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检索条件"任意字段=International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC"

共 285 条记录，以下是201-210 订阅

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The Key Algorithms of Promoter Data Parallel Processing Based on OpenMP

The Key Algorithms of Promoter Data Parallel Processing Base...

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international conference on cyber-enabled distributed computing and knowledge discovery, cyberc

作者： Yan Shi Jun Lu Xinyi Shi Jinliang Zheng Jun Li College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China University of Chinese Academy of Sciences Heilongjiang University Harbin Heilongjiang CN

The key algorithm of parallel technology based on OpenMP is advanced to dispose the promoter data. The promoter data are intercepted from the upstream area of five plants sequences. The five plants contain Arabidopsis Thaliana, Soybean, Alfalfa, Rice and Beans. The processing includes disturbing the sequences in random, calculating the P value, selecting the significant motifs from the frequent motifs, labeling the sequence number and so on. The serial algorithm and the parallel algorithm are compared in the implementation procedure. The experimental result shows that the OpenMP parallel technology can effectively improve the efficiency of the promoter data analysis and processing. It is a realistic significance to the vast amounts of biological information data processing.

关键词： Instruction sets Parallel algorithms Algorithm design and analysis Gene expression distributed databases Labeling

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An Encrypted Malicious Traffic Detection System Based on Neural Network

An Encrypted Malicious Traffic Detection System Based on Neu...

引用

international conference on cyber-enabled distributed computing and knowledge discovery, cyberc

作者： Tangda Yu FuTai Zou Linsen Li Ping Yi School of Cyber Security Shanghai Jiao Tong University Shanghai P.R.China

In recent years, with the widespread use of encrypted traffic communication technology, network traffic encryption has been gradually becoming a standard of communication. This phenomenon has a great impact on traditional traffic detection methods, especially on anomaly detection methods, which are highly dependent on the type of network protocol types and traffic data. By surveying the existing encrypted traffic classification and analyzing these methods, we found that there are two main methods for detection: payload-based detection and feature-based detection. On this basis, this paper puts forward an Encrypted Malicious Traffic Detection System which is based on multi-AEs(Autoencoder). We use malicious sandbox for traffic data collection, mark malicious flow and normal flow with labels. After that, we use multilayer networks of AEs for feature extraction and training classifier model. Our system analyzes the feature of cryptographic protocol from handshake phase to Authentication phase on the basis of existing research, and we extract the traffic feature for a better classification by further expanding flow feature vector to the higher dimensions. In addition, we compared the performance of our model with the traditional learning model under the same environment. The experimental results showed that our system had higher detection accuracy and lower loss rate. We also studied the influence of dataset imbalance on results in the detection of encrypted traffic by several experiments. According to the experimental evaluation, dataset imbalance will lead to the decrease of positive rate.

关键词： Protocols Feature extraction Encryption Tools Payloads Servers

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An Online Framework for Temporal Social Unrest Event Prediction Using News Stream

An Online Framework for Temporal Social Unrest Event Predict...

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international conference on cyber-enabled distributed computing and knowledge discovery, cyberc

作者： Qiao Fengcai Deng Jinsheng Wei Li College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha China Troop 96766 Xinyang China

ISBN: (数字)9781728184487

ISBN: (纸本)9781728184494

Continuously assessing the risk of upcoming social unrest events and predict the likelihood of these events are of great importance. Thanks to the era of big data, people's understanding, experience, values, and ideology are mirrored in the organization of cyber space. In this paper, we propose an online prediction framework, using frequent subgraph patterns and hidden semi-Markov models (HSMMs). The feature called BoEAG (Bag-of-Event-Association-subGraph) is constructed based on frequents subgraph mining and the bag of word model. The new framework leverages the large scale digital history events captured from GDELT (Global Data on Events, Location, and Tone) to characterize the transitional process of the social unrest events' evolutionary stages, uncovering the underlying event development mechanics and formulates the social unrest event prediction as a sequence classification problem based on Bayes decision. Experimental results with data from Thailand demonstrate the effectiveness of the new framework, which outperforms the traditional HMM by 12.7% and the logistic regression 37.4%.

关键词： Hidden Markov models Organizations Predictive models knowledge discovery History distributed computing Logistics

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Convolutional Recurrent Neural Networks for knowledge Tracing

Convolutional Recurrent Neural Networks for Knowledge Tracin...

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international conference on cyber-enabled distributed computing and knowledge discovery, cyberc

作者： Wei Wang Tieyuan Liu Liang Chang Tianlong Gu Xuemei Zhao School of Computer and Information Security Guilin University of Electronic Technology Guilin China School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728184487

ISBN: (纸本)9781728184494

knowledge Tracing (KT) is a task that aims to assess students' mastery level of knowledge and predict their performance over questions, which has attracted widespread attention over the years. Recently, an increasing number of researches have applied deep learning techniques to knowledge tracing and have made a huge success over traditional Bayesian knowledge Tracing methods. Most existing deep learning-based methods utilized either Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs). However, it is worth noticing that these two sorts of models are complementary in modeling abilities. Thus, in this paper, we propose a novel knowledge tracing model by taking advantage of both two models via combining them into a single integrated model, named Convolutional Recurrent knowledge Tracing (CRKT). Extensive experiments show that our model outperforms the state-of-the-art models in multiple KT datasets.

关键词： Learning systems Deep learning Recurrent neural networks Computational modeling knowledge discovery Task analysis distributed computing

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On the impact of erasure coding parameters to the reliability of distributed brick storage systems

On the impact of erasure coding parameters to the reliabilit...

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international conference on cyber-enabled distributed computing and knowledge discovery, cyberc

作者： Xiangyu Luo Yun Wang Zhuowei Shen School of Computer Science & Engineering South-East University Nanjing China

For a given amount of storage overhead, erasure coding offers a higher degree of survivability than pure replication. Consequently, erasure coding attracts much attention these years in the research area of reliable distributed storage systems. Although numerous erasure codes have been put forward, how to choose erasure coding parameters to maximize system reliability has not yet been sufficiently investigated. Erasure coding parameters greatly affect not only system survivability with multiple concurrent failures, but also data repair speed. We propose a method that can quantitatively evaluate these effects. Based on the method, the issue of determining erasure coding parameters is solved. Besides, relationships among other reliability-affecting factors such as storage overhead, repair bandwidth and single brick's properties are also investigated.

关键词： Redundancy Encoding Bandwidth Computer science Reliability engineering Local area networks Storage area networks Computer crashes Cost function

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Delay-minimized Energy-efficient Data Aggregation in Wireless Sensor Networks

Delay-minimized Energy-efficient Data Aggregation in Wireles...

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international conference on cyber-enabled distributed computing and knowledge discovery, cyberc

作者： Huu Nghia Le Vyacheslav Zalyubovskiy Hyunseung Choo College of Information and Communication Engineering Sungkyunkwan University Suwon South Korea

Data aggregation is a fundamental problem in wireless sensor networks that has attracted great attention in recent years. To design a data aggregation scheme, delay and energy efficiencies are two crucial issues that require much consideration. In this paper, we propose a distributed, energy-efficient algorithm for collecting data from all sensor nodes with minimum latency called Delay-minimized Energy-efficient Data Aggregation algorithm (DEDA). The DEDA algorithm minimizes data aggregation latency by building a delay-efficient network structure. At the same time, it also considers the distances between network nodes for saving sensor transmission power and network energy. Energy consumption is also well-balanced between sensors to achieve an acceptable network lifetime. The simulation results show that the scheme could significantly decrease data aggregation delay and obtain a reasonable network lifetime compared with other approaches.

关键词： Delay Wireless sensor networks Clustering algorithms Energy consumption distributed databases Buildings Protocols

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A Big Data Online Cleaning Algorithm Based on Dynamic Outlier Detection

A Big Data Online Cleaning Algorithm Based on Dynamic Outlie...

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international conference on cyber-enabled distributed computing and knowledge discovery, cyberc

作者： Yinglong Diao Ke-Yan Liu Xiaoli Meng Xueshun Ye Kaiyuan He Power Distribution Department China Electric Power Research Institute CEPRI Beijing China

ISBN: (纸本)9781467392013

To effectively clean the large-scale, mixed and inaccurate monitoring or collective data, reduce the cost of data cache and ensure the consistent deviation detection on timing data of each cycle, a big data online cleaning algorithm based on dynamic outlier detection has been proposed. The data cleaning method is improved by local outliner detection upon density, sampling cluster uniformly dilution Euclidean distance matrix retaining some corrections into next cycle of cleaning, which avoids a sampling causing overall cleaning deviation and reduces amount of calculation within data cleaning stable time, enhancing the speed greatly. Finally, the distributed solutions on online cleaning algorithm based on Hadoop platform.

关键词： Cleaning Heuristic algorithms distributed databases Euclidean distance Data mining Detection algorithms Big data

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Network Initialization for Wireless distributed Beaconing Networks

Network Initialization for Wireless Distributed Beaconing Ne...

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international conference on cyber-enabled distributed computing and knowledge discovery, cyberc

作者： Youngmi Baek Youngil Kim Byunghwa Lee Jilong Lee Jeongbae Yun Qin Shu Seungyong Oh Kijun Han Department of Electrical Engineering and Computer Science Kyungpook National University Daegu South Korea School of Computer Science and Engineering Kyungpook National University Daegu South Korea

The unlicensed incumbents must be offered seamless communications on other unused channels in TV white space. It must provide communication processes among competing users through the proper allocation of available resources. It is important to quickly organize a new network connection or to join to a new network rapidly. In this paper, we studied the limitations of the current distributed beaconing network in initial access time and proposed a modified selection mechanism and a dispersion mechanism of beacon slot for a successful, much faster joining process within the initial access time for enabled unlicensed incumbents. We also described how the proposed mechanism helps minimize initial access time, carry out joining processes within the maximum initial access time, regardless of whether or not there is a beacon collision in an existing beacon period.

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OCDE: Adaptive Differential Evolution Algorithm in distributed Intelligent Systems

OCDE: Adaptive Differential Evolution Algorithm in Distribut...

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international conference on cyber-enabled distributed computing and knowledge discovery, cyberc

作者： Minghao Wang Shaoyi Du Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798331506896

ISBN: (纸本)9798331506902

In autonomous driving, the optimization theory and algorithms for distributed intelligent systems are essential for enhancing vehicle decision-making capabilities, path planning, and environmental perception. Evolutionary algorithms, as a global optimization method inspired by biological evolution, is widely used in these scenarios. Unfortunately, the performance of evolutionary algorithms relies on the properties of the solution space, the optimization strategy, and parameter settings, and their efficiency also requires more rigorous evaluation. This study focuses on differential evolution algorithm, examining the properties of the solution space for optimization problems, the preferences of strategies and parameters in different optimization scenarios, and setting new standards for efficiency evaluation. Based on the experimental conclusions, modifications are made to our algorithm to further verify the correctness of our findings. All experimental results are deduced based on the CEC2017 benchmark problem suite from the IEEE Congress on Evolutionary Computation (CEC).

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MPLB: A Load Balance Scheme of Joint Resource Scheduling in Heterogeneous Wireless Network

MPLB: A Load Balance Scheme of Joint Resource Scheduling in ...

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international conference on cyber-enabled distributed computing and knowledge discovery, cyberc

作者： Xiaoyong Lin Pengwei Yin Yun Xia Yang Yu Tingting Qiu Xiaoming Wang Lixiang Li Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing China College of Communication & Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

A user terminal with multi-mode wireless interface is always located its position in the covered area of heterogeneous wireless network (HWN), there are too many algorithms about vertical handover (VHO) of multiple attributes decision making (MADM) network selections about HWN. According the analysis of current applications of wireless service, regular service has been classified into four kinds, including voice, video, small data and big data, and the proportions of wireless video and big data service may acquire an unavailable bandwidth for each individual wireless station, which will lead to a heavy load and a lower satisfaction rate. A multiple properties load balance (MPLB) scheme is proposed to solve this joint resource scheduling(JRS) problem properly, MPLB scheme first gave an enhanced architecture including the JSR sub-layer, then model of MPLB is established from the candidate wireless network, five aggregated network performance properties have been defined mathematically, the candidate of best grey relational coefficients (GRC) of grey relational analysis(GRA) algorithm is optimally selected as the final executable scheme, after the description of a general flowchart of MPLB scheme. The simulation results proved the MPLB scheme has a more stable load balance effects than those of VHO, so did most users of MPLB gain higher satisfaction rate, slightly different with the four kinds of service. It can be concluded theoretically that the MPLB scheduling scheme could achieve both the load balance on the service providers side and the increased quality of experience of satisfaction rate on the wireless user side, which would bring out some new thoughts in the future user centric network.

关键词： distributed computing knowledge discovery

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