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IEEE International Conference on Communications (ICC)

作者： Changkun Jiang Lin Gao Tong Wang Jingjing Luo Fen Hou College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China Key Laboratory of IoT for Smart City and the Department of Electrical and Computer Engineering University of Macau Macau China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Mobile edge caching is a promising approach for enhancing content delivery efficiency and alleviating backbone network burden, via caching popular contents at network edge devices (e.g., base stations or WiFi access points). The successful commercial deployment relies on a comprehensive understanding of the economic interactions among different stakeholders involved. In this paper, we study an edge caching system consisting of a Content Provider (CP), an Internet Service Provider (ISP) who provides the backbone network service, a wireless Access Provider (AP) who provides the wireless access service, and a set of mobile End-Users (EUs), where the CP provides contents for EUs either via the remote server (on the Internet) or via the edge cache (purchased from the AP). We formulate their interactions as a three-stage Stackelberg game. In Stage I, the CP decides the edge cache space to purchase from the AP and cache access fee to charge EUs. In Stage II, the ISP and AP determine the backbone and wireless access service prices, respectively. In Stage III, EUs decide whether to subscribe to the CP' s edge cache service, taking the cache hit probability, cache access fee, backbone and wireless access prices into consideration. We analyze the subgame perfect equilibrium of the dynamic game systematically under two different network pricing scenarios: cooperative pricing and competitive pricing, depending on whether ISP and AP cooperate or compete with each other to make their pricing decisions. Our analysis and simulation results show that all profits of the CP, ISP, AP, and utilities of EUs can be increased by adopting edge cache, compared with the case without edge cache.

关键词： Internet Quality of service Pricing Economics Games Ecosystems Stakeholders

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Adaptive fault-tolerant model for improving cloud computing performance using artificial neural network

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Procedia computer science 2020年 170卷 929-934页

作者： Awatif Ragmani Amina Elomri Noreddine Abghour Khalid Moussaid Mohammed Rida Elarbi Badidi Faculty of Sciences AinChock University Hassan II of Casablanca 20100 Casablanca Morocco Department of Computer Science and Software Engineering College of Information Technology UAE University AL-AIN PO Box. 15551 UAE

Cloud computing is a paradigm that has greatly contributed to the emergence of new applications of IT services. However, the design of dependable and efficient cloud architectures requires a high knowledge of the recurring failures causes which compromise the efficient functioning of the system. Nowadays, the number of datasets recording the real state of cloud systems still rarely available to the public. This article uses failure data published by Backblaze which is one of the largest data storage providers. This dataset consists of the operating status of heterogeneous servers that were collected during the period between January 2015 and December 2018. Then, the data has been filtered and preprocessed to keep a total of 2,878,440 records including 128,820 failures. We investigate the correlation between hard drive parameters and failure by exploiting the attributes of Self-Monitoring, Analysis and Reporting Technology (SMART). Then, we investigate the predictive capabilities of five machine learning models including naïve Bayes and artificial neural networks (ANN) to define a failure prediction module for the cloud architecture. The experimental results demonstrate that the artificial neural network (ANN) model offers the best prediction accuracy.

关键词： Artificial neural network (ANN) Cloud computing Fault-tolerance Artificial Intelligence Weka SMART attributes

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An Architecture for QoS-Aware Fog Service Provisioning

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Procedia computer science 2020年 170卷 411-418页

作者： Elarbi Badidi Awatif Ragmani Department of Computer Science and Software Engineering College of Information Technology UAE University AL-AIN PO Box. 15551 UAE Faculty of Sciences AinChock University Hassan II of Casablanca 20100 Casablanca Morocco

The proliferation of IoT technologies and the broad deployment of sensors and IoT devices are changing the way and the speed of delivery of many services. IoT data streams are typically transmitted to cloud services for processing. However, time-sensitive IoT applications cannot tolerate high latency they may experience when IoT data streams are sent to the cloud. Fog computing-based solutions for this kind of applications are becoming more and more attractive due to the low latency they can provide and guarantee. Given the growing deployments of fog nodes, we propose in this paper an architecture for quality of service (QoS) aware fog service provisioning, which allows to schedule the execution of IoT applications’ tasks on a cluster of fog nodes. A fog broker component can implement various scheduling policies to help IoT applications fulfill their QoS requirements. The results of the simulations we performed show that using some simple strategies, it is possible to keep low the latency of applications and distribute the load among the fog nodes of the cluster.

关键词： Fog computing cloud computing quality-of-service latency scheduling

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Mining important nodes in complex software network based on ripple effects of probability 19

Mining important nodes in complex software network based on ...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Ren, Jiadong Huang, Guoyan Wang, Qian He, Haitao Liu, Xinqian Zhao, Xiaolin Computer Virtual Technology and System Integration Laboratory of Hebei Province College of Information Science and Engineering Yanshan University Qinhuangdao Hebei China Beijing Key Laboratory of Software Security Engineering Technology School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781450371582

The complexity of software directly leads to an increasing cost in software testing and maintenance. Finding the important nodes with significant vulnerability is helpful for fault discovery and further reduces the damage to the software system. In this paper, a new algorithm named MIN-REP (Mining the Important Nodes based on Ripple Effects of Probability) is proposed to find out the paths with greater possibility for fault propagation, and then the important nodes are mined. To build a model of directed unweighted software network, functions are taken as the nodes and the dependencies between the functions are regarded as the edges. Fault propagation tendency paths are discovered based on the function execution paths and minimum probability threshold. The frequency of each directed edge in the set of fault propagation tendency path is taken as the weight of the corresponding edge. Then some metrics related to ripple effects of probability are calculated. Finally, the nodes with the metric at top-k are taken as the important nodes. The experiment verifies the accuracy and efficiency of the algorithm MIN-REP. © 2019 Association for Computing Machinery.

关键词： Probability

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A novel controllable image segmentation method using the inverse GAMMA distribution function based on histogram specification

A novel controllable image segmentation method using the inv...

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2019 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2019

作者： Saeed, Dhekra Shi, Huibin Ameen, Barakat College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China College of Information Science and Engineering Central South University Changsha China

ISBN: (纸本)9781728161068

Image segmentation(IMSEG) is defined as a top of the basic and most significant process of digital image handling which refers to the techniques that used to partitioning and dividing an image into useful and meaningful parts, called segments. It's so important for many applications which difficult and inefficient to process the entire image, such as object recognition or image compression. So, IMSEG aims to segment the image into districts for further operations and processing. There are several techniques for image segmentation which divide the image into many parts regarding to some specific image properties such as the intensity value of pixels, relation between pixels, color, texture and so on. However, most image segmentation techniques have deficient ability to plainly tune the segment sensitivity of the segmented image. In our manuscript we will propose a new technique for image segmentations using the invers GAMMA distribution function based on histogram matching, the proposed technique provides a method to manage the segment sensitivity of segmented image via setting one parameter. Experimental outcomes pretend a superior execution of this created technique. © 2019 IEEE.

关键词： Image segmentation

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Aeroengine remaining useful life prediction via integrating enhanced inverted transformer and spatiotemporal graph learning

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ISA Transactions 2025年

作者： Sun, Shilong Ding, Hao Zhao, Zida Zhou, Yu Wang, Dong Xu, Wenfu School of Robotics and Advanced Manufacturing College of Artificial Intelligence Harbin Institute of Technology Shenzhen China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai200240 China

Accurate prediction of aeroengine Remaining Useful Life (RUL) is critical for ensuring flight safety, minimizing maintenance costs, and improving operational efficiency. This study proposes a novel model, the Fourier-Enhanced Inverted Transformer with Graph-Augmented Spatiotemporal Modeling (FIT-GSTM), to enhance RUL prediction performance. FIT-GSTM combines an inverted Transformer with a Spatiotemporal Graph Convolutional Network (STGCN) to effectively capture global spatiotemporal dependencies across multi-sensor data. To further enrich feature representation, the model incorporates Fast Fourier Transform (FFT) to extract frequency-domain information and fuses it with time-domain features, enhancing robustness to noise. Additionally, the integration of Memory Tokens and Reversible Instance Normalization (RevIN) strengthens the model's ability to retain long-term dependencies and adapt to heterogeneous data distributions. Experimental evaluations on the C-MAPSS dataset demonstrate that FIT-GSTM achieves superior RUL prediction accuracy and generalization compared to existing methods, highlighting its potential for real-world deployment in aeroengine health management. © 2025 ISA

关键词： Fast Fourier transforms

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Implementing the matrix multiplication with dfc on kunlun small scale computer 20

Implementing the matrix multiplication with dfc on kunlun sm...

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20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019

作者： Du, Zheng Zhang, Jing Sha, Shihao Luo, Qiuming Department of Computer Science and Technology Tsinghua University China NHPCC/Guangdong Key Laboratory of Popular HPC Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781728126166

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512. © 2019 IEEE.

关键词： Matrix algebra

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Correction to: Feature selection via uncorrelated discriminant sparse regression for multimedia analysis

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Multimedia Tools and Applications 2022年第1期82卷 649-649页

作者： Guo, Shuangle Zhang, Jianguang Zhang, Wenting Song, Zhifei Meng, Chunmei The School of Information Engineering Binzhou University Binzhou China The College of Mathematics and Computer Science Hengshui University Hengshui China The School of Computer Science & Software Engineering Shenzhen University Shenzhen China The College Electronics & Information Engineering Hengshui University Hengshui China The Office of Academic Research Hengshui University Hengshui China The College of Continuing Education Hengshui University Hengshui China

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A Comparison of Classification Algorithms Based on The Number of Features

A Comparison of Classification Algorithms Based on The Numbe...

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Chinese Control Conference (CCC)

作者： Peichen Xiong Wei Ping Computer Science and Software Engineering Xi’an Jiaotong-Liverpool University Suzhou P. R. China School of Information Engineering College of Science and Technology Ningbo University Ningbo P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

As the most widely known data mining algorithm, classification algorithms have attracted wide attention. K-Nearest Neighbor (KNN) algorithm and decision tree algorithm are the two widely known algorithms in classification algorithms. Sometimes, people not sure how to choose the suitable to solve the classification problems. In this paper, we establish KNN algorithm model and decision tree ID3 algorithm model to analyze the accuracy of the two algorithms in the same data set with different number of features. Through the learning curve and cross validation, we find ID3 algorithm is better than KNN algorithm, and when the number of feature increased the accuracy of KNN is increasing while ID3 is decreased.

关键词： Decision trees Training Prediction algorithms Predictive models Analytical models Machine learning algorithms Data mining

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The computer Vision-based Tolerancing Callout Detection Model 16

The Computer Vision-based Tolerancing Callout Detection Mode...

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16th CIRP Conference on computer Aided Tolerancing, CIRP CAT 2020

作者： Deng, Xin Li, Tukun Xu, Yuanping Cao, Yanlong Kong, Chao Zhang, Errui School of Software Engineering Chengdu University of Information Technology Chengdu China EPSRC Future Advanced Metrology Hub Centre for Precision Technologies School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom Shandong Industrial Technology Research Institute Zhejiang University Shandong277500 China College of Engineering and Computer Science Australia National University CanberraACT2600 Australia

Tolerancing symbols play an important role in mechanical product drawings, and they directly determine the functions, mating properties, interchangeability and working life of geometrical products. A symbolic tolerancing callout containing a set of symbols represents a set of pre-ordered operations with attributes. It includes an amount of knowledge from the drawing and standard documents, which is often reconstructed manually by engineers. Thus, at the same time, a symbolic tolerancing callout makes it difficult for the end-user to understand and interpret these callouts manually. To this end, this study puts forward a tolerancing callout detection model via the use of off-the-shelf costumer-grade cameras on current mobile devices for extracting and recognizing tolerancing callout blocks and symbols in them intelligently. This model has four core components: image preprocessing, callout location and extraction, symbol and character segmentation, and deep learning-based symbol recognition. The image preprocessing component is developed to remove the interferences on the target technical drawings through the corresponding morphological methods. This study proposes a novel solution on callout block locations and extractions in callout intensive scenarios since the callout locations and extractions can directly affect the accuracy of symbol and character recognitions. Then, Huff Transform and improved projection methods have been devised to symbol and character segmentations. Finally, this study constructed a convolutional neural network (CNN) to train a symbol recognition model. The experimental results show that the proposed model gains applicability on intelligent callout extractions and the corresponding symbol recognitions. © 2020 The Authors. Published by Elsevier B.V.

关键词： Extraction

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