compared in an experiment, and blind walking was found to be the most accurate. A virtual replica of a real-life location was created as a transitional environment, while small (15m2), medium (35m2) and large (95m2) rooms were created to investigate the effect of VE size on spatial perception in VR. To establish the differences in estimations between the real world and VR when using blind walking, a study was conducted with the virtual replica and its real life counterpart at distances between 1 and 10 meters. Following this, the three distinct room sizes were used in an experiment to investigate the effect of the size of rooms on spatial perception in VR. The findings showed consistent underestimates of distances, and a trend for underestimation to grow as the distance grows was observed. Similarly, underestimates grew with the size of the environment. Copyright © 2022 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved.

关键词： Virtual reality

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Evolvable Agents, a Fine Grained Approach for Distributed Evolutionary Computing: Walking towards the Peer-to-Peer Computing Frontiers

arXiv

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arXiv 2024年

作者： Laredo, Juan Luis Jiménez Castillo, Pedro A. Mora, Antonio M. Merelo, Juan Julián Department of Architecture and Computer Technology ETSIIT University of Granada Spain

In this work we propose a fine grained approach with self-adaptive migration rate for distributed evolutionary computation. Our target is to gain some insights on the effects caused by communication when the algorithm scales. To this end, we consider a set of basic topologies in order to avoid the overlapping of algorithmic effects between communication and topological structures. We analyse the approach viability by comparing how solution quality and algorithm speed change when the number of processors increases and compare it with an Island model based implementation. A finer-grained approach implies a better chance of achieving a larger scalable system;such a feature is crucial concerning large-scale parallel architectures such as Peer-to-Peer systems. In order to check scalability, we perform a threefold experimental evaluation of this model: First, we concentrate on the algorithmic results when the problem scales up to eight nodes in comparison with how it does following the Island model. Second, we analyse the computing time speedup of the approach while scaling. Finally, we analyse the network performance with the proposed self-adaptive migration rate policy that depends on the link latency and bandwidth. With this experimental setup, our approach shows better scalability than the Island model and a equivalent robustness on the average of the three test functions under study. © 2024, CC BY.

关键词： Scalability

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Fast and efficient parallel breadth-first search with power-law graph transformation

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Frontiers of computer Science 2022年第5期16卷 225-227页

作者： Zite JIANG Tao LIU Shuai ZHANG Mengting YUAN Haihang YOU School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Comptuer Science Wuhan UniversityWuhan 430072China

1 Introduction Most real-world graphs are large-scale but unstructured and *** of the most notable characteristics of real-world graphs is the skewed power law degree distribution[1]:most vertices have a few neighbors while a few own a large number of *** characteristics present challenges for efficient parallel graph processing,such as load imbalance,poor locality,and redundant *** from modifying the graph programming abstraction or changing the execution models on different architectures,reducing the irregularity of graph data also improves the performance of graph processing[2].For example,it is wellknown that BFS has a bad temporal locality,but it is possible to transform irregular graphs to more regular ones to improve spatial locality and gain more performance.

关键词： neighbor programming redundant

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IoT Based Systems and Services: Recent Security Concerns and Feasible Solutions 1

IoT Based Systems and Services: Recent Security Concerns and...

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1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Attaullah, Hafiz Muhammad Memon, Shahzad Erkan, Ozlem Feyza Khawar, Ruba Mohammad Ali Jinnah University Department of Computer Science Karachi Pakistan Beykoz University Faculty of Engineering and Architecture Department of Computer Engineering Istanbul Turkey NED University of Engineering and Technology Faculty of Information Sciences and Humanities Karachi Pakistan

ISBN: (纸本)9798350373042

The term Internet of Things (IoT) is used to refer as embedded devices or objects with internet access, allowing them to communicate globally, interacting with people and networks. IoT security issues are directly related to the widespread use of its systems. This paper examines the security technologies employed in IoT and their respective features and applications. Additionally, it addresses the various security concerns associated with IoT-based systems and services, highlighting their occurrence within the three-layer framework of IoT. These days, as IoT introduces advanced application architectures that minimizes human intervention by integrating businesses in some way to address digital computer processes by communicating within a given network. Moreover, it will be discussed the key technologies associated with Advanced Encryption Standards (AES) and the changes brought in market value and usage of services that has been taken place b since the IoT incorporated. © 2024 IEEE.

关键词： Network security

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Maneuvering Angle Rigid Formations With Global Convergence Guarantees

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1464-1475页

作者： Liangming Chen Zhiyun Lin Hector Garcia de Marina Zhiyong Sun Mir Feroskhan School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Computer Architecture and Technology and also with CITICUniversity of Granada18014 GranadaSpain Department of Electrical Engineering Eindhoven University of Technology5600 MB Eindhoventhe Netherlands

Angle rigid multi-agent formations can simultaneously undergo translational,rotational,and scaling maneuvering,therefore combining the maneuvering capabilities of both distance and bearing rigid ***,maneuvering angle rigid formations in 2D or 3D with global convergence guarantees is shown to be a challenging problem in the existing literature even when relative position measurements are *** by angle-induced linear equations in 2D triangles and 3D tetrahedra,this paper aims to solve this challenging problem in both 2D and3D under a leader-follower *** the 2D case where the leaders have constant velocities,by using local relative position and velocity measurements,a formation maneuvering law is designed for the followers governed by double-integrator *** the leaders have time-varying velocities,a sliding mode formation maneuvering law is proposed by using the same *** the 3D case,to establish an angle-induced linear equation for each tetrahedron,we assume that all the followers'coordinate frames share a common Z ***,a formation maneuvering law is proposed for the followers to globally maneuver Z-weakly angle rigid formations in *** extension to Lagrangian agent dynamics and the construction of the desired rigid formations by using the minimum number of angle constraints are also *** examples are provided to validate the effectiveness of the proposed algorithms.

关键词： Index Terms—Angle rigid formations formation control formation maneuvering global convergence multi-agent systems

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Calligraphy's Reflective Expression: Intelligent Chinese Calligraphy Affective and Style Recognition 15

Calligraphy's Reflective Expression: Intelligent Chinese Cal...

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15th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2023

作者： Xing, Baixi Pan, Yanhong Chen, Qimeng Song, Xinjie Wang, Kaiqi Shi, Yuehan Industrial Design Institute Zhejiang University of Technology Hangzhou China School of Design and Architecture Zhejiang University of Technology Hangzhou China College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9798350326178

Calligraphy expresses people's inner mood and reflects their self-cultivation. Style and emotion plays a key role in calligraphy artistry. Consequently, we applied intelligent technology to analyze the emotion and artistic style recognition of Chinese calligraphy artworks. A dataset of 5,000 calligraphy images was established, including art works over seventy renowned Chinese calligraphists. We compared several deep-learning methods to build the best model. The results show that the optimal model achieves an accuracy of 0.9319 with MobileNet in calligraphy emotion recognition, while it achieves the best accuracy of 0.972 in calligraphy font style recognition, and the highest classification accuracy of 0.943 in calligraphy artist's style recognition respectively using ResNet50. Finally, an intelligent Chinese calligraphy emotion and style exploration application are developed based on the optimal model. © 2023 IEEE.

关键词： Emotion Recognition

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Application of residual network in household waste classification

Application of residual network in household waste classific...

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2023 IEEE International Conference on Control, Electronics and computer technology, ICCECT 2023

作者： Guo, Yutong Luo, Jie Guo, Weile Chengdu University of Technology College of Computer Science Cyber Security Oxford Brookes College Chengdu China Xi'An University of Architecture and Technology College of Metallurgical Engineering Xi'an China

ISBN: (纸本)9798350399028

With the acceleration of urbanization, the amount of garbage has skyrocketed. This makes the burden of sorting waste by manual means heavier and heavier, resulting in a large amount of waste accumulation. In this context, the classification of household garbage gradually received attention. In order to improve the efficiency of garbage classification, we should deal with the increasingly complicated garbage classification work and make garbage classification intelligent and efficient. In this paper, convolutional neural network intelligent algorithm is used to solve the classification problem of organic and inorganic garbage in daily life. The accuracy can reach 90.34% only with low sequence training. Compared with existing commonly used garbage classification and recognition algorithms, the proposed garbage classification and recognition algorithm has better recognition performance and is more suitable for widespread application. © 2023 IEEE.

关键词： Image classification

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Integrating Structural Information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (case study: protein-peptide region residue-level interaction) using structural information 29

Integrating Structural Information: Comparing Classification...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Shafiee, Shima Fathi, Abdolhossein Safari, Faezeh Department of Computer Engineering and Information Technology Razi University Kermanshah Iran School of Electrical Eng. and Computer Science Faculty of Eng. Architecture and Info. Tech University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798331523114

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method. © 2025 IEEE.

关键词： Bioinformatics

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