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检索条件"任意字段=17th International Conference on Distributed Computing and Intelligent Technology"

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A Computational Framework for the Analysis of Biological Models

A Computational Framework for the Analysis of Biological Mod...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Stefanos Konstantinos D. Petsios Dimitrios I. Fotiadis Unit of Medical Technology and Intelligent Information Systems Department of Computer Science University of Ioannina (UoI) Ioannina Greece

We present a computational framework for the visualization of large datasets produced from simulated multidimensional biological cell and tissue models. In this work we present a subsystem of a complete biological simulation system called BioPSiS (Biological Process Simulation System). the host system consists of special subsystems which monitor the simulation process and visualize its results. the mathematical computations in BioPSis can be performed either in a simple processing unit or in a distributed unit. the functionality of the subsystem presented here provides an interactive tool. In particular this intuitive visualization subsystem facilitates the analysis of the simulation results as well as the evaluation of the computational efficiency. Case studies and experiments demonstrate the efficacy of the proposed approach in terms of its ease to use, as well as its various features such as the capability to extract complex information from the output data for further study.

关键词： Biology computing Biological system modeling Computational modeling Visualization Biopsy Multidimensional systems Biological cells Biological tissues Biological processes Monitoring

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An analytical approach for determining coverage probabilities in large UMTS networks

An analytical approach for determining coverage probabilitie...

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IEEE conference on Vehicular technology (VTC)

作者： B. Schroder B. Liesenfeld A. Weller K. Leibnitz D. Staehle Phuoc Tran-Gia T-Mobile International Bonn Germany Dept. of Distributed Systems University of Würzburg Würzburg Germany

An analytical model for dimensioning and planning of large UMTS networks with varying service mixtures is presented. Given the distribution of the UMTS traffic and the attenuation between mobiles and base stations using any given path loss prediction, the uplink-coverage probabilities and call blocking rates can be determined for a realistic "many cells-many mobiles" case. Because of effects like "cell breathing" a precise calculation of in-cell and inter-cell interference is done simultaneously. Imperfection of power control is taken into account. this analytical model permits fast calculations in particular for large networks.

关键词： intelligent networks 3G mobile communication Base stations Interference Analytical models Attenuation Telecommunication traffic Mobile computing Distribution functions Traffic control

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An intelligent Game theory Framework for Detecting Advanced Persistent threats

An Intelligent Game Theory Framework for Detecting Advanced ...

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international conference on Parallel and distributed Systems (ICPADS)

作者： Hao Yan Qianzhen Zhang Junjie Xie Ziyue Lu Sheng Chen Deke Guo College of Intelligence and Computing Tianjin University Tianjin China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha Hunan China Institute of Systems Engineering AMS Beijing China

ISBN: (纸本)9781665408790

the advanced persistent threat (APT) is a stealthy cyber attack perpetrated by a group that gains unauthorized access to a computer network and remains undiscovered to steal specific data and resources. Fast detection and defense of APT attacks are critical tasks in cyber security. Previous works use simple feature extraction and classification methods to distinguish APT information flow from the normal one. However, APT attacks are latent, with very little flow and mixed in many normal information flows. Moreover, APT attacks can adjust their behavior according to the environment, making it challenging to be discovered and extract features. Meanwhile, dynamic information flow tracking (DIFT) is a tool for tracking information flow, which can also adjust the marking strategy according to the environment and is often used to track and detect APT information flow. On the other hand, game theory is a mathematical model that expresses the game of two or more parties. therefore, this motivates us to model a game theory to solve the above challenge. In this paper, to solve the above obstacles, we propose an intelligent game theory framework named DPS, which models the strategic interaction between APTs and DIFT and aims to get a high reward for DIFT. Our proposed DPS framework utilizes deep reinforcement learning to find the Nash equilibrium. the game model is a nonzero-sum, average reward stochastic game. Specifically, we design a subgraph pruning strategy and deep Q-network to guide the player in exploring new strategies in the information flow graph. Finally, we implement our framework to compute an optimal defender strategy to defend cyber security. Based on 2 real-world datasets, the experiment results demonstrate that the DPS framework can delay APT intrusions under equilibrium in 3 epochs and get a better reward than the Uniform policy.

关键词： Training Performance evaluation Stochastic processes Games Reinforcement learning Feature extraction Nash equilibrium

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Chronus+: Minimizing Switch Buffer Size during Network Updates in Timed SDNs

Chronus+: Minimizing Switch Buffer Size during Network Updat...

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international conference on distributed computing Systems

作者： Xin He Jiaqi Zheng Haipeng Dai Yuhu Sun Wanchun Dou Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

Although the logically-centralized perspective is offered in Software-Defined Networks (SDNs), the data plane is still distributed in nature. Update commands sent by the centralized controller are executed asynchronously and independently in each switch. the timed SDNs enable synchronous and coordinate update operations as each update command can be triggered by a pre-defined time point. Prior work on timed update mainly focuses on how to produce a congestion-free update sequence, whereas finding a congestion-free timed update sequence may be too long to be applied in practice, even worse, such an update order may not exist. In this paper, we propose Chronus + , a timed update system that utilizes switch buffer to shorten the update time while minimizing the switch buffer size during updates. We formulate the Minimum Switch Buffer Size Problem (MSBSP) as an optimization program and show its hardness. A set of efficient algorithms is proposed to determine a timed update sequence in polynomial time. Extensive evaluations in Mininet and large-scale simulations show that Chronus + can reduce the update time by at least 17% and the switch buffer by at least 27% compared with state-of-the-art approaches.

关键词： conferences Computational modeling distributed databases Switches Control systems Optimization

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Deep Learning Based Domain Adaptation with Data Fusion for Aerial Image Data Analysis 25th

Deep Learning Based Domain Adaptation with Data Fusion for A...

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25th international conference on Pattern Recognition Workshops, ICPR 2020

作者： Lu, Jingyang Yu, Chenggang Blasch, Erik Ilin, Roman Chen, Hua-mei Shen, Dan Sullivan, Nichole Chen, Genshe Kozma, Robert Intelligent Fusion Technology Inc. GermantownMD20876 United States Air Force Research Laboratory DaytonOH45435 United States The University of Memphis MemphisTN38152 United States

ISBN: (纸本)9783030687922

Current Artificial Intelligence (AI) machine learning approaches perform well with similar sensors for data collection, training, and testing. the ability to learn and analyze data from multiple sources would enhance capabilities for Artificial Intelligence (AI) systems. this paper presents a deep learning-based multi-source self-correcting approach to fuse data with different modalities. the data-level fusion approach maximizes the capability to detect unanticipated events/targets augmented with machine learning methods. the proposed Domain Adaptation for Efficient Learning Fusion (DAELF) deep neural network adapts to changes of the input distribution allowing for self-correcting of multiple source classification and fusion. When supported by a distributed computing hierarchy, the proposed DAELF scales up in neural network size and out in geographical span. the design of DAELF includes various types of data fusion, including decision-level and feature-level data fusion. the results of DAELF highlight that feature-level fusion outperforms other approaches in terms of classification accuracy for the digit data and the Aerial Image Data analysis. © 2021, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Edge computing-Enabled Lightweight Deep Neural Network for Real-Time Video Surveillance in Maritime Cyber-Physical Systems

Edge Computing-Enabled Lightweight Deep Neural Network for R...

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6th international conference on intelligent computing and Signal Processing (ICSP)

作者： Caofei Luo Fumin Wu Renhao Xiong Guanhua Xu Wen Liu CETC (Ningbo) Maritime Electronics Research Institute Co. Ltd. Ningbo China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Navigation Wuhan University of Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the rapid developments of low-end IoT devices and artificial intelligence (AI), the heterogeneous and dynamic data has been increasing drastically, especially in the era of AI-enabled maritime cyber-physical systems (CPS). It is intractable to provide the efficient and flexible computer vision applications through traditional cloud-centric data processing strategies, which commonly suffer from the limitations, e.g., high cost, high- latency communication, and high occupied storage, etc. To meet these challenges, edge computing has meaningfully evolved into a flexible and powerful distributed architecture. It contributes to improvements in bandwidth savings and response time in practical applications. To guarantee efficient video surveillance in AI-powered maritime CPS, the edge computing-based lightweight deep network (termed ShipNet-YOLOv3), incorporated with Kalman filtering and Hungarian algorithm, is proposed to implement real-time detection and tracking of moving ships. In particular, ShipNet-YOLOv3 adopts Darknet-53 as the backbone network (i.e., feature extractor) with three prediction boxes. the channel pruning and fine-tuning strategy are further introduced to balance the trade-off between computational cost, network size, and detection (or tracking) accuracy. Experimental results on both synthetic and realistic scenarios have illustrated that the proposed method could provide superior ship detection and tracking results under different adverse imaging conditions. It thus has the capacity of promoting real-time video surveillance in AI -enabled maritime CPS.

关键词： Performance evaluation Filtering Image edge detection Imaging Video surveillance Feature extraction Real-time systems Kalman filters Marine vehicles Artificial intelligence

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A TRANSMISSION ALGORIthM OF ANT COLONY SYSTEM BASED LARGE-SCALE DATA FOR WSN NETWORK

A TRANSMISSION ALGORITHM OF ANT COLONY SYSTEM BASED LARGE-SC...

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2016 4th IEEE international conference on Cloud computing and Intelligence Systems（IEEE CCIS2016）

作者： Liu Liu Meng Luo Ming Yang Yang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509012572

In this paper, a distributed ant colony system(DACS) based on large scale wireless sensor is proposed. the algorithm aims to solve the problem of large scale data transmission in wireless sensor networks from three aspects of the operational efficiency, energy balance and network congestion. the algorithm can optimize the transmission time and provide a more optimal capacity allocation scheme for the sensor at the same time with the same large scale data transmission. the simulation results show that the improved ant colony algorithm can balance the network energy, improve the transmission efficiency, and reduce the network congestion, and then extend the network lifetime.

关键词： Ant colony system DC method Map-Reduce DACS

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An Effective Diagnosis of Alzheimer’s Disease with the Use of Deep Learning based CNN Model

An Effective Diagnosis of Alzheimer’s Disease with the Use ...

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international conference on ent computing and Control Systems (ICICCS)

作者： K C Sharmili G P Suja E. Pandian Md. Abul Ala Walid Sripriya Arunachalam G.Charles Babu Computer Science Stella Maris College 17 Cathedral Road Chennai Computer science Muslim Arts College Thiruvithancode Master of Computer Application Sri Muthukumaran Institute of Technology Mangadu Chennai Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai Department of Computer Science and Engineering GRIET Hyderabad

In recent years, Alzheimer’s disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer’s disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer’s disease. those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. this research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. the proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin.

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Multi-domain Sentiment Classification on Self-constructed Indonesian Dataset 9th

Multi-domain Sentiment Classification on Self-constructed In...

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9th CCF international conference on Natural Language Processing and Chinese computing, NLPCC 2020

作者： Lin, Nankai Chen, Boyu Fu, Sihui Lin, Xiaotian Jiang, Shengyi School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Guangzhou Key Laboratory of Multilingual Intelligent Processing Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9783030604493

Domain-dependence limits the application of a well-trained sentiment classifier based on one domain data in other different domains. To solve this problem, multi-domain sentiment classification has received great attention recently. It aims to construct a domain-specific sentiment classifier at once from datasets of multi-domains. However, research on multi-domain sentiment classification mainly focuses on high-resource languages, and there is no research on Indonesian multi-domain sentiment classification. To fill the gap, we constructed an Indonesian multi-domain dataset, including 489,000 reviews from four domains with three sentiment polarities (positive, neutral, and negative), and proposed an integrated model for Indonesian multi-domain sentiment classification. this model is consisted of lemmatization layer, domain-general module, domain-specific module, and domain classifier module. Based on the Indonesian multi-domain dataset, the model was evaluated and compared with baseline methods commonly used in the sentiment analysis of high-resource languages. the effectiveness of some essential components in the model was also verified. the model achieved an average weighted F1 over four domains with 87.24%, outperforming the baseline methods and demonstrating its effectiveness. © 2020, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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AMD based Service Agent Collaboration and Specification

AMD based Service Agent Collaboration and Specification

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international Workshop on intelligent Communication and Social Networks

作者： Liwei Zheng Computer School Beijing Information Science & Technology University

ISBN: (纸本)9781479909735

With the emergence of Big Data in Internet, composing existing web services for satisfying new requirements, such as data quality enhancing, effective data choosing, knowledge discovering etc, has gained daily expanding attentions and interests. Many efforts have been pursued for supporting the essential activities in service composition. However, the existing techniques only focus on passive services which are waiting there for being discovered and invoked. We argue that it might be more attractive when Web services become active entities (Service Agent) distributed in Internet which can recognize the newly emergent requirements and compete with others for realize (part of) the requirements. Retreating or refinement of Big data will hardly be accomplished by one or two data handling center, Service Agent collaboration would be a competitive method for the big data handling problem. Mostly more than one service agents have to collaborate to satisfy requirements in current internet environment especially with social networks. that could be called as the requirement driven agent collaboration. Research on such collaboration might be useful for the previous problem. We have given a preliminary model for the requirement driven agent collaboration based on a function ontology and the automated mechanism design in the earlier work. this paper extended the Function Ontology, and enhanced the AMD model. that makes the interactions in MAS generated by agent collaboration can be described. A negotiation frame for the evaluation and choice of collaboration solutions is also given in this paper. It helps the requester evaluate the possible MAS systems, and helps the service agents make decisions to choose a good enough solution by negotiation. According to the dependencies provided in Function Ontology, a specification is given to describe the execution process of the chosen MAS. And also a method is given to translate the specification to BPEL which is more standard, acceptabl

关键词： Multi-Agent Collaboration Mechanism design

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