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23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Štípal, Jiří Trubák, Kamil Látal, Jan Šiška, Petr Vojtěch, Josef Hristov, Hristo Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic CESNET a. l. e. Zikova 1903/4 Prague160 00 6 Czech Republic Department of Software Technologies Plovdiv University "Paisii Hilendarski" Plovdiv4000 Bulgaria

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

This work is dealing with actual topic in the field of internet service providers that are solving the question of metallic and optical networks convergence. The aim was to construct an experimental network topology in the form of WAN or LAN topology consisting of GPON network with VDSL2 technology, which led to the creation of a hybrid access network, on which the parameters of multimedia services were then measured. In particular, focusing on the distribution of video streams through SDTV & HDTV (MPEG-2) + HDTV (MPEG-4). As part of the network integrity measurements, tests are performed according to the recommendations of RFC 6349 and ITU-T Y.1564 standard. © 2025 SPIE.

关键词： Multimedia services

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Multi-task Scheduling Algorithm Based on Self-adaptive Hybrid ICA–PSO Algorithm in Cloud Environment 8th

Multi-task Scheduling Algorithm Based on Self-adaptive Hybri...

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Tabrizchi, Hamed Kuchaki Rafsanjani, Marjan Balas, Valentina Emilia Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania

ISBN: (纸本)9783030521899

Cloud is a new concept in Internet technology and has brought many benefits, particularly, in the field of computing. Cloud has changed how on-demand resources are allocated to the different user requests and has provided more optimal resource utilization. Tasks (user requests) have different kinds, including deadline-based tasks having time-related limitations. This kind of task has various parameters and scheduling them is more challenging than scheduling the individual tasks. In this regard, many papers have been published according to these parameters. The objective of this paper is to propose a novel self-adaptive hybrid ICA–PSO algorithm for dealing with associate multi-task scheduling problem. To improve the exploration, two algorithms, namely, the imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) were combined in this study. The results of the present study outperform those of the existing mechanisms. © 2021, Springer Nature Switzerland AG.

关键词： Scheduling algorithms

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Efficient Resource Allocation Using Service Function Chain in Agricultural Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Yao, Jiamin Qiao, Sibo Xie, Yu Kumari, Saru Lin, Qiao Khan, Fazlullah Shanghai Maritime University College of Information Engineering Shanghai201306 China Tiangong University School of Software Tianjin300387 China Chaudhary Charan Singh University Department of Mathematics Meerut India University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China

As the level of intelligence in agricultural consumer electronics continues to advance, data-driven devices are often faced with challenges such as resource shortages, high real-time requirements, and complex data processing. In particular, the diversity and variability of massive agricultural consumer electronics in the agricultural Internet of Things (IoT) complicate the resource allocation process. Traditional centralized cloud computing architectures often struggle to meet these demands. To address these issues, this paper proposes a resource collaborative allocation method for agricultural consumer electronics based on Service Function Chain (SFC). First, we formalize the resource requirements of various agricultural consumer electronics applications based on SFC in a resource mapping model. Subsequently, we develop a multidimensional resource balancing model with computing resources, storage resources, and bandwidth resources requirements of agricultural consumer electronics. We also devise a fine-grained end-to-end latency model to comprehensively consider the effective utilization of multidimensional resources and latency guarantees for agricultural consumer electronics. Finally, we employ a deep reinforcement learning algorithm with experience replay to optimize the resource allocation strategy in real-time, thereby enhancing the agricultural consumer electronics system's responsiveness and resource utilization efficiency. Experimental results demonstrate that our proposed strategy outperforms existing methods in terms of improving resource utilization and reducing latency. © 1975-2011 IEEE.

关键词： Chains

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Imitation Learning from Purified Demonstrations

arXiv

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arXiv 2023年

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong China School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Confirmed Cases Prediction based on Vaccination

A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Con...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Marzia Ahmed Ahmad Johari Mohamad Mostafijur Rahman Mohd Herwan Sulaiman Mohammod Abul Kashem Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang (UMP) Pahang Malaysia Department of Software Engineering Daffodil International university Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Department of Computer Science and Engineering DUET Gazipur Bangladesh

Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics.

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Classification of Plants Leave Using Image Processing and Neural Network 8th

Classification of Plants Leave Using Image Processing and Ne...

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Bagherinezhad, Hashem Kuchaki Rafsanjani, Marjan Balas, Valentina Emilia Koles, Ioan E. Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania

ISBN: (纸本)9783030521899

Plants are one of the most widely used resources for humans in different fields. Therefore, the distinction between the plant species is important and it is referred to as the plant detection system. Until now, this task has been done by the expert botanists which is an overwhelming and time consuming task. Moreover, there is a lack of the memory and the human fault, so the researchers endeavored to solve these disadvantages using the AI algorithms. For this goal, in this paper, a system is proposed that includes four phases: pre-processing, feature extraction, training, and test. In this method, we use the combination of the useful features of the leave shape, the leave texture, the leave color, and we provide a method for the classification of a number of the plant species. Finally, the feature vectors will be created and then, the classification is performed by using feed-forward back-propagation multi-layer perceptron artificial neural network algorithm. Then, the results of this method compare with other methods. The obtained results show the high accuracy of this method for a large number of the species in different conditions (such as pests, season changes and lighting). © 2021, Springer Nature Switzerland AG.

关键词： Feature extraction

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A Novel Outlier-Tolerable and Predictive Approach to Web Service Composition 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Sun, Xiaoning Chen, Peng Dai, Mengxuan Xia, Yunni Zheng, Wanbo Li, Jianqi Xie, Hong Fu, Xiaodong Peng, Kai Niu, Xianhua Chen, Juan School of Computer and Information Science Chongqing Normal University Chongqing401331 China School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China College of Computer Science Chongqing University Chongqing400030 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Engineering college Huaqiao University Quanzhou362021 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

The Quality-of-Service (QoS) aspects of Web service has gained popularity in the field of service computing. QoS-oriented Web service composition is a distributed model to construct new web service on top of existing primitive or other composite web services with QoS guarantees. A major challenge in this field is that the QoS data of candidate services are with run-time fluctuations and thus difficult to predict. Traditional approaches in this direction tended to address the challenge by statistics, prediction and neural network-based models. A major limitation of these methods lies in that they ignore outliers data in the historical QoS data, in terms of inconsistencies, errors, shifts, corruptions, etc. In this work, instead, we consider outliers in QoS series to be non-neglectable, and propose an outlier-tolerable and predictive approach to service composition through leveraging a joint estimation-based outlier detection method and a niched genetic algorithm. To validate the effectiveness of our proposed method, we conduct extensive case studies based on different outlier conditions, and the experimental results show that our method is superior to existing ones. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quality of service

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Frequency-aware Interaction Network for Ultrasound Image Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2024年

作者： Wang, Dongfang Zhou, Tao Zhang, Yizhe Gao, Shangbing Yang, Jian Nanjing University Of Science And Technology Pca Lab Key Lab Of Intelligent Percept. And Syst. For High-Dimensional Information Of Ministry Of Education Jiangsu Key Lab Of Image And Video Understanding For Social Security School Of Computer Science And Engineering Nanjing210094 China Huaiyin Institute Of Technology Faculty Of Computer And Software Engineering Huaian223003 China

Accurate segmentation of medical ultrasound images is crucial for guiding treatment decisions and assessing intervention effectiveness. The challenge of segmenting lesions in ultrasound images arises from factors such as low contrast, high speckle noise, artifacts, and blurred boundaries. Furthermore, this complexity varies significantly among lesions in different cases. While methods based on Convolutional Neural Networks (CNNs) and Transformers have shown promising results in this field, each approach possesses distinct advantages and limitations. To address these challenges, we propose a novel Frequency-aware Interaction Network (FINet). At the core of our FINet lies the proposed Multi-scale Frequency-aware Self-attention (MFS) module, which effectively captures multi-scale feature information within the self-attention layer. This enables our network to model both local and global features, capitalizing on the strengths of both CNNs and Transformers. Additionally, a frequency-aware network is introduced to learn the interactions between spatial locations in the frequency domain to enhance detailed feature representation such as edges. Furthermore, we present a collaborative interactive decoder network, in which a Selective Feature Interaction (SFI) module is proposed to facilitate the semantic and boundary feature interaction, resulting in more precise segmentation outcomes. Experimental results on four medical ultrasound image datasets show the superiority of our FINet over other state-of-the-art segmentation methods. More importantly, our model achieves an excellent trade-off between performance and computational efficiency. © 1991-2012 IEEE.

关键词： Ultrasonic imaging

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Evaluation of Valued Tolerance Rough Set and Decision Rules Method for WiFi-Based Indoor Localization in Different Environments

Evaluation of Valued Tolerance Rough Set and Decision Rules ...

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Proceedings of the International Conference on Intelligent Systems and Network, ICISN 2023

作者： Duong-Bao, Ninh He, Jing Thi, Luong Nguyen Lee, Seon-Woo Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Faculty of Mathematics and Informatics Dalat University Dalat66100 Viet Nam Faculty of Information Technology Dalat University Dalat66100 Viet Nam Division of Software Hallym University Chuncheon24252 Korea Republic of Department of Electronics and Telecommunications Dalat University Dalat66100 Viet Nam

ISBN: (纸本)9789819947249

Among various technologies being applied for indoor localization, WiFi has become a common source of information to determine the pedestrian’s position due to the widespread of WiFi access points in indoor environments. However, the fluctuation of the WiFi signals makes it difficult to achieve a good localization result. In this paper, to handle this problem, the valued tolerance rough set and decision rules method (VTRS-DR), which is firstly registered to WiFi fingerprinting-based localization, will be implemented and evaluated in big and complicated environments using two public datasets. The first one was conducted by a subject using a smartphone at a multi-floor library for several months. Furthermore, to evaluate the localization accuracy when WiFi data was collected from different pedestrians as well as different smartphones, a crowdsourced WiFi fingerprinting dataset was utilized. From the deep analyses of localization results, the VTRS-DR method shows high accuracy and high robustness when testing in different environments with a mean error of 3.84 m, which is 27.87% lower than other compared methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smartphones

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation 11th

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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11th International Conference on computer engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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