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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Syrotkina, Olena Kobti, Ziad Aleksieiev, Mykhailo Moroz, Borys Udovyk, Iryna Shvachych, Hennadii University of Windsor School of Computer Science Windsor Canada Dnipro University of Technology Department of Software Engineering Dnipro Ukraine

This paper addresses the issue of creating and applying mathematical models and methods for finding generalized solutions when working with structured collections of 'big data'. We reviewed the modern methodologies used to solve problems of this class. The mathematical model presented describes an ordered set of all subsets formed from a finite ordered base set of arbitrary size and data type. We explored a set of functional dependencies of five discrete input variables to work with this mathematical model. Some of these functional dependencies are derived for specific solutions with specified boundary conditions. The paper also presents examples of how the derived functional dependencies are applied in the implementation of mathematical methods using this model. This required us to conduct a comparative assessment of the search time for a solution with and without the use of these mathematical methods. Comparative graphs are demonstrated to show the rate of increase in the number of operations depending on the size of the original finite base set with and without the use of these mathematical methods. As a result of this, logical conclusions are drawn regarding the impact of mathematical methods for working with structured collections on minimizing time and computational resources. © 2023 IEEE.

关键词： Big data

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Business Scenario Driven Reinforcement Learning Testing Method 26

Business Scenario Driven Reinforcement Learning Testing Meth...

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26th ACIS International Winter Conference on software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Cai, Lizhi Wang, Jin School of Information Science and Engineering East China University of Science and Technology Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9798350345865

Reinforcement learning has been successfully applied in software testing, but the existing testing methods cannot perform effective testing according to the characteristics of applications, and using outdated interactive experience during training, resulting in inefficient testing. In this paper, we propose BSDRTesting. Firstly, the demonstration experience of human users is collected according to the functional scenarios and business logic of each application, and combining reinforcement learning and imitation learning to maximize rewards while imitating user behavior, experience replay aims to sample experiences from the agent's self-exploration and expert demonstrations to improve sampling efficiency. At the same time, according to the input rules, the black-box testing method is used to fully test the input events, and finally an experience filtering mechanism is proposed, and the reward value and TD-Error are used as the basis for priority sampling. The experimental results on 10 open source applications show BSDRTesting has achieved significant improvements in code coverage and branch coverage compared with existing methods. © 2023 IEEE.

关键词： Reinforcement learning

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Interaction Petri Nets for Modeling Multiagent Interactions

Interaction Petri Nets for Modeling Multiagent Interactions

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Aldahash, Norah King, Steve Benabid, Abir University of York Computer Science York United Kingdom King Saud University Software Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350327595

Petri nets have been closely related to multiagent systems (MAS) and used as a tool for modeling agents and their behavior for specification, verification, and validation purposes. Various models of agent activities, plans, and interactions show the ability of Petri nets to represent dynamic and concurrent MAS. Insight from previous Petri net models of agent interaction led to the proposal of a Colored Petri net model for monitoring the state of communication. This paper presents an interaction Colored Petri net for modeling message-based communication ruled by Interaction Protocols (IP). While previous works focused on modeling a single protocol, the proposed Interaction Petri Net (IPN) allows specifying multiple protocols within one model. Moreover, IPN allows the detection of common communication errors such as: lost messages, delay, busy-wait and agent termination. In IPN, an interaction protocol is specified as a Petri net marking where tokens are message instances. This approach enables the capture of the status of each IP in real-time for error detection where two possible approaches have been examined. The proposed model has been validated through a comparison analysis with a previous Petri net model of interaction. © 2023 IEEE.

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The SOC Prediction of Li-ion Batteries Based on Neural Network and Random Forest 2nd

The SOC Prediction of Li-ion Batteries Based on Neural Netwo...

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2nd International Conference on Image, Vision and Intelligent Systems, ICIVIS 2022

作者： Yu, Mengqi Wang, Shuai Wei, Yining Zhong, Zhenkun Peng, Hui School of Computer Science and Engineering Central South University Changsha410083 China School of Software Engineering Xinjiang University Xinjiang830008 China

ISBN: (纸本)9789819909223

The state of charge (SOC) of lithium-ion batteries is predicted by using neural network and random forest. According to the battery discharge state in different periods, the entire discharge process is divided into platform stage and non-platform stage, and the above two methods are used to predict each stage separately. On the basis of fully analyzing the charge and discharge mechanism of lithium-ion batteries, the back propagation (BP) neural network model of lithium-ion batteries is established by genetic algorithm. The result proves that the SOC prediction method based on neural network and random forest has high accuracy, so this method can be applied in predicting the battery SOC. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithium-ion batteries

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Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation 28th

Self-Sampling Training and Evaluation for the Accuracy-Bi...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Liu, Dugang Qiao, Yang Tang, Xing Chen, Liang He, Xiuqiang Pan, Weike Ming, Zhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China FIT Tencent Shenzhen China

ISBN: (纸本)9783031306778

Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most of the existing methods require some specific data, architectures and training methods. In this paper, we first argue through an online study that arbitrarily removing all the biases in industrial recommendation may not consistently yield a desired performance improvement. For the situation that a randomized dataset is not available, we propose a novel self-sampling training and evaluation (SSTE) framework to achieve the accuracy-bias tradeoff in recommendation, i.e., eliminate the harmful biases and preserve the beneficial ones. Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data. A self-training module infers the beneficial biases and learns better tradeoff based on these subsets, and a self-evaluation module aims to use these subsets to construct more plausible references to reflect the optimized model. Finally, we conduct extensive offline experiments on two datasets to verify the effectiveness of our SSTE. Moreover, we deploy our SSTE in homepage recommendation of a famous financial management product called Tencent Licaitong, and find very promising results in an online A/B test. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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FAS-reid: Fair Architecture Search for Person Re-IDentification 3

FAS-reid: Fair Architecture Search for Person Re-IDentificat...

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3rd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Chen, Shengbo Chen, Shijie Lei, Zhou Shanghai University School of Computer Engineering and Science Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9781665490788

The development of deep learning has driven the development of ReID, and more and more excellent methods have been proposed, but most of these are artificially designed network backbones. Automation is a trend in the development of deep learning. This paper propose FAS-ReID(Fair Architecture Search for Person Re-IDentification), which can automatically design and generate a neural network backbone for ReID. This paper compare operation selection to competition, and inject noise to make the competition fairer, while making the architecture of automated search More adaptable to ReID We use TriHard loss to improve the feature extraction ability. Our experiments show that the backbone searched by FAS-ReID is better and reduces the search time. Meanwhile, as for the problem of performance collapse caused by skip-connection enrichment in the search process, FAS-Reid does not use the early stop strategy to avoid performance loss like other solutions, which is also the reason why our method is more reliable and robust. © 2023 IEEE.

关键词： Automation

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based software Systems

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computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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WIP: Concept Maps for computer Organization: A Systematic Study 54

WIP: Concept Maps for Computer Organization: A Systematic St...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Belkhouche, F. Bendjilali, K. Shippensburg University of Pennsylvania Department of Computer Electrical and Software Engineering ShippensburgPA United States Raritan Valley Community College Department of Mathematics and Computer Science BranchburgNJ United States

ISBN: (纸本)9798350351507

This research-to-practice WIP paper describes concept maps for computer organization. A concept map is a tool that can be used to describe the relationship between the topics (or concepts) in a specific field of study. Concept maps are used in a variety of applications to improve curricula and assessment, particularly in K12 and undergraduate STEM education. In this paper we use concept maps for modeling computer organization topics and assess their importance with respect to the depth and breadth levels required for computer science and engineering graduates. The proposed concept maps focus on two aspects: The first one is the idea of the centrality of the topics. Centrality is an important concept in graph theory. Each topic in the concept map is given a level of centrality or a centrality index. The second aspect is the relationship between the topics, particularly between the topics with high level of centrality. The objective of the modeling is to identify the most central topics and come up with a set of more standard learning outcomes for undergraduate level computer organization courses. © 2024 IEEE.

关键词： Students

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3D Atrous Spatial Pyramid Pooling based Multi-Scale Feature Fusion Network for Hyperspectral Image Classification

3D Atrous Spatial Pyramid Pooling based Multi-Scale Feature ...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Zhu, Tianxing Liu, Qin Zhang, Lixiang School of Software Engineering Tongji University Shanghai China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781510668898

The convolutional neural network (CNN) has been widely adopted in the processing of hyperspectral images (HSI) due to its remarkable capabilities in feature extraction. However, CNN-based methods for HSI classification still face the challenge of extracting spectral-spatial features that are more effective while considering all spectral bands. This paper reviews various spectral and spectral-spatial classifiers that have been developed to overcome the challenges of HSI classification, including the limitations of handcrafted features and the problems caused by spectral variability and nonlinearity. The paper proposes a novel method, the 3D ASPP Multi-Scale Feature Fusion Network (3A-MFFN), which simultaneously preserves high-resolution spatial detail information and high-level semantic information of HSI. The proposed network extracts and fuses multi-scale features for HSI classification using a multi-scale 3D ASPP convolution block to fuse different levels of feature output. The paper presents experiments and analysis to demonstrate the effectiveness of the proposed method. The results suggest that the 3A-MFFN algorithm can significantly enhance the classification accuracy of HSI with limited samples, making it a valuable contribution to the hyperspectral remote sensing community. © 2023 SPIE.

关键词： Convolution

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Enabling Collaborative and Green Generative AI Inference in Edge Networks

Enabling Collaborative and Green Generative AI Inference in ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Tian, Meng Liu, Zhicheng Qiu, Chao Wang, Xiaofei Niyato, Dusit Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin China Nanyang Technological University College of Computing and Data Science Singapore Shenzhen University College of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350351255

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm's ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content. © 2024 IEEE.

关键词： Generative adversarial networks

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